Method and system for detecting a failure or performance degradation in a dynamic system such as a flight vehicle

NASA Technical Reports Server (NTRS)

Miller, Robert H. (Inventor); Ribbens, William B. (Inventor)

2003-01-01

A method and system for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables and providing corresponding output signals in response to one or more system input signals are provided. The method includes calculating estimated gains of a filter and selecting an appropriate linear model for processing the output signals based on the input signals. The step of calculating utilizes one or more models of the dynamic system to obtain estimated signals. The method further includes calculating output error residuals based on the output signals and the estimated signals. The method also includes detecting one or more hypothesized failures or performance degradations of a component or subsystem of the dynamic system based on the error residuals. The step of calculating the estimated values is performed optimally with respect to one or more of: noise, uncertainty of parameters of the models and un-modeled dynamics of the dynamic system which may be a flight vehicle or financial market or modeled financial system.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

A source number estimation method for single optical fiber sensor

NASA Astrophysics Data System (ADS)

Hu, Junpeng; Huang, Zhiping; Su, Shaojing; Zhang, Yimeng; Liu, Chunwu

2015-10-01

The single-channel blind source separation (SCBSS) technique makes great significance in many fields, such as optical fiber communication, sensor detection, image processing and so on. It is a wide range application to realize blind source separation (BSS) from a single optical fiber sensor received data. The performance of many BSS algorithms and signal process methods will be worsened with inaccurate source number estimation. Many excellent algorithms have been proposed to deal with the source number estimation in array signal process which consists of multiple sensors, but they can not be applied directly to the single sensor condition. This paper presents a source number estimation method dealing with the single optical fiber sensor received data. By delay process, this paper converts the single sensor received data to multi-dimension form. And the data covariance matrix is constructed. Then the estimation algorithms used in array signal processing can be utilized. The information theoretic criteria (ITC) based methods, presented by AIC and MDL, Gerschgorin's disk estimation (GDE) are introduced to estimate the source number of the single optical fiber sensor's received signal. To improve the performance of these estimation methods at low signal noise ratio (SNR), this paper make a smooth process to the data covariance matrix. By the smooth process, the fluctuation and uncertainty of the eigenvalues of the covariance matrix are reduced. Simulation results prove that ITC base methods can not estimate the source number effectively under colored noise. The GDE method, although gets a poor performance at low SNR, but it is able to accurately estimate the number of sources with colored noise. The experiments also show that the proposed method can be applied to estimate the source number of single sensor received data.

Information's role in the estimation of chaotic signals

NASA Astrophysics Data System (ADS)

Drake, Daniel Fred

1998-11-01

Researchers have proposed several methods designed to recover chaotic signals from noise-corrupted observations. While the methods vary, their qualitative performance does not: in low levels of noise all methods effectively recover the underlying signal; in high levels of noise no method can recover the underlying signal to any meaningful degree of accuracy. Of the methods proposed to date, all represent sub-optimal estimators. So: Is the inability to recover the signal in high noise levels simply a consequence of estimator sub-optimality? Or is estimator failure actually a manifestation of some intrinsic property of chaos itself? These questions are answered by deriving an optimal estimator for a class of chaotic systems and noting that it, too, fails in high levels of noise. An exact, closed- form expression for the estimator is obtained for a class of chaotic systems whose signals are solutions to a set of linear (but noncausal) difference equations. The existence of this linear description circumvents the difficulties normally encountered when manipulating the nonlinear (but causal) expressions that govern. chaotic behavior. The reason why even the optimal estimator fails to recover underlying chaotic signals in high levels of noise has its roots in information theory. At such noise levels, the mutual information linking the corrupted observations to the underlying signal is essentially nil, reducing the estimator to a simple guessing strategy based solely on a priori statistics. Entropy, long the common bond between information theory and dynamical systems, is actually one aspect of a far more complete characterization of information sources: the rate distortion function. Determining the rate distortion function associated with the class of chaotic systems considered in this work provides bounds on estimator performance in high levels of noise. Finally, a slight modification of the linear description leads to a method of synthesizing on limited precision platforms ``pseudo-chaotic'' sequences that mimic true chaotic behavior to any finite degree of precision and duration. The use of such a technique in spread-spectrum communications is considered.

A de-noising method using the improved wavelet threshold function based on noise variance estimation

NASA Astrophysics Data System (ADS)

Liu, Hui; Wang, Weida; Xiang, Changle; Han, Lijin; Nie, Haizhao

2018-01-01

The precise and efficient noise variance estimation is very important for the processing of all kinds of signals while using the wavelet transform to analyze signals and extract signal features. In view of the problem that the accuracy of traditional noise variance estimation is greatly affected by the fluctuation of noise values, this study puts forward the strategy of using the two-state Gaussian mixture model to classify the high-frequency wavelet coefficients in the minimum scale, which takes both the efficiency and accuracy into account. According to the noise variance estimation, a novel improved wavelet threshold function is proposed by combining the advantages of hard and soft threshold functions, and on the basis of the noise variance estimation algorithm and the improved wavelet threshold function, the research puts forth a novel wavelet threshold de-noising method. The method is tested and validated using random signals and bench test data of an electro-mechanical transmission system. The test results indicate that the wavelet threshold de-noising method based on the noise variance estimation shows preferable performance in processing the testing signals of the electro-mechanical transmission system: it can effectively eliminate the interference of transient signals including voltage, current, and oil pressure and maintain the dynamic characteristics of the signals favorably.

Reconstructing for joint angles on the shoulder and elbow from non-invasive electroencephalographic signals through electromyography

PubMed Central

Choi, Kyuwan

2013-01-01

In this study, first the cortical activities over 2240 vertexes on the brain were estimated from 64 channels electroencephalography (EEG) signals using the Hierarchical Bayesian estimation while 5 subjects did continuous arm reaching movements. From the estimated cortical activities, a sparse linear regression method selected only useful features in reconstructing the electromyography (EMG) signals and estimated the EMG signals of 9 arm muscles. Then, a modular artificial neural network was used to estimate four joint angles from the estimated EMG signals of 9 muscles: one for movement control and the other for posture control. The estimated joint angles using this method have the correlation coefficient (CC) of 0.807 (±0.10) and the normalized root-mean-square error (nRMSE) of 0.176 (±0.29) with the actual joint angles. PMID:24167469

Velocity interferometer signal de-noising using modified Wiener filter

NASA Astrophysics Data System (ADS)

Rav, Amit; Joshi, K. D.; Roy, Kallol; Kaushik, T. C.

2017-05-01

The accuracy and precision of the non-contact velocity interferometer system for any reflector (VISAR) depends not only on the good optical design and linear optical-to- electrical conversion system, but also on accurate and robust post-processing techniques. The performance of these techniques, such as the phase unwrapping algorithm, depends on the signal-to-noise ratio (SNR) of the recorded signal. In the present work, a novel method of improving the SNR of the recorded VISAR signal, based on the knowledge of the noise characteristic of the signal conversion and recording system, is presented. The proposed method uses a modified Wiener filter, for which the signal power spectrum estimation is obtained using a spectral subtraction method (SSM), and the noise power spectrum estimation is obtained by taking the average of the recorded signal during the period when no target movement is expected. Since the noise power spectrum estimate is dynamic in nature, and obtained for each experimental record individually, the improved signal quality is high. The proposed method is applied to the simulated standard signals, and is not only found to be better than the SSM, but is also less sensitive to the selection of the noise floor during signal power spectrum estimation. Finally, the proposed method is applied to the recorded experimental signal and an improvement in the SNR is reported.

Estimation of source location and ground impedance using a hybrid multiple signal classification and Levenberg-Marquardt approach

NASA Astrophysics Data System (ADS)

Tam, Kai-Chung; Lau, Siu-Kit; Tang, Shiu-Keung

2016-07-01

A microphone array signal processing method for locating a stationary point source over a locally reactive ground and for estimating ground impedance is examined in detail in the present study. A non-linear least square approach using the Levenberg-Marquardt method is proposed to overcome the problem of unknown ground impedance. The multiple signal classification method (MUSIC) is used to give the initial estimation of the source location, while the technique of forward backward spatial smoothing is adopted as a pre-processer of the source localization to minimize the effects of source coherence. The accuracy and robustness of the proposed signal processing method are examined. Results show that source localization in the horizontal direction by MUSIC is satisfactory. However, source coherence reduces drastically the accuracy in estimating the source height. The further application of Levenberg-Marquardt method with the results from MUSIC as the initial inputs improves significantly the accuracy of source height estimation. The present proposed method provides effective and robust estimation of the ground surface impedance.

Towards estimation of respiratory muscle effort with respiratory inductance plethysmography signals and complementary ensemble empirical mode decomposition.

PubMed

Chen, Ya-Chen; Hsiao, Tzu-Chien

2018-07-01

Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal's amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects' RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm. Graphical abstract ᅟ.

Comprehensive tire-road friction coefficient estimation based on signal fusion method under complex maneuvering operations

NASA Astrophysics Data System (ADS)

Li, L.; Yang, K.; Jia, G.; Ran, X.; Song, J.; Han, Z.-Q.

2015-05-01

The accurate estimation of the tire-road friction coefficient plays a significant role in the vehicle dynamics control. The estimation method should be timely and reliable for the controlling requirements, which means the contact friction characteristics between the tire and the road should be recognized before the interference to ensure the safety of the driver and passengers from drifting and losing control. In addition, the estimation method should be stable and feasible for complex maneuvering operations to guarantee the control performance as well. A signal fusion method combining the available signals to estimate the road friction is suggested in this paper on the basis of the estimated ones of braking, driving and steering conditions individually. Through the input characteristics and the states of the vehicle and tires from sensors the maneuvering condition may be recognized, by which the certainty factors of the friction of the three conditions mentioned above may be obtained correspondingly, and then the comprehensive road friction may be calculated. Experimental vehicle tests validate the effectiveness of the proposed method through complex maneuvering operations; the estimated road friction coefficient based on the signal fusion method is relatively timely and accurate to satisfy the control demands.

A novel power spectrum calculation method using phase-compensation and weighted averaging for the estimation of ultrasound attenuation.

PubMed

Heo, Seo Weon; Kim, Hyungsuk

2010-05-01

An estimation of ultrasound attenuation in soft tissues is critical in the quantitative ultrasound analysis since it is not only related to the estimations of other ultrasound parameters, such as speed of sound, integrated scatterers, or scatterer size, but also provides pathological information of the scanned tissue. However, estimation performances of ultrasound attenuation are intimately tied to the accurate extraction of spectral information from the backscattered radiofrequency (RF) signals. In this paper, we propose two novel techniques for calculating a block power spectrum from the backscattered ultrasound signals. These are based on the phase-compensation of each RF segment using the normalized cross-correlation to minimize estimation errors due to phase variations, and the weighted averaging technique to maximize the signal-to-noise ratio (SNR). The simulation results with uniform numerical phantoms demonstrate that the proposed method estimates local attenuation coefficients within 1.57% of the actual values while the conventional methods estimate those within 2.96%. The proposed method is especially effective when we deal with the signal reflected from the deeper depth where the SNR level is lower or when the gated window contains a small number of signal samples. Experimental results, performed at 5MHz, were obtained with a one-dimensional 128 elements array, using the tissue-mimicking phantoms also show that the proposed method provides better estimation results (within 3.04% of the actual value) with smaller estimation variances compared to the conventional methods (within 5.93%) for all cases considered. Copyright 2009 Elsevier B.V. All rights reserved.

Self-configurable radio receiver system and method for use with signals without prior knowledge of signal defining characteristics

NASA Technical Reports Server (NTRS)

Hamkins, Jon (Inventor); Simon, Marvin K. (Inventor); Divsalar, Dariush (Inventor); Dolinar, Samuel J. (Inventor); Tkacenko, Andre (Inventor)

2013-01-01

A method, radio receiver, and system to autonomously receive and decode a plurality of signals having a variety of signal types without a priori knowledge of the defining characteristics of the signals is disclosed. The radio receiver is capable of receiving a signal of an unknown signal type and, by estimating one or more defining characteristics of the signal, determine the type of signal. The estimated defining characteristic(s) is/are utilized to enable the receiver to determine other defining characteristics. This in turn, enables the receiver, through multiple iterations, to make a maximum-likelihood (ML) estimate for each of the defining characteristics. After the type of signal is determined by its defining characteristics, the receiver selects an appropriate decoder from a plurality of decoders to decode the signal.

High-resolution correlation

NASA Astrophysics Data System (ADS)

Nelson, D. J.

2007-09-01

In the basic correlation process a sequence of time-lag-indexed correlation coefficients are computed as the inner or dot product of segments of two signals. The time-lag(s) for which the magnitude of the correlation coefficient sequence is maximized is the estimated relative time delay of the two signals. For discrete sampled signals, the delay estimated in this manner is quantized with the same relative accuracy as the clock used in sampling the signals. In addition, the correlation coefficients are real if the input signals are real. There have been many methods proposed to estimate signal delay to more accuracy than the sample interval of the digitizer clock, with some success. These methods include interpolation of the correlation coefficients, estimation of the signal delay from the group delay function, and beam forming techniques, such as the MUSIC algorithm. For spectral estimation, techniques based on phase differentiation have been popular, but these techniques have apparently not been applied to the correlation problem . We propose a phase based delay estimation method (PBDEM) based on the phase of the correlation function that provides a significant improvement of the accuracy of time delay estimation. In the process, the standard correlation function is first calculated. A time lag error function is then calculated from the correlation phase and is used to interpolate the correlation function. The signal delay is shown to be accurately estimated as the zero crossing of the correlation phase near the index of the peak correlation magnitude. This process is nearly as fast as the conventional correlation function on which it is based. For real valued signals, a simple modification is provided, which results in the same correlation accuracy as is obtained for complex valued signals.

Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation.

PubMed

Andreotti, Fernando; Graser, Felix; Malberg, Hagen; Zaunseder, Sebastian

2017-12-01

The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation. Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used. The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving accuracy. The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation. NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox.

Self-adaptive signals separation for non-contact heart rate estimation from facial video in realistic environments.

PubMed

Liu, Xuenan; Yang, Xuezhi; Jin, Jing; Li, Jiangshan

2018-06-05

Recent researches indicate that facial epidermis color varies with the rhythm of heat beats. It can be captured by consumer-level cameras and, astonishingly, be adopted to estimate heart rate (HR). The HR estimated remains not as precise as required in practical environment where illumination interference, facial expressions, or motion artifacts are involved, though numerous methods have been proposed in the last few years. A novel algorithm is proposed to make non-contact HR estimation technique more robust. First, the face of subject is detected and tracked to follow the head movement. The facial region then falls into several blocks, and the chrominance feature of each block is extracted to establish raw HR sub-signal. Self-adaptive signals separation (SASS) is performed to separate the noiseless HR sub-signals from raw sub-signals. On that basis, the noiseless sub-signals full of HR information are selected using weight-based scheme to establish the holistic HR signal, from which average HR is computed adopting wavelet transform and data filter. Forty subjects take part in our experiments, whose facial videos are recorded by a normal webcam with the frame rate of 30 fps under ambient lighting conditions. The average HR estimated by our method correlates strongly with ground truth measurements, as indicated in experimental results measured in static scenario with the Pearson's correlation r=0.980 and dynamic scenario with the Pearson's correlation r=0.897. Our method, compared to the newest method, decreases the error rate by 38.63% and increases the Pearson's correlation by 15.59%, indicating that our method evidently outperforms state-of-the-art non-contact HR estimation methods in realistic environments. © 2018 Institute of Physics and Engineering in Medicine.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

PubMed

Liu, Huawei; Li, Baoqing; Yuan, Xiaobing; Zhou, Qianwei; Huang, Jingchang

2018-03-27

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Measuring the scale dependence of intrinsic alignments using multiple shear estimates

NASA Astrophysics Data System (ADS)

Leonard, C. Danielle; Mandelbaum, Rachel

2018-06-01

We present a new method for measuring the scale dependence of the intrinsic alignment (IA) contamination to the galaxy-galaxy lensing signal, which takes advantage of multiple shear estimation methods applied to the same source galaxy sample. By exploiting the resulting correlation of both shape noise and cosmic variance, our method can provide an increase in the signal-to-noise of the measured IA signal as compared to methods which rely on the difference of the lensing signal from multiple photometric redshift bins. For a galaxy-galaxy lensing measurement which uses LSST sources and DESI lenses, the signal-to-noise on the IA signal from our method is predicted to improve by a factor of ˜2 relative to the method of Blazek et al. (2012), for pairs of shear estimates which yield substantially different measured IA amplitudes and highly correlated shape noise terms. We show that statistical error necessarily dominates the measurement of intrinsic alignments using our method. We also consider a physically motivated extension of the Blazek et al. (2012) method which assumes that all nearby galaxy pairs, rather than only excess pairs, are subject to IA. In this case, the signal-to-noise of the method of Blazek et al. (2012) is improved.

A Matrix Pencil Algorithm Based Multiband Iterative Fusion Imaging Method

NASA Astrophysics Data System (ADS)

Zou, Yong Qiang; Gao, Xun Zhang; Li, Xiang; Liu, Yong Xiang

2016-01-01

Multiband signal fusion technique is a practicable and efficient way to improve the range resolution of ISAR image. The classical fusion method estimates the poles of each subband signal by the root-MUSIC method, and some good results were get in several experiments. However, this method is fragile in noise for the proper poles could not easy to get in low signal to noise ratio (SNR). In order to eliminate the influence of noise, this paper propose a matrix pencil algorithm based method to estimate the multiband signal poles. And to deal with mutual incoherent between subband signals, the incoherent parameters (ICP) are predicted through the relation of corresponding poles of each subband. Then, an iterative algorithm which aimed to minimize the 2-norm of signal difference is introduced to reduce signal fusion error. Applications to simulate dada verify that the proposed method get better fusion results at low SNR.

A Matrix Pencil Algorithm Based Multiband Iterative Fusion Imaging Method

PubMed Central

Zou, Yong Qiang; Gao, Xun Zhang; Li, Xiang; Liu, Yong Xiang

2016-01-01

Multiband signal fusion technique is a practicable and efficient way to improve the range resolution of ISAR image. The classical fusion method estimates the poles of each subband signal by the root-MUSIC method, and some good results were get in several experiments. However, this method is fragile in noise for the proper poles could not easy to get in low signal to noise ratio (SNR). In order to eliminate the influence of noise, this paper propose a matrix pencil algorithm based method to estimate the multiband signal poles. And to deal with mutual incoherent between subband signals, the incoherent parameters (ICP) are predicted through the relation of corresponding poles of each subband. Then, an iterative algorithm which aimed to minimize the 2-norm of signal difference is introduced to reduce signal fusion error. Applications to simulate dada verify that the proposed method get better fusion results at low SNR. PMID:26781194

Chaotic Signal Denoising Based on Hierarchical Threshold Synchrosqueezed Wavelet Transform

NASA Astrophysics Data System (ADS)

Wang, Wen-Bo; Jing, Yun-yu; Zhao, Yan-chao; Zhang, Lian-Hua; Wang, Xiang-Li

2017-12-01

In order to overcoming the shortcoming of single threshold synchrosqueezed wavelet transform(SWT) denoising method, an adaptive hierarchical threshold SWT chaotic signal denoising method is proposed. Firstly, a new SWT threshold function is constructed based on Stein unbiased risk estimation, which is two order continuous derivable. Then, by using of the new threshold function, a threshold process based on the minimum mean square error was implemented, and the optimal estimation value of each layer threshold in SWT chaotic denoising is obtained. The experimental results of the simulating chaotic signal and measured sunspot signals show that, the proposed method can filter the noise of chaotic signal well, and the intrinsic chaotic characteristic of the original signal can be recovered very well. Compared with the EEMD denoising method and the single threshold SWT denoising method, the proposed method can obtain better denoising result for the chaotic signal.

A fast estimation of shock wave pressure based on trend identification

NASA Astrophysics Data System (ADS)

Yao, Zhenjian; Wang, Zhongyu; Wang, Chenchen; Lv, Jing

2018-04-01

In this paper, a fast method based on trend identification is proposed to accurately estimate the shock wave pressure in a dynamic measurement. Firstly, the collected output signal of the pressure sensor is reconstructed by discrete cosine transform (DCT) to reduce the computational complexity for the subsequent steps. Secondly, the empirical mode decomposition (EMD) is applied to decompose the reconstructed signal into several components with different frequency-bands, and the last few low-frequency components are chosen to recover the trend of the reconstructed signal. In the meantime, the optimal component number is determined based on the correlation coefficient and the normalized Euclidean distance between the trend and the reconstructed signal. Thirdly, with the areas under the gradient curve of the trend signal, the stable interval that produces the minimum can be easily identified. As a result, the stable value of the output signal is achieved in this interval. Finally, the shock wave pressure can be estimated according to the stable value of the output signal and the sensitivity of the sensor in the dynamic measurement. A series of shock wave pressure measurements are carried out with a shock tube system to validate the performance of this method. The experimental results show that the proposed method works well in shock wave pressure estimation. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing approaches in both estimation accuracy and computational efficiency.

A New Low Complexity Angle of Arrival Algorithm for 1D and 2D Direction Estimation in MIMO Smart Antenna Systems

PubMed Central

Al-Sadoon, Mohammed A. G.; Zuid, Abdulkareim; Jones, Stephen M. R.; Noras, James M.

2017-01-01

This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots. PMID:29140313

A New Low Complexity Angle of Arrival Algorithm for 1D and 2D Direction Estimation in MIMO Smart Antenna Systems.

PubMed

Al-Sadoon, Mohammed A G; Ali, Nazar T; Dama, Yousf; Zuid, Abdulkareim; Jones, Stephen M R; Abd-Alhameed, Raed A; Noras, James M

2017-11-15

This paper proposes a new low complexity angle of arrival (AOA) method for signal direction estimation in multi-element smart wireless communication systems. The new method estimates the AOAs of the received signals directly from the received signals with significantly reduced complexity since it does not need to construct the correlation matrix, invert the matrix or apply eigen-decomposition, which are computationally expensive. A mathematical model of the proposed method is illustrated and then verified using extensive computer simulations. Both linear and circular sensors arrays are studied using various numerical examples. The method is systematically compared with other common and recently introduced AOA methods over a wide range of scenarios. The simulated results show that the new method has several advantages in terms of reduced complexity and improved accuracy under the assumptions of correlated signals and limited numbers of snapshots.

[Analysis of scatterer microstructure feature based on Chirp-Z transform cepstrum].

PubMed

Guo, Jianzhong; Lin, Shuyu

2007-12-01

The fundamental research field of medical ultrasound has been the characterization of tissue scatterers. The signal processing method is widely used in this research field. A new method of Chirp-Z Transform Cepstrum for mean spacing estimation of tissue scatterers using ultrasonic scattered signals has been developed. By using this method together with conventional AR cepstrum method, we processed the backscattered signals of mimic tissue and pig liver in vitro. The results illustrated that the Chirp-Z Transform Cepstrum method is effective for signal analysis of ultrasonic scattering and characterization of tissue scatterers, and it can improve the resolution for mean spacing estimation of tissue scatterers.

Iteration of ultrasound aberration correction methods

NASA Astrophysics Data System (ADS)

Maasoey, Svein-Erik; Angelsen, Bjoern; Varslot, Trond

2004-05-01

Aberration in ultrasound medical imaging is usually modeled by time-delay and amplitude variations concentrated on the transmitting/receiving array. This filter process is here denoted a TDA filter. The TDA filter is an approximation to the physical aberration process, which occurs over an extended part of the human body wall. Estimation of the TDA filter, and performing correction on transmit and receive, has proven difficult. It has yet to be shown that this method works adequately for severe aberration. Estimation of the TDA filter can be iterated by retransmitting a corrected signal and re-estimate until a convergence criterion is fulfilled (adaptive imaging). Two methods for estimating time-delay and amplitude variations in receive signals from random scatterers have been developed. One method correlates each element signal with a reference signal. The other method use eigenvalue decomposition of the receive cross-spectrum matrix, based upon a receive energy-maximizing criterion. Simulations of iterating aberration correction with a TDA filter have been investigated to study its convergence properties. A weak and strong human-body wall model generated aberration. Both emulated the human abdominal wall. Results after iteration improve aberration correction substantially, and both estimation methods converge, even for the case of strong aberration.

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

Time-Frequency Distribution of Seismocardiographic Signals: A Comparative Study

PubMed Central

Taebi, Amirtaha; Mansy, Hansen A.

2017-01-01

Accurate estimation of seismocardiographic (SCG) signal features can help successful signal characterization and classification in health and disease. This may lead to new methods for diagnosing and monitoring heart function. Time-frequency distributions (TFD) were often used to estimate the spectrotemporal signal features. In this study, the performance of different TFDs (e.g., short-time Fourier transform (STFT), polynomial chirplet transform (PCT), and continuous wavelet transform (CWT) with different mother functions) was assessed using simulated signals, and then utilized to analyze actual SCGs. The instantaneous frequency (IF) was determined from TFD and the error in estimating IF was calculated for simulated signals. Results suggested that the lowest IF error depended on the TFD and the test signal. STFT had lower error than CWT methods for most test signals. For a simulated SCG, Morlet CWT more accurately estimated IF than other CWTs, but Morlet did not provide noticeable advantages over STFT or PCT. PCT had the most consistently accurate IF estimations and appeared more suited for estimating IF of actual SCG signals. PCT analysis showed that actual SCGs from eight healthy subjects had multiple spectral peaks at 9.20 ± 0.48, 25.84 ± 0.77, 50.71 ± 1.83 Hz (mean ± SEM). These may prove useful features for SCG characterization and classification. PMID:28952511

Walsh transforms and signal detection

NASA Technical Reports Server (NTRS)

Welch, L. R.

1977-01-01

The detection of signals using Walsh power spectral estimates is analyzed. In addition, a generalization of this method of estimation is evaluated. The conclusion is that Walsh transforms are not suitable tools for the detection of weak signals in noise.

Novel health monitoring method using an RGB camera.

PubMed

Hassan, M A; Malik, A S; Fofi, D; Saad, N; Meriaudeau, F

2017-11-01

In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.

Simultaneous estimation of multiple phases in digital holographic interferometry using state space analysis

NASA Astrophysics Data System (ADS)

Kulkarni, Rishikesh; Rastogi, Pramod

2018-05-01

A new approach is proposed for the multiple phase estimation from a multicomponent exponential phase signal recorded in multi-beam digital holographic interferometry. It is capable of providing multidimensional measurements in a simultaneous manner from a single recording of the exponential phase signal encoding multiple phases. Each phase within a small window around each pixel is appproximated with a first order polynomial function of spatial coordinates. The problem of accurate estimation of polynomial coefficients, and in turn the unwrapped phases, is formulated as a state space analysis wherein the coefficients and signal amplitudes are set as the elements of a state vector. The state estimation is performed using the extended Kalman filter. An amplitude discrimination criterion is utilized in order to unambiguously estimate the coefficients associated with the individual signal components. The performance of proposed method is stable over a wide range of the ratio of signal amplitudes. The pixelwise phase estimation approach of the proposed method allows it to handle the fringe patterns that may contain invalid regions.

A parameter estimation algorithm for LFM/BPSK hybrid modulated signal intercepted by Nyquist folding receiver

NASA Astrophysics Data System (ADS)

Qiu, Zhaoyang; Wang, Pei; Zhu, Jun; Tang, Bin

2016-12-01

Nyquist folding receiver (NYFR) is a novel ultra-wideband receiver architecture which can realize wideband receiving with a small amount of equipment. Linear frequency modulated/binary phase shift keying (LFM/BPSK) hybrid modulated signal is a novel kind of low probability interception signal with wide bandwidth. The NYFR is an effective architecture to intercept the LFM/BPSK signal and the LFM/BPSK signal intercepted by the NYFR will add the local oscillator modulation. A parameter estimation algorithm for the NYFR output signal is proposed. According to the NYFR prior information, the chirp singular value ratio spectrum is proposed to estimate the chirp rate. Then, based on the output self-characteristic, matching component function is designed to estimate Nyquist zone (NZ) index. Finally, matching code and subspace method are employed to estimate the phase change points and code length. Compared with the existing methods, the proposed algorithm has a better performance. It also has no need to construct a multi-channel structure, which means the computational complexity for the NZ index estimation is small. The simulation results demonstrate the efficacy of the proposed algorithm.

A Comparative Study of Co-Channel Interference Suppression Techniques

NASA Technical Reports Server (NTRS)

Hamkins, Jon; Satorius, Ed; Paparisto, Gent; Polydoros, Andreas

1997-01-01

We describe three methods of combatting co-channel interference (CCI): a cross-coupled phase-locked loop (CCPLL); a phase-tracking circuit (PTC), and joint Viterbi estimation based on the maximum likelihood principle. In the case of co-channel FM-modulated voice signals, the CCPLL and PTC methods typically outperform the maximum likelihood estimators when the modulation parameters are dissimilar. However, as the modulation parameters become identical, joint Viterbi estimation provides for a more robust estimate of the co-channel signals and does not suffer as much from "signal switching" which especially plagues the CCPLL approach. Good performance for the PTC requires both dissimilar modulation parameters and a priori knowledge of the co-channel signal amplitudes. The CCPLL and joint Viterbi estimators, on the other hand, incorporate accurate amplitude estimates. In addition, application of the joint Viterbi algorithm to demodulating co-channel digital (BPSK) signals in a multipath environment is also discussed. It is shown in this case that if the interference is sufficiently small, a single trellis model is most effective in demodulating the co-channel signals.

Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods.

PubMed

Ferguson, B G; Lo, K W

2000-10-01

Flight parameter estimation methods for an airborne acoustic source can be divided into two categories, depending on whether the narrow-band lines or the broadband component of the received signal spectrum is processed to estimate the flight parameters. This paper provides a common framework for the formulation and test of two flight parameter estimation methods: one narrow band, the other broadband. The performances of the two methods are evaluated by applying them to the same acoustic data set, which is recorded by a planar array of passive acoustic sensors during multiple transits of a turboprop fixed-wing aircraft and two types of rotary-wing aircraft. The narrow-band method, which is based on a kinematic model that assumes the source travels in a straight line at constant speed and altitude, requires time-frequency analysis of the acoustic signal received by a single sensor during each aircraft transit. The broadband method is based on the same kinematic model, but requires observing the temporal variation of the differential time of arrival of the acoustic signal at each pair of sensors that comprises the planar array. Generalized cross correlation of each pair of sensor outputs using a cross-spectral phase transform prefilter provides instantaneous estimates of the differential times of arrival of the signal as the acoustic wavefront traverses the array.

Investigation to realize a computationally efficient implementation of the high-order instantaneous-moments-based fringe analysis method

NASA Astrophysics Data System (ADS)

Gorthi, Sai Siva; Rajshekhar, Gannavarpu; Rastogi, Pramod

2010-06-01

Recently, a high-order instantaneous moments (HIM)-operator-based method was proposed for accurate phase estimation in digital holographic interferometry. The method relies on piece-wise polynomial approximation of phase and subsequent evaluation of the polynomial coefficients from the HIM operator using single-tone frequency estimation. The work presents a comparative analysis of the performance of different single-tone frequency estimation techniques, like Fourier transform followed by optimization, estimation of signal parameters by rotational invariance technique (ESPRIT), multiple signal classification (MUSIC), and iterative frequency estimation by interpolation on Fourier coefficients (IFEIF) in HIM-operator-based methods for phase estimation. Simulation and experimental results demonstrate the potential of the IFEIF technique with respect to computational efficiency and estimation accuracy.

New instantaneous frequency estimation method based on the use of image processing techniques

NASA Astrophysics Data System (ADS)

Borda, Monica; Nafornita, Ioan; Isar, Alexandru

2003-05-01

The aim of this paper is to present a new method for the estimation of the instantaneous frequency of a frequency modulated signal, corrupted by additive noise. This method represents an example of fusion of two theories: the time-frequency representations and the mathematical morphology. Any time-frequency representation of a useful signal is concentrated around its instantaneous frequency law and realizes the diffusion of the noise that perturbs the useful signal in the time - frequency plane. In this paper a new time-frequency representation, useful for the estimation of the instantaneous frequency, is proposed. This time-frequency representation is the product of two others time-frequency representations: the Wigner - Ville time-frequency representation and a new one obtained by filtering with a hard thresholding filter the Gabor representation of the signal to be processed. Using the image of this new time-frequency representation the instantaneous frequency of the useful signal can be extracted with the aid of some mathematical morphology operators: the conversion in binary form, the dilation and the skeleton. The simulations of the proposed method have proved its qualities. It is better than other estimation methods, like those based on the use of adaptive notch filters.

Analysis of Coherent Phonon Signals by Sparsity-promoting Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Murata, Shin; Aihara, Shingo; Tokuda, Satoru; Iwamitsu, Kazunori; Mizoguchi, Kohji; Akai, Ichiro; Okada, Masato

2018-05-01

We propose a method to decompose normal modes in a coherent phonon (CP) signal by sparsity-promoting dynamic mode decomposition. While the CP signals can be modeled as the sum of finite number of damped oscillators, the conventional method such as Fourier transform adopts continuous bases in a frequency domain. Thus, the uncertainty of frequency appears and it is difficult to estimate the initial phase. Moreover, measurement artifacts are imposed on the CP signal and deforms the Fourier spectrum. In contrast, the proposed method can separate the signal from the artifact precisely and can successfully estimate physical properties of the normal modes.

Unconstrained and Noninvasive Measurement of Swimming Behavior of Small Fish Based on Ventilatory Signals

NASA Astrophysics Data System (ADS)

Kitayama, Shigehisa; Soh, Zu; Hirano, Akira; Tsuji, Toshio; Takiguchi, Noboru; Ohtake, Hisao

Ventilatory signal is a kind of bioelectric signals reflecting the ventilatory conditions of fish, and has received recent attention as an indicator for assessment of water quality, since breathing is adjusted by the respiratory center according to changes in the underwater environment surrounding the fish. The signals are thus beginning to be used in bioassay systems for water examination. Other than ventilatory conditions, swimming behavior also contains important information for water examination. The conventional bioassay systems, however, only measure either ventilatory signals or swimming behavior. This paper proposes a new unconstrained and noninvasive measurement method that is capable of conducting ventilatory signal measurement and behavioral analysis of fish at the same time. The proposed method estimates the position and the velocity of a fish in free-swimming conditions using power spectrum distribution of measured ventilatory signals from multiple electrodes. This allowed the system to avoid using a camera system which requires light sources. In order to validate estimation accuracy, the position and the velocity estimated by the proposed method were compared to those obtained from video analysis. The results confirmed that the estimated error of the fish positions was within the size of fish, and the correlation coefficient between the velocities was 0.906. The proposed method thus not only can measure the ventilatory signals, but also performs behavioral analysis as accurate as using a video camera.

A phase match based frequency estimation method for sinusoidal signals

NASA Astrophysics Data System (ADS)

Shen, Yan-Lin; Tu, Ya-Qing; Chen, Lin-Jun; Shen, Ting-Ao

2015-04-01

Accurate frequency estimation affects the ranging precision of linear frequency modulated continuous wave (LFMCW) radars significantly. To improve the ranging precision of LFMCW radars, a phase match based frequency estimation method is proposed. To obtain frequency estimation, linear prediction property, autocorrelation, and cross correlation of sinusoidal signals are utilized. The analysis of computational complex shows that the computational load of the proposed method is smaller than those of two-stage autocorrelation (TSA) and maximum likelihood. Simulations and field experiments are performed to validate the proposed method, and the results demonstrate the proposed method has better performance in terms of frequency estimation precision than methods of Pisarenko harmonic decomposition, modified covariance, and TSA, which contribute to improving the precision of LFMCW radars effectively.

Constrained signal reconstruction from wavelet transform coefficients

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

Brislawn, C.M.

1991-12-31

A new method is introduced for reconstructing a signal from an incomplete sampling of its Discrete Wavelet Transform (DWT). The algorithm yields a minimum-norm estimate satisfying a priori upper and lower bounds on the signal. The method is based on a finite-dimensional representation theory for minimum-norm estimates of bounded signals developed by R.E. Cole. Cole`s work has its origins in earlier techniques of maximum-entropy spectral estimation due to Lang and McClellan, which were adapted by Steinhardt, Goodrich and Roberts for minimum-norm spectral estimation. Cole`s extension of their work provides a representation for minimum-norm estimates of a class of generalized transformsmore » in terms of general correlation data (not just DFT`s of autocorrelation lags, as in spectral estimation). One virtue of this great generality is that it includes the inverse DWT. 20 refs.« less

Processing of Antenna-Array Signals on the Basis of the Interference Model Including a Rank-Deficient Correlation Matrix

NASA Astrophysics Data System (ADS)

Rodionov, A. A.; Turchin, V. I.

2017-06-01

We propose a new method of signal processing in antenna arrays, which is called the Maximum-Likelihood Signal Classification. The proposed method is based on the model in which interference includes a component with a rank-deficient correlation matrix. Using numerical simulation, we show that the proposed method allows one to ensure variance of the estimated arrival angle of the plane wave, which is close to the Cramer-Rao lower boundary and more efficient than the best-known MUSIC method. It is also shown that the proposed technique can be efficiently used for estimating the time dependence of the useful signal.

Parameter Estimation of Multiple Frequency-Hopping Signals with Two Sensors

PubMed Central

Pan, Jin; Ma, Boyuan

2018-01-01

This paper essentially focuses on parameter estimation of multiple wideband emitting sources with time-varying frequencies, such as two-dimensional (2-D) direction of arrival (DOA) and signal sorting, with a low-cost circular synthetic array (CSA) consisting of only two rotating sensors. Our basic idea is to decompose the received data, which is a superimposition of phase measurements from multiple sources into separated groups and separately estimate the DOA associated with each source. Motivated by joint parameter estimation, we propose to adopt the expectation maximization (EM) algorithm in this paper; our method involves two steps, namely, the expectation-step (E-step) and the maximization (M-step). In the E-step, the correspondence of each signal with its emitting source is found. Then, in the M-step, the maximum-likelihood (ML) estimates of the DOA parameters are obtained. These two steps are iteratively and alternatively executed to jointly determine the DOAs and sort multiple signals. Closed-form DOA estimation formulae are developed by ML estimation based on phase data, which also realize an optimal estimation. Directional ambiguity is also addressed by another ML estimation method based on received complex responses. The Cramer-Rao lower bound is derived for understanding the estimation accuracy and performance comparison. The verification of the proposed method is demonstrated with simulations. PMID:29617323

A fast signal subspace approach for the determination of absolute levels from phased microphone array measurements

NASA Astrophysics Data System (ADS)

Sarradj, Ennes

2010-04-01

Phased microphone arrays are used in a variety of applications for the estimation of acoustic source location and spectra. The popular conventional delay-and-sum beamforming methods used with such arrays suffer from inaccurate estimations of absolute source levels and in some cases also from low resolution. Deconvolution approaches such as DAMAS have better performance, but require high computational effort. A fast beamforming method is proposed that can be used in conjunction with a phased microphone array in applications with focus on the correct quantitative estimation of acoustic source spectra. This method bases on an eigenvalue decomposition of the cross spectral matrix of microphone signals and uses the eigenvalues from the signal subspace to estimate absolute source levels. The theoretical basis of the method is discussed together with an assessment of the quality of the estimation. Experimental tests using a loudspeaker setup and an airfoil trailing edge noise setup in an aeroacoustic wind tunnel show that the proposed method is robust and leads to reliable quantitative results.

A novel technique for fetal heart rate estimation from Doppler ultrasound signal

PubMed Central

2011-01-01

Background The currently used fetal monitoring instrumentation that is based on Doppler ultrasound technique provides the fetal heart rate (FHR) signal with limited accuracy. It is particularly noticeable as significant decrease of clinically important feature - the variability of FHR signal. The aim of our work was to develop a novel efficient technique for processing of the ultrasound signal, which could estimate the cardiac cycle duration with accuracy comparable to a direct electrocardiography. Methods We have proposed a new technique which provides the true beat-to-beat values of the FHR signal through multiple measurement of a given cardiac cycle in the ultrasound signal. The method consists in three steps: the dynamic adjustment of autocorrelation window, the adaptive autocorrelation peak detection and determination of beat-to-beat intervals. The estimated fetal heart rate values and calculated indices describing variability of FHR, were compared to the reference data obtained from the direct fetal electrocardiogram, as well as to another method for FHR estimation. Results The results revealed that our method increases the accuracy in comparison to currently used fetal monitoring instrumentation, and thus enables to calculate reliable parameters describing the variability of FHR. Relating these results to the other method for FHR estimation we showed that in our approach a much lower number of measured cardiac cycles was rejected as being invalid. Conclusions The proposed method for fetal heart rate determination on a beat-to-beat basis offers a high accuracy of the heart interval measurement enabling reliable quantitative assessment of the FHR variability, at the same time reducing the number of invalid cardiac cycle measurements. PMID:21999764

SU-E-I-08: Investigation of Deconvolution Methods for Blocker-Based CBCT Scatter Estimation

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

Zhao, C; Jin, M; Ouyang, L

2015-06-15

Purpose: To investigate whether deconvolution methods can improve the scatter estimation under different blurring and noise conditions for blocker-based scatter correction methods for cone-beam X-ray computed tomography (CBCT). Methods: An “ideal” projection image with scatter was first simulated for blocker-based CBCT data acquisition by assuming no blurring effect and no noise. The ideal image was then convolved with long-tail point spread functions (PSF) with different widths to mimic the blurring effect from the finite focal spot and detector response. Different levels of noise were also added. Three deconvolution Methods: 1) inverse filtering; 2) Wiener; and 3) Richardson-Lucy, were used tomore » recover the scatter signal in the blocked region. The root mean square error (RMSE) of estimated scatter serves as a quantitative measure for the performance of different methods under different blurring and noise conditions. Results: Due to the blurring effect, the scatter signal in the blocked region is contaminated by the primary signal in the unblocked region. The direct use of the signal in the blocked region to estimate scatter (“direct method”) leads to large RMSE values, which increase with the increased width of PSF and increased noise. The inverse filtering is very sensitive to noise and practically useless. The Wiener and Richardson-Lucy deconvolution methods significantly improve scatter estimation compared to the direct method. For a typical medium PSF and medium noise condition, both methods (∼20 RMSE) can achieve 4-fold improvement over the direct method (∼80 RMSE). The Wiener method deals better with large noise and Richardson-Lucy works better on wide PSF. Conclusion: We investigated several deconvolution methods to recover the scatter signal in the blocked region for blocker-based scatter correction for CBCT. Our simulation results demonstrate that Wiener and Richardson-Lucy deconvolution can significantly improve the scatter estimation compared to the direct method.« less

An Introduction to Goodness of Fit for PMU Parameter Estimation

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

Riepnieks, Artis; Kirkham, Harold

2017-10-01

New results of measurements of phasor-like signals are presented based on our previous work on the topic. In this document an improved estimation method is described. The algorithm (which is realized in MATLAB software) is discussed. We examine the effect of noisy and distorted signals on the Goodness of Fit metric. The estimation method is shown to be performing very well with clean data and with a measurement window as short as a half a cycle and as few as 5 samples per cycle. The Goodness of Fit decreases predictably with added phase noise, and seems to be acceptable evenmore » with visible distortion in the signal. While the exact results we obtain are specific to our method of estimation, the Goodness of Fit method could be implemented in any phasor measurement unit.« less

Removing damped sinusoidal vibrations in adaptive optics systems using a DFT-based estimation method

NASA Astrophysics Data System (ADS)

Kania, Dariusz

2017-06-01

The problem of a vibrations rejection in adaptive optics systems is still present in publications. These undesirable signals emerge because of shaking the system structure, the tracking process, etc., and they usually are damped sinusoidal signals. There are some mechanical solutions to reduce the signals but they are not very effective. One of software solutions are very popular adaptive methods. An AVC (Adaptive Vibration Cancellation) method has been presented and developed in recent years. The method is based on the estimation of three vibrations parameters and values of frequency, amplitude and phase are essential to produce and adjust a proper signal to reduce or eliminate vibrations signals. This paper presents a fast (below 10 ms) and accurate estimation method of frequency, amplitude and phase of a multifrequency signal that can be used in the AVC method to increase the AO system performance. The method accuracy depends on several parameters: CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, THD, b - number of A/D converter bits in a real time system, γ - the damping ratio of the tested signal, φ - the phase of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value of systematic error for γ = 0.1%, CiR = 1.1 and N = 32 is approximately 10^-4 Hz/Hz. This paper focuses on systematic errors of and effect of the signal phase and values of γ on the results.

Estimating the delay-Doppler of target echo in a high clutter underwater environment using wideband linear chirp signals: Evaluation of performance with experimental data.

PubMed

Yu, Ge; Yang, T C; Piao, Shengchun

2017-10-01

A chirp signal is a signal with linearly varying instantaneous frequency over the signal bandwidth, also known as a linear frequency modulated (LFM) signal. It is widely used in communication, radar, active sonar, and other applications due to its Doppler tolerance property in signal detection using the matched filter (MF) processing. Modern sonar uses high-gain, wideband signals to improve the signal to reverberation ratio. High gain implies a high product of the signal bandwidth and duration. However, wideband and/or long duration LFM signals are no longer Doppler tolerant. The shortcoming of the standard MF processing is loss of performance, and bias in range estimation. This paper uses the wideband ambiguity function and the fractional Fourier transform method to estimate the target velocity and restore the performance. Target velocity or Doppler provides a clue for differentiating the target from the background reverberation and clutter. The methods are applied to simulated and experimental data.

Modified ADALINE algorithm for harmonic estimation and selective harmonic elimination in inverters

NASA Astrophysics Data System (ADS)

Vasumathi, B.; Moorthi, S.

2011-11-01

In digital signal processing, algorithms are very well developed for the estimation of harmonic components. In power electronic applications, an objective like fast response of a system is of primary importance. An effective method for the estimation of instantaneous harmonic components, along with conventional harmonic elimination technique, is presented in this article. The primary function is to eliminate undesirable higher harmonic components from the selected signal (current or voltage) and it requires only the knowledge of the frequency of the component to be eliminated. A signal processing technique using modified ADALINE algorithm has been proposed for harmonic estimation. The proposed method stays effective as it converges to a minimum error and brings out a finer estimation. A conventional control based on pulse width modulation for selective harmonic elimination is used to eliminate harmonic components after its estimation. This method can be applied to a wide range of equipment. The validity of the proposed method to estimate and eliminate voltage harmonics is proved with a dc/ac inverter as a simulation example. Then, the results are compared with existing ADALINE algorithm for illustrating its effectiveness.

Methods and Apparatus for Reducing Multipath Signal Error Using Deconvolution

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor); Lau, Kenneth H. (Inventor)

1999-01-01

A deconvolution approach to adaptive signal processing has been applied to the elimination of signal multipath errors as embodied in one preferred embodiment in a global positioning system receiver. The method and receiver of the present invention estimates then compensates for multipath effects in a comprehensive manner. Application of deconvolution, along with other adaptive identification and estimation techniques, results in completely novel GPS (Global Positioning System) receiver architecture.

An estimation method for echo signal energy of pipe inner surface longitudinal crack detection by 2-D energy coefficients integration

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

Zhou, Shiyuan, E-mail: redaple@bit.edu.cn; Sun, Haoyu, E-mail: redaple@bit.edu.cn; Xu, Chunguang, E-mail: redaple@bit.edu.cn

The echo signal energy is directly affected by the incident sound beam eccentricity or angle for thick-walled pipes inner longitudinal cracks detection. A method for analyzing the relationship between echo signal energy between the values of incident eccentricity is brought forward, which can be used to estimate echo signal energy when testing inside wall longitudinal crack of pipe, using mode-transformed compression wave adaptation of shear wave with water-immersion method, by making a two-dimension integration of “energy coefficient” in both circumferential and axial directions. The calculation model is founded for cylinder sound beam case, in which the refraction and reflection energymore » coefficients of different rays in the whole sound beam are considered different. The echo signal energy is calculated for a particular cylinder sound beam testing different pipes: a beam with a diameter of 0.5 inch (12.7mm) testing a φ279.4mm pipe and a φ79.4mm one. As a comparison, both the results of two-dimension integration and one-dimension (circumferential direction) integration are listed, and only the former agrees well with experimental results. The estimation method proves to be valid and shows that the usual method of simplifying the sound beam as a single ray for estimating echo signal energy and choosing optimal incident eccentricity is not so appropriate.« less

An estimation method for echo signal energy of pipe inner surface longitudinal crack detection by 2-D energy coefficients integration

NASA Astrophysics Data System (ADS)

Zhou, Shiyuan; Sun, Haoyu; Xu, Chunguang; Cao, Xiandong; Cui, Liming; Xiao, Dingguo

2015-03-01

The echo signal energy is directly affected by the incident sound beam eccentricity or angle for thick-walled pipes inner longitudinal cracks detection. A method for analyzing the relationship between echo signal energy between the values of incident eccentricity is brought forward, which can be used to estimate echo signal energy when testing inside wall longitudinal crack of pipe, using mode-transformed compression wave adaptation of shear wave with water-immersion method, by making a two-dimension integration of "energy coefficient" in both circumferential and axial directions. The calculation model is founded for cylinder sound beam case, in which the refraction and reflection energy coefficients of different rays in the whole sound beam are considered different. The echo signal energy is calculated for a particular cylinder sound beam testing different pipes: a beam with a diameter of 0.5 inch (12.7mm) testing a φ279.4mm pipe and a φ79.4mm one. As a comparison, both the results of two-dimension integration and one-dimension (circumferential direction) integration are listed, and only the former agrees well with experimental results. The estimation method proves to be valid and shows that the usual method of simplifying the sound beam as a single ray for estimating echo signal energy and choosing optimal incident eccentricity is not so appropriate.

Method of Enhancing On-Board State Estimation Using Communication Signals

NASA Technical Reports Server (NTRS)

Anzalone, Evan J. (Inventor); Chuang, Jason C. H. (Inventor)

2015-01-01

A method of enhancing on-board state estimation for a spacecraft utilizes a network of assets to include planetary-based assets and space-based assets. Communication signals transmitted from each of the assets into space are defined by a common protocol. Data is embedded in each communication signal transmitted by the assets. The data includes a time-of-transmission for a corresponding one of the communication signals and a position of a corresponding one of the assets at the time-of-transmission. A spacecraft is equipped to receive the communication signals, has a clock synchronized to the space-wide time reference frame, and has a processor programmed to generate state estimates of the spacecraft. Using its processor, the spacecraft determines a one-dimensional range from itself to at least one of the assets and then updates its state estimates using each one-dimensional range.

a Method Using Gnss Lh-Reflected Signals for Soil Roughness Estimation

NASA Astrophysics Data System (ADS)

Jia, Y.; Li, W.; Chen, Y.; Lv, H.; Pei, Y.

2018-04-01

Global Navigation Satellite System Reflectometry (GNSS-R) is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. The receiver can be on the ground or installed on a small aircraft or UAV and collects the electromagnetic field scattered from the surface of the Earth. The received signals are then analyzed to determine the characteristics of the surface. Many research has been reported showing the capability of the GNSS-R technique. However, the roughness of the surface impacts the phase and amplitude of the received signals, which is still a worthwhile study. This paper presented a method can be used by GNSS-R to estimate the surface roughness. First, the data was calculated in the specular reflection with the assumption of a flat surface with different permittivity. Since the power reflectivity can be evaluated as the ratio of left-hand (LH) reflected signal to the direct right-hand (RH) signal. Then a semi-empirical roughness model was applied to the data for testing. The results showed the method can distinguish the water and the soil surface. The sensitivity of the parameters was also analyzed. It indicates this method for soil roughness estimation can be used by GNSS-R LH reflected signals. In the next step, several experiments need to be done for improving the model and exploring the way of the estimation.

Chirplet Wigner-Ville distribution for time-frequency representation and its application

NASA Astrophysics Data System (ADS)

Chen, G.; Chen, J.; Dong, G. M.

2013-12-01

This paper presents a Chirplet Wigner-Ville Distribution (CWVD) that is free for cross-term that usually occurs in Wigner-Ville distribution (WVD). By transforming the signal with frequency rotating operators, several mono-frequency signals without intermittent are obtained, WVD is applied to the rotated signals that is cross-term free, then some frequency shift operators corresponding to the rotating operator are utilized to relocate the signal‧s instantaneous frequencies (IFs). The operators‧ parameters come from the estimation of the IFs which are approached with a polynomial functions or spline functions. What is more, by analysis of error, the main factors for the performance of the novel method have been discovered and an effective signal extending method based on the IFs estimation has been developed to improve the energy concentration of WVD. The excellent performance of the novel method was manifested by applying it to estimate the IFs of some numerical signals and the echolocation signal emitted by the Large Brown Bat.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

PubMed

Takeshima, Hidenori; Saitoh, Kanako; Nitta, Shuhei; Shiodera, Taichiro; Takeguchi, Tomoyuki; Bannae, Shuhei; Kuhara, Shigehide

2018-03-13

Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k - t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements. The proposed method achieves a reduction in acquisition time by estimating the spatiotemporal (x - f) sensitivity without requiring the acquisition of the alias-free signals, typical of the k - t SENSE technique. The cost-effective reconstruction, in turn, is achieved by a computationally efficient estimation of the x - f sensitivity from the band-limited signals of the aliased inputs. Such band-limited signals are suitable for sensitivity estimation because the strongly aliased signals have been removed. For the same reduction factor 4, the net reduction factor 4 for the proposed method was significantly higher than the factor 2.29 achieved by k - t SENSE. The processing time is reduced from 4.1 s for k - t SENSE to 1.7 s for the proposed method. The image quality obtained using the proposed method proved to be superior (mean squared error [MSE] ± standard deviation [SD] = 6.85 ± 2.73) compared to the k - t SENSE case (MSE ± SD = 12.73 ± 3.60) for the vertical long-axis (VLA) view, as well as other views. In the present study, k - t SENSE was identified as a suitable base method to be improved achieving both short acquisition times and a cost-effective reconstruction. To enhance these characteristics of base method, a novel implementation is proposed, estimating the x - f sensitivity without the need for an explicit scan of the reference signals. Experimental results showed that the acquisition, computational times and image quality for the proposed method were improved compared to the standard k - t SENSE method.

P-Code-Enhanced Encryption-Mode Processing of GPS Signals

NASA Technical Reports Server (NTRS)

Young, Lawrence; Meehan, Thomas; Thomas, Jess B.

2003-01-01

A method of processing signals in a Global Positioning System (GPS) receiver has been invented to enable the receiver to recover some of the information that is otherwise lost when GPS signals are encrypted at the transmitters. The need for this method arises because, at the option of the military, precision GPS code (P-code) is sometimes encrypted by a secret binary code, denoted the A code. Authorized users can recover the full signal with knowledge of the A-code. However, even in the absence of knowledge of the A-code, one can track the encrypted signal by use of an estimate of the A-code. The present invention is a method of making and using such an estimate. In comparison with prior such methods, this method makes it possible to recover more of the lost information and obtain greater accuracy.

Heart rate estimation from FBG sensors using cepstrum analysis and sensor fusion.

PubMed

Zhu, Yongwei; Fook, Victor Foo Siang; Jianzhong, Emily Hao; Maniyeri, Jayachandran; Guan, Cuntai; Zhang, Haihong; Jiliang, Eugene Phua; Biswas, Jit

2014-01-01

This paper presents a method of estimating heart rate from arrays of fiber Bragg grating (FBG) sensors embedded in a mat. A cepstral domain signal analysis technique is proposed to characterize Ballistocardiogram (BCG) signals. With this technique, the average heart beat intervals can be estimated by detecting the dominant peaks in the cepstrum, and the signals of multiple sensors can be fused together to obtain higher signal to noise ratio than each individual sensor. Experiments were conducted with 10 human subjects lying on 2 different postures on a bed. The estimated heart rate from BCG was compared with heart rate ground truth from ECG, and the mean error of estimation obtained is below 1 beat per minute (BPM). The results show that the proposed fusion method can achieve promising heart rate measurement accuracy and robustness against various sensor contact conditions.

Passive fetal heart rate monitoring apparatus and method with enhanced fetal heart beat discrimination

NASA Technical Reports Server (NTRS)

Zahorian, Stephen A. (Inventor); Livingston, David L. (Inventor); Pretlow, III, Robert A. (Inventor)

1996-01-01

An apparatus for acquiring signals emitted by a fetus, identifying fetal heart beats and determining a fetal heart rate. Multiple sensor signals are outputted by a passive fetal heart rate monitoring sensor. Multiple parallel nonlinear filters filter these multiple sensor signals to identify fetal heart beats in the signal data. A processor determines a fetal heart rate based on these identified fetal heart beats. The processor includes the use of a figure of merit weighting of heart rate estimates based on the identified heart beats from each filter for each signal. The fetal heart rate thus determined is outputted to a display, storage, or communications channel. A method for enhanced fetal heart beat discrimination includes acquiring signals from a fetus, identifying fetal heart beats from the signals by multiple parallel nonlinear filtering, and determining a fetal heart rate based on the identified fetal heart beats. A figure of merit operation in this method provides for weighting a plurality of fetal heart rate estimates based on the identified fetal heart beats and selecting the highest ranking fetal heart rate estimate.

Passive fetal heart rate monitoring apparatus and method with enhanced fetal heart beat discrimination

NASA Technical Reports Server (NTRS)

Zahorian, Stephen A. (Inventor); Livingston, David L. (Inventor); Pretlow, Robert A., III (Inventor)

1994-01-01

An apparatus for acquiring signals emitted by a fetus, identifying fetal heart beats and determining a fetal heart rate is presented. Multiple sensor signals are outputted by a passive fetal heart rate monitoring sensor. Multiple parallel nonlinear filters filter these multiple sensor signals to identify fetal heart beats in the signal data. A processor determines a fetal heart rate based on these identified fetal heart beats. The processor includes the use of a figure of merit weighting of heart rate estimates based on the identified heart beats from each filter for each signal. The fetal heart rate thus determined is outputted to a display, storage, or communications channel. A method for enhanced fetal heart beat discrimination includes acquiring signals from a fetus, identifying fetal heart beats from the signals by multiple parallel nonlinear filtering, and determining a fetal heart rate based on the identified fetal heart beats. A figure of merit operation in this method provides for weighting a plurality of fetal heart rate estimates based on the identified fetal heart beats and selecting the highest ranking fetal heart rate estimate.

An internal reference model-based PRF temperature mapping method with Cramer-Rao lower bound noise performance analysis.

PubMed

Li, Cheng; Pan, Xinyi; Ying, Kui; Zhang, Qiang; An, Jing; Weng, Dehe; Qin, Wen; Li, Kuncheng

2009-11-01

The conventional phase difference method for MR thermometry suffers from disturbances caused by the presence of lipid protons, motion-induced error, and field drift. A signal model is presented with multi-echo gradient echo (GRE) sequence using a fat signal as an internal reference to overcome these problems. The internal reference signal model is fit to the water and fat signals by the extended Prony algorithm and the Levenberg-Marquardt algorithm to estimate the chemical shifts between water and fat which contain temperature information. A noise analysis of the signal model was conducted using the Cramer-Rao lower bound to evaluate the noise performance of various algorithms, the effects of imaging parameters, and the influence of the water:fat signal ratio in a sample on the temperature estimate. Comparison of the calculated temperature map and thermocouple temperature measurements shows that the maximum temperature estimation error is 0.614 degrees C, with a standard deviation of 0.06 degrees C, confirming the feasibility of this model-based temperature mapping method. The influence of sample water:fat signal ratio on the accuracy of the temperature estimate is evaluated in a water-fat mixed phantom experiment with an optimal ratio of approximately 0.66:1. (c) 2009 Wiley-Liss, Inc.

A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging.

PubMed

Koay, Cheng Guan; Chang, Lin-Ching; Carew, John D; Pierpaoli, Carlo; Basser, Peter J

2006-09-01

A unifying theoretical and algorithmic framework for diffusion tensor estimation is presented. Theoretical connections among the least squares (LS) methods, (linear least squares (LLS), weighted linear least squares (WLLS), nonlinear least squares (NLS) and their constrained counterparts), are established through their respective objective functions, and higher order derivatives of these objective functions, i.e., Hessian matrices. These theoretical connections provide new insights in designing efficient algorithms for NLS and constrained NLS (CNLS) estimation. Here, we propose novel algorithms of full Newton-type for the NLS and CNLS estimations, which are evaluated with Monte Carlo simulations and compared with the commonly used Levenberg-Marquardt method. The proposed methods have a lower percent of relative error in estimating the trace and lower reduced chi2 value than those of the Levenberg-Marquardt method. These results also demonstrate that the accuracy of an estimate, particularly in a nonlinear estimation problem, is greatly affected by the Hessian matrix. In other words, the accuracy of a nonlinear estimation is algorithm-dependent. Further, this study shows that the noise variance in diffusion weighted signals is orientation dependent when signal-to-noise ratio (SNR) is low (

Nonlinear least squares regression for single image scanning electron microscope signal-to-noise ratio estimation.

PubMed

Sim, K S; Norhisham, S

2016-11-01

A new method based on nonlinear least squares regression (NLLSR) is formulated to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. The estimation of SNR value based on NLLSR method is compared with the three existing methods of nearest neighbourhood, first-order interpolation and the combination of both nearest neighbourhood and first-order interpolation. Samples of SEM images with different textures, contrasts and edges were used to test the performance of NLLSR method in estimating the SNR values of the SEM images. It is shown that the NLLSR method is able to produce better estimation accuracy as compared to the other three existing methods. According to the SNR results obtained from the experiment, the NLLSR method is able to produce approximately less than 1% of SNR error difference as compared to the other three existing methods. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence.

PubMed

Taylor, Terence E; Lacalle Muls, Helena; Costello, Richard W; Reilly, Richard B

2018-01-01

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence.

Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence

PubMed Central

Lacalle Muls, Helena; Costello, Richard W.; Reilly, Richard B.

2018-01-01

Asthma and chronic obstructive pulmonary disease (COPD) patients are required to inhale forcefully and deeply to receive medication when using a dry powder inhaler (DPI). There is a clinical need to objectively monitor the inhalation flow profile of DPIs in order to remotely monitor patient inhalation technique. Audio-based methods have been previously employed to accurately estimate flow parameters such as the peak inspiratory flow rate of inhalations, however, these methods required multiple calibration inhalation audio recordings. In this study, an audio-based method is presented that accurately estimates inhalation flow profile using only one calibration inhalation audio recording. Twenty healthy participants were asked to perform 15 inhalations through a placebo Ellipta™ DPI at a range of inspiratory flow rates. Inhalation flow signals were recorded using a pneumotachograph spirometer while inhalation audio signals were recorded simultaneously using the Inhaler Compliance Assessment device attached to the inhaler. The acoustic (amplitude) envelope was estimated from each inhalation audio signal. Using only one recording, linear and power law regression models were employed to determine which model best described the relationship between the inhalation acoustic envelope and flow signal. Each model was then employed to estimate the flow signals of the remaining 14 inhalation audio recordings. This process repeated until each of the 15 recordings were employed to calibrate single models while testing on the remaining 14 recordings. It was observed that power law models generated the highest average flow estimation accuracy across all participants (90.89±0.9% for power law models and 76.63±2.38% for linear models). The method also generated sufficient accuracy in estimating inhalation parameters such as peak inspiratory flow rate and inspiratory capacity within the presence of noise. Estimating inhaler inhalation flow profiles using audio based methods may be clinically beneficial for inhaler technique training and the remote monitoring of patient adherence. PMID:29346430

A MIMO radar quadrature and multi-channel amplitude-phase error combined correction method based on cross-correlation

NASA Astrophysics Data System (ADS)

Yun, Lingtong; Zhao, Hongzhong; Du, Mengyuan

2018-04-01

Quadrature and multi-channel amplitude-phase error have to be compensated in the I/Q quadrature sampling and signal through multi-channel. A new method that it doesn't need filter and standard signal is presented in this paper. And it can combined estimate quadrature and multi-channel amplitude-phase error. The method uses cross-correlation and amplitude ratio between the signal to estimate the two amplitude-phase errors simply and effectively. And the advantages of this method are verified by computer simulation. Finally, the superiority of the method is also verified by measure data of outfield experiments.

A novel joint timing/frequency synchronization scheme based on Radon-Wigner transform of LFM signals in CO-OFDM systems

NASA Astrophysics Data System (ADS)

Liu, Jianfei; Wei, Ying; Zeng, Xiangye; Lu, Jia; Zhang, Shuangxi; Wang, Mengjun

2018-03-01

A joint timing and frequency synchronization method has been proposed for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) system in this paper. The timing offset (TO), integer frequency offset (FO) and the fractional FO can be realized by only one training symbol, which consists of two linear frequency modulation (LFM) signals with opposite chirp rates. By detecting the peak of LFM signals after Radon-Wigner transform (RWT), the TO and the integer FO can be estimated at the same time, moreover, the fractional FO can be acquired correspondingly through the self-correlation characteristic of the same training symbol. Simulation results show that the proposed method can give a more accurate TO estimation than the existing methods, especially at poor OSNR conditions; for the FO estimation, both the fractional and the integer FO can be estimated through the proposed training symbol with no extra overhead, a more accurate estimation and a large FO estimation range of [ - 5 GHz, 5GHz] can be acquired.

Time-frequency domain SNR estimation and its application in seismic data processing

NASA Astrophysics Data System (ADS)

Zhao, Yan; Liu, Yang; Li, Xuxuan; Jiang, Nansen

2014-08-01

Based on an approach estimating frequency domain signal-to-noise ratio (FSNR), we propose a method to evaluate time-frequency domain signal-to-noise ratio (TFSNR). This method adopts short-time Fourier transform (STFT) to estimate instantaneous power spectrum of signal and noise, and thus uses their ratio to compute TFSNR. Unlike FSNR describing the variation of SNR with frequency only, TFSNR depicts the variation of SNR with time and frequency, and thus better handles non-stationary seismic data. By considering TFSNR, we develop methods to improve the effects of inverse Q filtering and high frequency noise attenuation in seismic data processing. Inverse Q filtering considering TFSNR can better solve the problem of amplitude amplification of noise. The high frequency noise attenuation method considering TFSNR, different from other de-noising methods, distinguishes and suppresses noise using an explicit criterion. Examples of synthetic and real seismic data illustrate the correctness and effectiveness of the proposed methods.

Maximum a posteriori decoder for digital communications

NASA Technical Reports Server (NTRS)

Altes, Richard A. (Inventor)

1997-01-01

A system and method for decoding by identification of the most likely phase coded signal corresponding to received data. The present invention has particular application to communication with signals that experience spurious random phase perturbations. The generalized estimator-correlator uses a maximum a posteriori (MAP) estimator to generate phase estimates for correlation with incoming data samples and for correlation with mean phases indicative of unique hypothesized signals. The result is a MAP likelihood statistic for each hypothesized transmission, wherein the highest value statistic identifies the transmitted signal.

Respiratory rate estimation from the built-in cameras of smartphones and tablets.

PubMed

Nam, Yunyoung; Lee, Jinseok; Chon, Ki H

2014-04-01

This paper presents a method for respiratory rate estimation using the camera of a smartphone, an MP3 player or a tablet. The iPhone 4S, iPad 2, iPod 5, and Galaxy S3 were used to estimate respiratory rates from the pulse signal derived from a finger placed on the camera lens of these devices. Prior to estimation of respiratory rates, we systematically investigated the optimal signal quality of these 4 devices by dividing the video camera's resolution into 12 different pixel regions. We also investigated the optimal signal quality among the red, green and blue color bands for each of these 12 pixel regions for all four devices. It was found that the green color band provided the best signal quality for all 4 devices and that the left half VGA pixel region was found to be the best choice only for iPhone 4S. For the other three devices, smaller 50 × 50 pixel regions were found to provide better or equally good signal quality than the larger pixel regions. Using the green signal and the optimal pixel regions derived from the four devices, we then investigated the suitability of the smartphones, the iPod 5 and the tablet for respiratory rate estimation using three different computational methods: the autoregressive (AR) model, variable-frequency complex demodulation (VFCDM), and continuous wavelet transform (CWT) approaches. Specifically, these time-varying spectral techniques were used to identify the frequency and amplitude modulations as they contain respiratory rate information. To evaluate the performance of the three computational methods and the pixel regions for the optimal signal quality, data were collected from 10 healthy subjects. It was found that the VFCDM method provided good estimates of breathing rates that were in the normal range (12-24 breaths/min). Both CWT and VFCDM methods provided reasonably good estimates for breathing rates that were higher than 26 breaths/min but their accuracy degraded concomitantly with increased respiratory rates. Overall, the VFCDM method provided the best results for accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.5 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12 to 36 breaths/min. The AR method provided the least accurate respiratory rate estimation among the three methods. This work illustrates that both heart rates and normal breathing rates can be accurately derived from a video signal obtained from smartphones, an MP3 player and tablets with or without a flashlight.

Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

PubMed Central

Adib, Mani; Cretu, Edmond

2013-01-01

We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786

Computing moment to moment BOLD activation for real-time neurofeedback

PubMed Central

Hinds, Oliver; Ghosh, Satrajit; Thompson, Todd W.; Yoo, Julie J.; Whitfield-Gabrieli, Susan; Triantafyllou, Christina; Gabrieli, John D.E.

2013-01-01

Estimating moment to moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment to moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment to moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI timeseries, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method. PMID:20682350

Monaural room acoustic parameters from music and speech.

PubMed

Kendrick, Paul; Cox, Trevor J; Li, Francis F; Zhang, Yonggang; Chambers, Jonathon A

2008-07-01

This paper compares two methods for extracting room acoustic parameters from reverberated speech and music. An approach which uses statistical machine learning, previously developed for speech, is extended to work with music. For speech, reverberation time estimations are within a perceptual difference limen of the true value. For music, virtually all early decay time estimations are within a difference limen of the true value. The estimation accuracy is not good enough in other cases due to differences between the simulated data set used to develop the empirical model and real rooms. The second method carries out a maximum likelihood estimation on decay phases at the end of notes or speech utterances. This paper extends the method to estimate parameters relating to the balance of early and late energies in the impulse response. For reverberation time and speech, the method provides estimations which are within the perceptual difference limen of the true value. For other parameters such as clarity, the estimations are not sufficiently accurate due to the natural reverberance of the excitation signals. Speech is a better test signal than music because of the greater periods of silence in the signal, although music is needed for low frequency measurement.

A Frequency-Domain Multipath Parameter Estimation and Mitigation Method for BOC-Modulated GNSS Signals

PubMed Central

Sun, Chao; Feng, Wenquan; Du, Songlin

2018-01-01

As multipath is one of the dominating error sources for high accuracy Global Navigation Satellite System (GNSS) applications, multipath mitigation approaches are employed to minimize this hazardous error in receivers. Binary offset carrier modulation (BOC), as a modernized signal structure, is adopted to achieve significant enhancement. However, because of its multi-peak autocorrelation function, conventional multipath mitigation techniques for binary phase shift keying (BPSK) signal would not be optimal. Currently, non-parametric and parametric approaches have been studied specifically aiming at multipath mitigation for BOC signals. Non-parametric techniques, such as Code Correlation Reference Waveforms (CCRW), usually have good feasibility with simple structures, but suffer from low universal applicability for different BOC signals. Parametric approaches can thoroughly eliminate multipath error by estimating multipath parameters. The problems with this category are at the high computation complexity and vulnerability to the noise. To tackle the problem, we present a practical parametric multipath estimation method in the frequency domain for BOC signals. The received signal is transferred to the frequency domain to separate out the multipath channel transfer function for multipath parameter estimation. During this process, we take the operations of segmentation and averaging to reduce both noise effect and computational load. The performance of the proposed method is evaluated and compared with the previous work in three scenarios. Results indicate that the proposed averaging-Fast Fourier Transform (averaging-FFT) method achieves good robustness in severe multipath environments with lower computational load for both low-order and high-order BOC signals. PMID:29495589

DOA Finding with Support Vector Regression Based Forward-Backward Linear Prediction.

PubMed

Pan, Jingjing; Wang, Yide; Le Bastard, Cédric; Wang, Tianzhen

2017-05-27

Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward-backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.

Effect of Non-speckle Echo Signals on Tissue Characteristics for Liver Fibrosis using Probability Density Function of Ultrasonic B-mode image

NASA Astrophysics Data System (ADS)

Mori, Shohei; Hirata, Shinnosuke; Yamaguchi, Tadashi; Hachiya, Hiroyuki

To develop a quantitative diagnostic method for liver fibrosis using an ultrasound B-mode image, a probability imaging method of tissue characteristics based on a multi-Rayleigh model, which expresses a probability density function of echo signals from liver fibrosis, has been proposed. In this paper, an effect of non-speckle echo signals on tissue characteristics estimated from the multi-Rayleigh model was evaluated. Non-speckle signals were determined and removed using the modeling error of the multi-Rayleigh model. The correct tissue characteristics of fibrotic tissue could be estimated with the removal of non-speckle signals.

Investigation of signal models and methods for evaluating structures of processing telecommunication information exchange systems under acoustic noise conditions

NASA Astrophysics Data System (ADS)

Kropotov, Y. A.; Belov, A. A.; Proskuryakov, A. Y.; Kolpakov, A. A.

2018-05-01

The paper considers models and methods for estimating signals during the transmission of information messages in telecommunication systems of audio exchange. One-dimensional probability distribution functions that can be used to isolate useful signals, and acoustic noise interference are presented. An approach to the estimation of the correlation and spectral functions of the parameters of acoustic signals is proposed, based on the parametric representation of acoustic signals and the components of the noise components. The paper suggests an approach to improving the efficiency of interference cancellation and highlighting the necessary information when processing signals from telecommunications systems. In this case, the suppression of acoustic noise is based on the methods of adaptive filtering and adaptive compensation. The work also describes the models of echo signals and the structure of subscriber devices in operational command telecommunications systems.

A Pseudorange Measurement Scheme Based on Snapshot for Base Station Positioning Receivers.

PubMed

Mo, Jun; Deng, Zhongliang; Jia, Buyun; Bian, Xinmei

2017-12-01

Digital multimedia broadcasting signal is promised to be a wireless positioning signal. This paper mainly studies a multimedia broadcasting technology, named China mobile multimedia broadcasting (CMMB), in the context of positioning. Theoretical and practical analysis on the CMMB signal suggests that the existing CMMB signal does not have the meter positioning capability. So, the CMMB system has been modified to achieve meter positioning capability by multiplexing the CMMB signal and pseudo codes in the same frequency band. The time difference of arrival (TDOA) estimation method is used in base station positioning receivers. Due to the influence of a complex fading channel and the limited bandwidth of receivers, the regular tracking method based on pseudo code ranging is difficult to provide continuous and accurate TDOA estimations. A pseudorange measurement scheme based on snapshot is proposed to solve the problem. This algorithm extracts the TDOA estimation from the stored signal fragments, and utilizes the Taylor expansion of the autocorrelation function to improve the TDOA estimation accuracy. Monte Carlo simulations and real data tests show that the proposed algorithm can significantly reduce the TDOA estimation error for base station positioning receivers, and then the modified CMMB system achieves meter positioning accuracy.

Signal location using generalized linear constraints

NASA Astrophysics Data System (ADS)

Griffiths, Lloyd J.; Feldman, D. D.

1992-01-01

This report has presented a two-part method for estimating the directions of arrival of uncorrelated narrowband sources when there are arbitrary phase errors and angle independent gain errors. The signal steering vectors are estimated in the first part of the method; in the second part, the arrival directions are estimated. It should be noted that the second part of the method can be tailored to incorporate additional information about the nature of the phase errors. For example, if the phase errors are known to be caused solely by element misplacement, the element locations can be estimated concurrently with the DOA's by trying to match the theoretical steering vectors to the estimated ones. Simulation results suggest that, for general perturbation, the method can resolve closely spaced sources under conditions for which a standard high-resolution DOA method such as MUSIC fails.

Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

Validation of attenuation, beam blockage, and calibration estimation methods using two dual polarization X band weather radars

NASA Astrophysics Data System (ADS)

Diederich, M.; Ryzhkov, A.; Simmer, C.; Mühlbauer, K.

2011-12-01

The amplitude a of radar wave reflected by meteorological targets can be misjudged due to several factors. At X band wavelength, attenuation of the radar beam by hydro meteors reduces the signal strength enough to be a significant source of error for quantitative precipitation estimation. Depending on the surrounding orography, the radar beam may be partially blocked when scanning at low elevation angles, and the knowledge of the exact amount of signal loss through beam blockage becomes necessary. The phase shift between the radar signals at horizontal and vertical polarizations is affected by the hydrometeors that the beam travels through, but remains unaffected by variations in signal strength. This has allowed for several ways of compensating for the attenuation of the signal, and for consistency checks between these variables. In this study, we make use of several weather radars and gauge network measuring in the same area to examine the effectiveness of several methods of attenuation and beam blockage corrections. The methods include consistency checks of radar reflectivity and specific differential phase, calculation of beam blockage using a topography map, estimating attenuation using differential propagation phase, and the ZPHI method proposed by Testud et al. in 2000. Results show the high effectiveness of differential phase in estimating attenuation, and potential of the ZPHI method to compensate attenuation, beam blockage, and calibration errors.

A Novel Estimator for the Rate of Information Transfer by Continuous Signals

PubMed Central

Takalo, Jouni; Ignatova, Irina; Weckström, Matti; Vähäsöyrinki, Mikko

2011-01-01

The information transfer rate provides an objective and rigorous way to quantify how much information is being transmitted through a communications channel whose input and output consist of time-varying signals. However, current estimators of information content in continuous signals are typically based on assumptions about the system's linearity and signal statistics, or they require prohibitive amounts of data. Here we present a novel information rate estimator without these limitations that is also optimized for computational efficiency. We validate the method with a simulated Gaussian information channel and demonstrate its performance with two example applications. Information transfer between the input and output signals of a nonlinear system is analyzed using a sensory receptor neuron as the model system. Then, a climate data set is analyzed to demonstrate that the method can be applied to a system based on two outputs generated by interrelated random processes. These analyses also demonstrate that the new method offers consistent performance in situations where classical methods fail. In addition to these examples, the method is applicable to a wide range of continuous time series commonly observed in the natural sciences, economics and engineering. PMID:21494562

Dimension from covariance matrices.

PubMed

Carroll, T L; Byers, J M

2017-02-01

We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems. The algorithm described here compares the eigenvalues of covariance matrices created from an embedded signal to the eigenvalues for a covariance matrix of a Gaussian random process with the same dimension and number of points. A statistical test gives the probability that the eigenvalues for the embedded signal did not come from the Gaussian random process.

Uncertainties associated with parameter estimation in atmospheric infrasound arrays.

PubMed

Szuberla, Curt A L; Olson, John V

2004-01-01

This study describes a method for determining the statistical confidence in estimates of direction-of-arrival and trace velocity stemming from signals present in atmospheric infrasound data. It is assumed that the signal source is far enough removed from the infrasound sensor array that a plane-wave approximation holds, and that multipath and multiple source effects are not present. Propagation path and medium inhomogeneities are assumed not to be known at the time of signal detection, but the ensemble of time delays of signal arrivals between array sensor pairs is estimable and corrupted by uncorrelated Gaussian noise. The method results in a set of practical uncertainties that lend themselves to a geometric interpretation. Although quite general, this method is intended for use by analysts interpreting data from atmospheric acoustic arrays, or those interested in designing and deploying them. The method is applied to infrasound arrays typical of those deployed as a part of the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organization.

A new linear least squares method for T1 estimation from SPGR signals with multiple TRs

NASA Astrophysics Data System (ADS)

Chang, Lin-Ching; Koay, Cheng Guan; Basser, Peter J.; Pierpaoli, Carlo

2009-02-01

The longitudinal relaxation time, T1, can be estimated from two or more spoiled gradient recalled echo x (SPGR) images with two or more flip angles and one or more repetition times (TRs). The function relating signal intensity and the parameters are nonlinear; T1 maps can be computed from SPGR signals using nonlinear least squares regression. A widely-used linear method transforms the nonlinear model by assuming a fixed TR in SPGR images. This constraint is not desirable since multiple TRs are a clinically practical way to reduce the total acquisition time, to satisfy the required resolution, and/or to combine SPGR data acquired at different times. A new linear least squares method is proposed using the first order Taylor expansion. Monte Carlo simulations of SPGR experiments are used to evaluate the accuracy and precision of the estimated T1 from the proposed linear and the nonlinear methods. We show that the new linear least squares method provides T1 estimates comparable in both precision and accuracy to those from the nonlinear method, allowing multiple TRs and reducing computation time significantly.

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing.

PubMed

Li, Jun; Lin, Qiu-Hua; Kang, Chun-Yu; Wang, Kai; Yang, Xiu-Ting

2018-03-18

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets.

Application of wavelet analysis to estimation of parameters of the gravitational-wave signal from a coalescing binary

NASA Astrophysics Data System (ADS)

Królak, Andrzej; Trzaskoma, Pawel

1996-05-01

Application of wavelet analysis to the estimation of parameters of the broad-band gravitational-wave signal emitted by a binary system is investigated. A method of instantaneous frequency extraction first proposed in this context by Innocent and Vinet is used. The gravitational-wave signal from a binary is investigated from the point of view of signal analysis theory and it is shown that such a signal is characterized by a large time - bandwidth product. This property enables the extraction of frequency modulation from the wavelet transform of the signal. The wavelet transform of the chirp signal from a binary is calculated analytically. Numerical simulations with the noisy chirp signal are performed. The gravitational-wave signal from a binary is taken in the quadrupole approximation and it is buried in noise corresponding to three different values of the signal-to-noise ratio and the wavelet method to extract the frequency modulation of the signal is applied. Then, from the frequency modulation, the chirp mass parameter of the binary is estimated. It is found that the chirp mass can be estimated to a good accuracy, typically of the order of (20/0264-9381/13/5/006/img5% where 0264-9381/13/5/006/img6 is the optimal signal-to-noise ratio. It is also shown that the post-Newtonian effects in the gravitational wave signal from a binary can be discriminated to a satisfactory accuracy.

A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals.

PubMed

Zhang, Hongyang; Xu, Luping; Yan, Bo; Zhang, Hua; Luo, Liyan

2017-06-22

Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg-Marquardt (LM) method iteratively. Its performance including Cramér-Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances.

Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation

PubMed Central

Hao, Jiucang; Attias, Hagai; Nagarajan, Srikantan; Lee, Te-Won; Sejnowski, Terrence J.

2010-01-01

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log-spectral domain. This is in contrast to most current models in frequency domain. Exact signal estimation is a computationally intractable problem. We derive three approximations to enhance the efficiency of signal estimation. The Gaussian approximation transforms the log-spectral domain GMM into the frequency domain using minimal Kullback–Leiber (KL)-divergency criterion. The frequency domain Laplace method computes the maximum a posteriori (MAP) estimator for the spectral amplitude. Correspondingly, the log-spectral domain Laplace method computes the MAP estimator for the log-spectral amplitude. Further, the gain and noise spectrum adaptation are implemented using the expectation–maximization (EM) algorithm within the GMM under Gaussian approximation. The proposed algorithms are evaluated by applying them to enhance the speeches corrupted by the speech-shaped noise (SSN). The experimental results demonstrate that the proposed algorithms offer improved signal-to-noise ratio, lower word recognition error rate, and less spectral distortion. PMID:20428253

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements.

PubMed

Da Poian, Giulia; Rozell, Christopher J; Bernardini, Riccardo; Rinaldo, Roberto; Clifford, Gari D

2017-09-14

Compressive Sensing (CS) has recently been applied as a low complexity compression framework for long-term monitoring of electrocardiogram signals using Wireless Body Sensor Networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat to beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in case of compressed sensed data, signal reconstruction can be performed with relatively complex optimisation algorithms, which may require significant energy consumption. This article addresses the problem of hearth rate estimation from compressive sensing electrocardiogram (ECG) recordings, avoiding the reconstruction of the entire signal. We consider a framework where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time, low-power applications.

Motion estimation using point cluster method and Kalman filter.

PubMed

Senesh, M; Wolf, A

2009-05-01

The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal instantaneous frequencies.

A fast and accurate frequency estimation algorithm for sinusoidal signal with harmonic components

NASA Astrophysics Data System (ADS)

Hu, Jinghua; Pan, Mengchun; Zeng, Zhidun; Hu, Jiafei; Chen, Dixiang; Tian, Wugang; Zhao, Jianqiang; Du, Qingfa

2016-10-01

Frequency estimation is a fundamental problem in many applications, such as traditional vibration measurement, power system supervision, and microelectromechanical system sensors control. In this paper, a fast and accurate frequency estimation algorithm is proposed to deal with low efficiency problem in traditional methods. The proposed algorithm consists of coarse and fine frequency estimation steps, and we demonstrate that it is more efficient than conventional searching methods to achieve coarse frequency estimation (location peak of FFT amplitude) by applying modified zero-crossing technique. Thus, the proposed estimation algorithm requires less hardware and software sources and can achieve even higher efficiency when the experimental data increase. Experimental results with modulated magnetic signal show that the root mean square error of frequency estimation is below 0.032 Hz with the proposed algorithm, which has lower computational complexity and better global performance than conventional frequency estimation methods.

Population density estimated from locations of individuals on a passive detector array

USGS Publications Warehouse

Efford, Murray G.; Dawson, Deanna K.; Borchers, David L.

2009-01-01

The density of a closed population of animals occupying stable home ranges may be estimated from detections of individuals on an array of detectors, using newly developed methods for spatially explicit capture–recapture. Likelihood-based methods provide estimates for data from multi-catch traps or from devices that record presence without restricting animal movement ("proximity" detectors such as camera traps and hair snags). As originally proposed, these methods require multiple sampling intervals. We show that equally precise and unbiased estimates may be obtained from a single sampling interval, using only the spatial pattern of detections. This considerably extends the range of possible applications, and we illustrate the potential by estimating density from simulated detections of bird vocalizations on a microphone array. Acoustic detection can be defined as occurring when received signal strength exceeds a threshold. We suggest detection models for binary acoustic data, and for continuous data comprising measurements of all signals above the threshold. While binary data are often sufficient for density estimation, modeling signal strength improves precision when the microphone array is small.

Time delay estimation using new spectral and adaptive filtering methods with applications to underwater target detection

NASA Astrophysics Data System (ADS)

Hasan, Mohammed A.

1997-11-01

In this dissertation, we present several novel approaches for detection and identification of targets of arbitrary shapes from the acoustic backscattered data and using the incident waveform. This problem is formulated as time- delay estimation and sinusoidal frequency estimation problems which both have applications in many other important areas in signal processing. Solving time-delay estimation problem allows the identification of the specular components in the backscattered signal from elastic and non-elastic targets. Thus, accurate estimation of these time delays would help in determining the existence of certain clues for detecting targets. Several new methods for solving these two problems in the time, frequency and wavelet domains are developed. In the time domain, a new block fast transversal filter (BFTF) is proposed for a fast implementation of the least squares (LS) method. This BFTF algorithm is derived by using data-related constrained block-LS cost function to guarantee global optimality. The new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data and thus it is computationally very efficient compared with other LS- based schemes. Additionally, the tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. The effectiveness of this algorithm is tested on several underwater acoustic backscattered data for elastic targets and non-elastic (cement chunk) objects. In the frequency domain, the time-delay estimation problem is converted to a sinusoidal frequency estimation problem by using the discrete Fourier transform. Then, the lagged sample covariance matrices of the resulting signal are computed and studied in terms of their eigen- structure. These matrices are shown to be robust and effective in extracting bases for the signal and noise subspaces. New MUSIC and matrix pencil-based methods are derived these subspaces. The effectiveness of the method is demonstrated on the problem of detection of multiple specular components in the acoustic backscattered data. Finally, a method for the estimation of time delays using wavelet decomposition is derived. The sub-band adaptive filtering uses discrete wavelet transform for multi- resolution or sub-band decomposition. Joint time delay estimation for identifying multi-specular components and subsequent adaptive filtering processes are performed on the signal in each sub-band. This would provide multiple 'look' of the signal at different resolution scale which results in more accurate estimates for delays associated with the specular components. Simulation results on the simulated and real shallow water data are provided which show the promise of this new scheme for target detection in a heavy cluttered environment.

Estimation of neural energy in microelectrode signals

NASA Astrophysics Data System (ADS)

Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

2004-09-01

We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

Calculation of the time resolution of the J-PET tomograph using kernel density estimation

NASA Astrophysics Data System (ADS)

Raczyński, L.; Wiślicki, W.; Krzemień, W.; Kowalski, P.; Alfs, D.; Bednarski, T.; Białas, P.; Curceanu, C.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gorgol, M.; Hiesmayr, B.; Jasińska, B.; Kamińska, D.; Korcyl, G.; Kozik, T.; Krawczyk, N.; Kubicz, E.; Mohammed, M.; Pawlik-Niedźwiecka, M.; Niedźwiecki, S.; Pałka, M.; Rudy, Z.; Rundel, O.; Sharma, N. G.; Silarski, M.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Zgardzińska, B.; Zieliński, M.; Moskal, P.

2017-06-01

In this paper we estimate the time resolution of the J-PET scanner built from plastic scintillators. We incorporate the method of signal processing using the Tikhonov regularization framework and the kernel density estimation method. We obtain simple, closed-form analytical formulae for time resolution. The proposed method is validated using signals registered by means of the single detection unit of the J-PET tomograph built from a 30 cm long plastic scintillator strip. It is shown that the experimental and theoretical results obtained for the J-PET scanner equipped with vacuum tube photomultipliers are consistent.

Fast Noncircular 2D-DOA Estimation for Rectangular Planar Array

PubMed Central

Xu, Lingyun; Wen, Fangqing

2017-01-01

A novel scheme is proposed for direction finding with uniform rectangular planar array. First, the characteristics of noncircular signals and Euler’s formula are exploited to construct a new real-valued rectangular array data. Then, the rotational invariance relations for real-valued signal space are depicted in a new way. Finally the real-valued propagator method is utilized to estimate the pairing two-dimensional direction of arrival (2D-DOA). The proposed algorithm provides better angle estimation performance and can discern more sources than the 2D propagator method. At the same time, it has very close angle estimation performance to the noncircular propagator method (NC-PM) with reduced computational complexity. PMID:28417926

Adaptive Window Zero-Crossing-Based Instantaneous Frequency Estimation

NASA Astrophysics Data System (ADS)

Sekhar, S. Chandra; Sreenivas, TV

2004-12-01

We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).

Gaze Estimation Method Using Analysis of Electrooculogram Signals and Kinect Sensor

PubMed Central

Tanno, Koichi

2017-01-01

A gaze estimation system is one of the communication methods for severely disabled people who cannot perform gestures and speech. We previously developed an eye tracking method using a compact and light electrooculogram (EOG) signal, but its accuracy is not very high. In the present study, we conducted experiments to investigate the EOG component strongly correlated with the change of eye movements. The experiments in this study are of two types: experiments to see objects only by eye movements and experiments to see objects by face and eye movements. The experimental results show the possibility of an eye tracking method using EOG signals and a Kinect sensor. PMID:28912800

A measurement fusion method for nonlinear system identification using a cooperative learning algorithm.

PubMed

Xia, Youshen; Kamel, Mohamed S

2007-06-01

Identification of a general nonlinear noisy system viewed as an estimation of a predictor function is studied in this article. A measurement fusion method for the predictor function estimate is proposed. In the proposed scheme, observed data are first fused by using an optimal fusion technique, and then the optimal fused data are incorporated in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM). A cooperative learning algorithm is proposed to implement the proposed measurement fusion method. Compared with related identification methods, the proposed method can minimize both the approximation error and the noise error. The performance analysis shows that the proposed optimal measurement fusion function estimate has a smaller mean square error than the LS-SVM function estimate. Moreover, the proposed cooperative learning algorithm can converge globally to the optimal measurement fusion function estimate. Finally, the proposed measurement fusion method is applied to ARMA signal and spatial temporal signal modeling. Experimental results show that the proposed measurement fusion method can provide a more accurate model.

The extended Fourier transform for 2D spectral estimation.

PubMed

Armstrong, G S; Mandelshtam, V A

2001-11-01

We present a linear algebraic method, named the eXtended Fourier Transform (XFT), for spectral estimation from truncated time signals. The method is a hybrid of the discrete Fourier transform (DFT) and the regularized resolvent transform (RRT) (J. Chen et al., J. Magn. Reson. 147, 129-137 (2000)). Namely, it estimates the remainder of a finite DFT by RRT. The RRT estimation corresponds to solution of an ill-conditioned problem, which requires regularization. The regularization depends on a parameter, q, that essentially controls the resolution. By varying q from 0 to infinity one can "tune" the spectrum between a high-resolution spectral estimate and the finite DFT. The optimal value of q is chosen according to how well the data fits the form of a sum of complex sinusoids and, in particular, the signal-to-noise ratio. Both 1D and 2D XFT are presented with applications to experimental NMR signals. Copyright 2001 Academic Press.

PULSAR SIGNAL DENOISING METHOD BASED ON LAPLACE DISTRIBUTION IN NO-SUBSAMPLING WAVELET PACKET DOMAIN

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

Wenbo, Wang; Yanchao, Zhao; Xiangli, Wang

2016-11-01

In order to improve the denoising effect of the pulsar signal, a new denoising method is proposed in the no-subsampling wavelet packet domain based on the local Laplace prior model. First, we count the true noise-free pulsar signal’s wavelet packet coefficient distribution characteristics and construct the true signal wavelet packet coefficients’ Laplace probability density function model. Then, we estimate the denosied wavelet packet coefficients by using the noisy pulsar wavelet coefficients based on maximum a posteriori criteria. Finally, we obtain the denoisied pulsar signal through no-subsampling wavelet packet reconstruction of the estimated coefficients. The experimental results show that the proposed method performs better when calculating the pulsar time of arrival than the translation-invariant wavelet denoising method.

Parameter estimation using weighted total least squares in the two-compartment exchange model.

PubMed

Garpebring, Anders; Löfstedt, Tommy

2018-01-01

The linear least squares (LLS) estimator provides a fast approach to parameter estimation in the linearized two-compartment exchange model. However, the LLS method may introduce a bias through correlated noise in the system matrix of the model. The purpose of this work is to present a new estimator for the linearized two-compartment exchange model that takes this noise into account. To account for the noise in the system matrix, we developed an estimator based on the weighted total least squares (WTLS) method. Using simulations, the proposed WTLS estimator was compared, in terms of accuracy and precision, to an LLS estimator and a nonlinear least squares (NLLS) estimator. The WTLS method improved the accuracy compared to the LLS method to levels comparable to the NLLS method. This improvement was at the expense of increased computational time; however, the WTLS was still faster than the NLLS method. At high signal-to-noise ratio all methods provided similar precisions while inconclusive results were observed at low signal-to-noise ratio. The proposed method provides improvements in accuracy compared to the LLS method, however, at an increased computational cost. Magn Reson Med 79:561-567, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Automated delay estimation at signalized intersections : phase I concept and algorithm development.

DOT National Transportation Integrated Search

2011-07-01

Currently there are several methods to measure the performance of surface streets, but their capabilities in dynamically estimating vehicle delay are limited. The objective of this research is to develop a method to automate traffic delay estimation ...

Support vector machines for nuclear reactor state estimation

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

Zavaljevski, N.; Gross, K. C.

2000-02-14

Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformedmore » into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.« less

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

PubMed

Soares, Fabiano Araujo; Carvalho, João Luiz Azevedo; Miosso, Cristiano Jacques; de Andrade, Marcelino Monteiro; da Rocha, Adson Ferreira

2015-09-17

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep

PubMed Central

Crainiceanu, Ciprian M.; Caffo, Brian S.; Di, Chong-Zhi; Punjabi, Naresh M.

2009-01-01

We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS. PMID:20057925

An improved method based on wavelet coefficient correlation to filter noise in Doppler ultrasound blood flow signals

NASA Astrophysics Data System (ADS)

Wan, Renzhi; Zu, Yunxiao; Shao, Lin

2018-04-01

The blood echo signal maintained through Medical ultrasound Doppler devices would always include vascular wall pulsation signal .The traditional method to de-noise wall signal is using high-pass filter, which will also remove the lowfrequency part of the blood flow signal. Some scholars put forward a method based on region selective reduction, which at first estimates of the wall pulsation signals and then removes the wall signal from the mixed signal. Apparently, this method uses the correlation between wavelet coefficients to distinguish blood signal from wall signal, but in fact it is a kind of wavelet threshold de-noising method, whose effect is not so much ideal. In order to maintain a better effect, this paper proposes an improved method based on wavelet coefficient correlation to separate blood signal and wall signal, and simulates the algorithm by computer to verify its validity.

Scanning linear estimation: improvements over region of interest (ROI) methods

NASA Astrophysics Data System (ADS)

Kupinski, Meredith K.; Clarkson, Eric W.; Barrett, Harrison H.

2013-03-01

In tomographic medical imaging, a signal activity is typically estimated by summing voxels from a reconstructed image. We introduce an alternative estimation scheme that operates on the raw projection data and offers a substantial improvement, as measured by the ensemble mean-square error (EMSE), when compared to using voxel values from a maximum-likelihood expectation-maximization (MLEM) reconstruction. The scanning-linear (SL) estimator operates on the raw projection data and is derived as a special case of maximum-likelihood estimation with a series of approximations to make the calculation tractable. The approximated likelihood accounts for background randomness, measurement noise and variability in the parameters to be estimated. When signal size and location are known, the SL estimate of signal activity is unbiased, i.e. the average estimate equals the true value. By contrast, unpredictable bias arising from the null functions of the imaging system affect standard algorithms that operate on reconstructed data. The SL method is demonstrated for two different tasks: (1) simultaneously estimating a signal’s size, location and activity; (2) for a fixed signal size and location, estimating activity. Noisy projection data are realistically simulated using measured calibration data from the multi-module multi-resolution small-animal SPECT imaging system. For both tasks, the same set of images is reconstructed using the MLEM algorithm (80 iterations), and the average and maximum values within the region of interest (ROI) are calculated for comparison. This comparison shows dramatic improvements in EMSE for the SL estimates. To show that the bias in ROI estimates affects not only absolute values but also relative differences, such as those used to monitor the response to therapy, the activity estimation task is repeated for three different signal sizes.

Signal-to-noise ratio estimation in digital computer simulation of lowpass and bandpass systems with applications to analog and digital communications, volume 3

NASA Technical Reports Server (NTRS)

Tranter, W. H.; Turner, M. D.

1977-01-01

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation.

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method for monitoring an apparatus or process asset including creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto; acquiring a set of observed signal data values from the asset; determining an operating mode of the asset for the set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a set of estimated signal data values from the selected process submodel for the determined operating mode; and determining asset status as a function of the calculated set of estimated signal data values for providing asset surveillance and/or control.

Multi-ball and one-ball geolocation and location verification

NASA Astrophysics Data System (ADS)

Nelson, D. J.; Townsend, J. L.

2017-05-01

We present analysis methods that may be used to geolocate emitters using one or more moving receivers. While some of the methods we present may apply to a broader class of signals, our primary interest is locating and tracking ships from short pulsed transmissions, such as the maritime Automatic Identification System (AIS.) The AIS signal is difficult to process and track since the pulse duration is only 25 milliseconds, and the pulses may only be transmitted every six to ten seconds. Several fundamental problems are addressed, including demodulation of AIS/GMSK signals, verification of the emitter location, accurate frequency and delay estimation and identification of pulse trains from the same emitter. In particular, we present several new correlation methods, including cross-cross correlation that greatly improves correlation accuracy over conventional methods and cross- TDOA and cross-FDOA functions that make it possible to estimate time and frequency delay without the need of computing a two dimensional cross-ambiguity surface. By isolating pulses from the same emitter and accurately tracking the received signal frequency, we are able to accurately estimate the emitter location from the received Doppler characteristics.

An LFMCW detector with new structure and FRFT based differential distance estimation method.

PubMed

Yue, Kai; Hao, Xinhong; Li, Ping

2016-01-01

This paper describes a linear frequency modulated continuous wave (LFMCW) detector which is designed for a collision avoidance radar. This detector can estimate distance between the detector and pedestrians or vehicles, thereby it will help to reduce the likelihood of traffic accidents. The detector consists of a transceiver and a signal processor. A novel structure based on the intermediate frequency signal (IFS) is designed for the transceiver which is different from the traditional LFMCW transceiver using the beat frequency signal (BFS) based structure. In the signal processor, a novel fractional Fourier transform (FRFT) based differential distance estimation (DDE) method is used to detect the distance. The new IFS based structure is beneficial for the FRFT based DDE method to reduce the computation complexity, because it does not need the scan of the optimal FRFT order. Low computation complexity ensures the feasibility of practical applications. Simulations are carried out and results demonstrate the efficiency of the detector designed in this paper.

Improved methods for signal processing in measurements of mercury by Tekran® 2537A and 2537B instruments

NASA Astrophysics Data System (ADS)

Ambrose, Jesse L.

2017-12-01

Atmospheric Hg measurements are commonly carried out using Tekran® Instruments Corporation's model 2537 Hg vapor analyzers, which employ gold amalgamation preconcentration sampling and detection by thermal desorption (TD) and atomic fluorescence spectrometry (AFS). A generally overlooked and poorly characterized source of analytical uncertainty in those measurements is the method by which the raw Hg atomic fluorescence (AF) signal is processed. Here I describe new software-based methods for processing the raw signal from the Tekran® 2537 instruments, and I evaluate the performances of those methods together with the standard Tekran® internal signal processing method. For test datasets from two Tekran® instruments (one 2537A and one 2537B), I estimate that signal processing uncertainties in Hg loadings determined with the Tekran® method are within ±[1 % + 1.2 pg] and ±[6 % + 0.21 pg], respectively. I demonstrate that the Tekran® method can produce significant low biases (≥ 5 %) not only at low Hg sample loadings (< 5 pg) but also at tropospheric background concentrations of gaseous elemental mercury (GEM) and total mercury (THg) (˜ 1 to 2 ng m-3) under typical operating conditions (sample loadings of 5-10 pg). Signal processing uncertainties associated with the Tekran® method can therefore represent a significant unaccounted for addition to the overall ˜ 10 to 15 % uncertainty previously estimated for Tekran®-based GEM and THg measurements. Signal processing bias can also add significantly to uncertainties in Tekran®-based gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurements, which often derive from Hg sample loadings < 5 pg. In comparison, estimated signal processing uncertainties associated with the new methods described herein are low, ranging from within ±0.053 pg, when the Hg thermal desorption peaks are defined manually, to within ±[2 % + 0.080 pg] when peak definition is automated. Mercury limits of detection (LODs) decrease by 31 to 88 % when the new methods are used in place of the Tekran® method. I recommend that signal processing uncertainties be quantified in future applications of the Tekran® 2537 instruments.

Feature-Based Correlation and Topological Similarity for Interbeat Interval Estimation Using Ultrawideband Radar.

PubMed

Sakamoto, Takuya; Imasaka, Ryohei; Taki, Hirofumi; Sato, Toru; Yoshioka, Mototaka; Inoue, Kenichi; Fukuda, Takeshi; Sakai, Hiroyuki

2016-04-01

The objectives of this paper are to propose a method that can accurately estimate the human heart rate (HR) using an ultrawideband (UWB) radar system, and to determine the performance of the proposed method through measurements. The proposed method uses the feature points of a radar signal to estimate the HR efficiently and accurately. Fourier- and periodicity-based methods are inappropriate for estimation of instantaneous HRs in real time because heartbeat waveforms are highly variable, even within the beat-to-beat interval. We define six radar waveform features that enable correlation processing to be performed quickly and accurately. In addition, we propose a feature topology signal that is generated from a feature sequence without using amplitude information. This feature topology signal is used to find unreliable feature points, and thus, to suppress inaccurate HR estimates. Measurements were taken using UWB radar, while simultaneously performing electrocardiography measurements in an experiment that was conducted on nine participants. The proposed method achieved an average root-mean-square error in the interbeat interval of 7.17 ms for the nine participants. The results demonstrate the effectiveness and accuracy of the proposed method. The significance of this study for biomedical research is that the proposed method will be useful in the realization of a remote vital signs monitoring system that enables accurate estimation of HR variability, which has been used in various clinical settings for the treatment of conditions such as diabetes and arterial hypertension.

Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal.

PubMed

Terwilliger, Thomas C; Bunkóczi, Gábor; Hung, Li Wei; Zwart, Peter H; Smith, Janet L; Akey, David L; Adams, Paul D

2016-03-01

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment are described. A simple theoretical framework [Terwilliger et al. (2016), Acta Cryst. D72, 346-358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. The phenix.plan_sad_experiment tool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimate the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. The phenix.scale_and_merge tool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and the phenix.anomalous_signal tool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.

Separation of fNIRS Signals into Functional and Systemic Components Based on Differences in Hemodynamic Modalities

PubMed Central

Yamada, Toru; Umeyama, Shinji; Matsuda, Keiji

2012-01-01

In conventional functional near-infrared spectroscopy (fNIRS), systemic physiological fluctuations evoked by a body's motion and psychophysiological changes often contaminate fNIRS signals. We propose a novel method for separating functional and systemic signals based on their hemodynamic differences. Considering their physiological origins, we assumed a negative and positive linear relationship between oxy- and deoxyhemoglobin changes of functional and systemic signals, respectively. Their coefficients are determined by an empirical procedure. The proposed method was compared to conventional and multi-distance NIRS. The results were as follows: (1) Nonfunctional tasks evoked substantial oxyhemoglobin changes, and comparatively smaller deoxyhemoglobin changes, in the same direction by conventional NIRS. The systemic components estimated by the proposed method were similar to the above finding. The estimated functional components were very small. (2) During finger-tapping tasks, laterality in the functional component was more distinctive using our proposed method than that by conventional fNIRS. The systemic component indicated task-evoked changes, regardless of the finger used to perform the task. (3) For all tasks, the functional components were highly coincident with signals estimated by multi-distance NIRS. These results strongly suggest that the functional component obtained by the proposed method originates in the cerebral cortical layer. We believe that the proposed method could improve the reliability of fNIRS measurements without any modification in commercially available instruments. PMID:23185590

A Full Dynamic Compound Inverse Method for output-only element-level system identification and input estimation from earthquake response signals

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Rizzi, Egidio

2016-08-01

This paper proposes a new output-only element-level system identification and input estimation technique, towards the simultaneous identification of modal parameters, input excitation time history and structural features at the element-level by adopting earthquake-induced structural response signals. The method, named Full Dynamic Compound Inverse Method (FDCIM), releases strong assumptions of earlier element-level techniques, by working with a two-stage iterative algorithm. Jointly, a Statistical Average technique, a modification process and a parameter projection strategy are adopted at each stage to achieve stronger convergence for the identified estimates. The proposed method works in a deterministic way and is completely developed in State-Space form. Further, it does not require continuous- to discrete-time transformations and does not depend on initialization conditions. Synthetic earthquake-induced response signals from different shear-type buildings are generated to validate the implemented procedure, also with noise-corrupted cases. The achieved results provide a necessary condition to demonstrate the effectiveness of the proposed identification method.

DOA Estimation for Underwater Wideband Weak Targets Based on Coherent Signal Subspace and Compressed Sensing

PubMed Central

2018-01-01

Direction of arrival (DOA) estimation is the basis for underwater target localization and tracking using towed line array sonar devices. A method of DOA estimation for underwater wideband weak targets based on coherent signal subspace (CSS) processing and compressed sensing (CS) theory is proposed. Under the CSS processing framework, wideband frequency focusing is accompanied by a two-sided correlation transformation, allowing the DOA of underwater wideband targets to be estimated based on the spatial sparsity of the targets and the compressed sensing reconstruction algorithm. Through analysis and processing of simulation data and marine trial data, it is shown that this method can accomplish the DOA estimation of underwater wideband weak targets. Results also show that this method can considerably improve the spatial spectrum of weak target signals, enhancing the ability to detect them. It can solve the problems of low directional resolution and unreliable weak-target detection in traditional beamforming technology. Compared with the conventional minimum variance distortionless response beamformers (MVDR), this method has many advantages, such as higher directional resolution, wider detection range, fewer required snapshots and more accurate detection for weak targets. PMID:29562642

A comparison of methods to estimate seismic phase delays--Numerical examples for coda wave interferometry

USGS Publications Warehouse

Mikesell, T. Dylan; Malcolm, Alison E.; Yang, Di; Haney, Matthew M.

2015-01-01

Time-shift estimation between arrivals in two seismic traces before and after a velocity perturbation is a crucial step in many seismic methods. The accuracy of the estimated velocity perturbation location and amplitude depend on this time shift. Windowed cross correlation and trace stretching are two techniques commonly used to estimate local time shifts in seismic signals. In the work presented here, we implement Dynamic Time Warping (DTW) to estimate the warping function – a vector of local time shifts that globally minimizes the misfit between two seismic traces. We illustrate the differences of all three methods compared to one another using acoustic numerical experiments. We show that DTW is comparable to or better than the other two methods when the velocity perturbation is homogeneous and the signal-to-noise ratio is high. When the signal-to-noise ratio is low, we find that DTW and windowed cross correlation are more accurate than the stretching method. Finally, we show that the DTW algorithm has better time resolution when identifying small differences in the seismic traces for a model with an isolated velocity perturbation. These results impact current methods that utilize not only time shifts between (multiply) scattered waves, but also amplitude and decoherence measurements. DTW is a new tool that may find new applications in seismology and other geophysical methods (e.g., as a waveform inversion misfit function).

Estimation of chirp rates of music-adapted prolate spheroidal atoms using reassignment

NASA Astrophysics Data System (ADS)

Mesz, Bruno; Serrano, Eduardo

2007-09-01

We introduce a modified Matching Pursuit algorithm for estimating frequency and frequency slope of FM-modulated music signals. The use of Matching Pursuit with constant frequency atoms provides coarse estimates which could be improved with chirped atoms, more suited in principle to this kind of signals. Application of the reassignment method is suggested by its good localization properties for chirps. We start considering a family of atoms generated by modulation and scaling of a prolate spheroidal wave function. These functions are concentrated in frequency on intervals of a semitone centered at the frequencies of the well-tempered scale. At each stage of the pursuit, we search the atom most correlated with the signal. We then consider the spectral peaks at each frame of the spectrogram and calculate a modified frequency and frequency slope using the derivatives of the reassignment operators; this is then used to estimate the parameters of a cubic interpolation polynomial that models local pitch fluctuations. We apply the method both to synthetic and music signals.

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array

PubMed Central

Gao, Yu-Fei; Gui, Guan; Xie, Wei; Zou, Yan-Bin; Yang, Yue; Wan, Qun

2017-01-01

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank-(L1,L2,·) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method. PMID:28448431

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array.

PubMed

Gao, Yu-Fei; Gui, Guan; Xie, Wei; Zou, Yan-Bin; Yang, Yue; Wan, Qun

2017-04-27

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · ) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method.

A method searching for optimum fractional order and its application in self-phase modulation induced nonlinear phase noise estimation in coherent optical fiber transmission systems

NASA Astrophysics Data System (ADS)

Huang, Chuan; Guo, Peng; Yang, Aiying; Qiao, Yaojun

2018-07-01

In single channel systems, the nonlinear phase noise only comes from the channel itself through self-phase modulation (SPM). In this paper, a fast-nonlinear effect estimation method is proposed based on fractional Fourier transformation (FrFT). The nonlinear phase noise caused by Self-phase modulation effect is accurately estimated for single model 10Gbaud OOK and RZ-QPSK signals with the fiber length range of 0-200 km and the launch power range of 1-10 mW. The pulse windowing is adopted to search the optimum fractional order for the OOK and RZ-QPSK signals. Since the nonlinear phase shift caused by the SPM effect is very small, the accurate optimum fractional order of the signal cannot be found based on the traditional method. In this paper, a new method magnifying the phase shift is proposed to get the accurate optimum order and thus the nonlinear phase shift is calculated. The simulation results agree with the theoretical analysis and the method is applicable to signals whose pulse type has the similar characteristics with Gaussian pulse.

An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

NASA Astrophysics Data System (ADS)

Chang, Yong; Zi, Yanyang; Zhao, Jiyuan; Yang, Zhe; He, Wangpeng; Sun, Hailiang

2017-03-01

In guided wave pipeline inspection, echoes reflected from closely spaced reflectors generally overlap, meaning useful information is lost. To solve the overlapping problem, sparse deconvolution methods have been developed in the past decade. However, conventional sparse deconvolution methods have limitations in handling guided wave signals, because the input signal is directly used as the prototype of the convolution matrix, without considering the waveform change caused by the dispersion properties of the guided wave. In this paper, an adaptive sparse deconvolution (ASD) method is proposed to overcome these limitations. First, the Gaussian echo model is employed to adaptively estimate the column prototype of the convolution matrix instead of directly using the input signal as the prototype. Then, the convolution matrix is constructed upon the estimated results. Third, the split augmented Lagrangian shrinkage (SALSA) algorithm is introduced to solve the deconvolution problem with high computational efficiency. To verify the effectiveness of the proposed method, guided wave signals obtained from pipeline inspection are investigated numerically and experimentally. Compared to conventional sparse deconvolution methods, e.g. the {{l}1} -norm deconvolution method, the proposed method shows better performance in handling the echo overlap problem in the guided wave signal.

A technique for estimating the absolute gain of a photomultiplier tube

NASA Astrophysics Data System (ADS)

Takahashi, M.; Inome, Y.; Yoshii, S.; Bamba, A.; Gunji, S.; Hadasch, D.; Hayashida, M.; Katagiri, H.; Konno, Y.; Kubo, H.; Kushida, J.; Nakajima, D.; Nakamori, T.; Nagayoshi, T.; Nishijima, K.; Nozaki, S.; Mazin, D.; Mashuda, S.; Mirzoyan, R.; Ohoka, H.; Orito, R.; Saito, T.; Sakurai, S.; Takeda, J.; Teshima, M.; Terada, Y.; Tokanai, F.; Yamamoto, T.; Yoshida, T.

2018-06-01

Detection of low-intensity light relies on the conversion of photons to photoelectrons, which are then multiplied and detected as an electrical signal. To measure the actual intensity of the light, one must know the factor by which the photoelectrons have been multiplied. To obtain this amplification factor, we have developed a procedure for estimating precisely the signal caused by a single photoelectron. The method utilizes the fact that the photoelectrons conform to a Poisson distribution. The average signal produced by a single photoelectron can then be estimated from the number of noise events, without requiring analysis of the distribution of the signal produced by a single photoelectron. The signal produced by one or more photoelectrons can be estimated experimentally without any assumptions. This technique, and an example of the analysis of a signal from a photomultiplier tube, are described in this study.

Estimating the signal-to-noise ratio of AVIRIS data

NASA Technical Reports Server (NTRS)

Curran, Paul J.; Dungan, Jennifer L.

1988-01-01

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.

Development of gait segmentation methods for wearable foot pressure sensors.

PubMed

Crea, S; De Rossi, S M M; Donati, M; Reberšek, P; Novak, D; Vitiello, N; Lenzi, T; Podobnik, J; Munih, M; Carrozza, M C

2012-01-01

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

Signal inference with unknown response: calibration-uncertainty renormalized estimator.

PubMed

Dorn, Sebastian; Enßlin, Torsten A; Greiner, Maksim; Selig, Marco; Boehm, Vanessa

2015-01-01

The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration-uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure. The idea of the CURE method, developed in the framework of information field theory, is to start with an assumed calibration to successively include more and more portions of calibration uncertainty into the signal inference equations and to absorb the resulting corrections into renormalized signal (and calibration) solutions. Thereby, the signal inference and calibration problem turns into a problem of solving a single system of ordinary differential equations and can be identified with common resummation techniques used in field theories. We verify the CURE method by applying it to a simplistic toy example and compare it against existent self-calibration schemes, Wiener filter solutions, and Markov chain Monte Carlo sampling. We conclude that the method is able to keep up in accuracy with the best self-calibration methods and serves as a noniterative alternative to them.

Hardware design and implementation of fast DOA estimation method based on multicore DSP

NASA Astrophysics Data System (ADS)

Guo, Rui; Zhao, Yingxiao; Zhang, Yue; Lin, Qianqiang; Chen, Zengping

2016-10-01

In this paper, we present a high-speed real-time signal processing hardware platform based on multicore digital signal processor (DSP). The real-time signal processing platform shows several excellent characteristics including high performance computing, low power consumption, large-capacity data storage and high speed data transmission, which make it able to meet the constraint of real-time direction of arrival (DOA) estimation. To reduce the high computational complexity of DOA estimation algorithm, a novel real-valued MUSIC estimator is used. The algorithm is decomposed into several independent steps and the time consumption of each step is counted. Based on the statistics of the time consumption, we present a new parallel processing strategy to distribute the task of DOA estimation to different cores of the real-time signal processing hardware platform. Experimental results demonstrate that the high processing capability of the signal processing platform meets the constraint of real-time direction of arrival (DOA) estimation.

Characterization of turbulence stability through the identification of multifractional Brownian motions

NASA Astrophysics Data System (ADS)

Lee, K. C.

2013-02-01

Multifractional Brownian motions have become popular as flexible models in describing real-life signals of high-frequency features in geoscience, microeconomics, and turbulence, to name a few. The time-changing Hurst exponent, which describes regularity levels depending on time measurements, and variance, which relates to an energy level, are two parameters that characterize multifractional Brownian motions. This research suggests a combined method of estimating the time-changing Hurst exponent and variance using the local variation of sampled paths of signals. The method consists of two phases: initially estimating global variance and then accurately estimating the time-changing Hurst exponent. A simulation study shows its performance in estimation of the parameters. The proposed method is applied to characterization of atmospheric stability in which descriptive statistics from the estimated time-changing Hurst exponent and variance classify stable atmosphere flows from unstable ones.

Fusion of magnetometer and gradiometer sensors of MEG in the presence of multiplicative error.

PubMed

Mohseni, Hamid R; Woolrich, Mark W; Kringelbach, Morten L; Luckhoo, Henry; Smith, Penny Probert; Aziz, Tipu Z

2012-07-01

Novel neuroimaging techniques have provided unprecedented information on the structure and function of the living human brain. Multimodal fusion of data from different sensors promises to radically improve this understanding, yet optimal methods have not been developed. Here, we demonstrate a novel method for combining multichannel signals. We show how this method can be used to fuse signals from the magnetometer and gradiometer sensors used in magnetoencephalography (MEG), and through extensive experiments using simulation, head phantom and real MEG data, show that it is both robust and accurate. This new approach works by assuming that the lead fields have multiplicative error. The criterion to estimate the error is given within a spatial filter framework such that the estimated power is minimized in the worst case scenario. The method is compared to, and found better than, existing approaches. The closed-form solution and the conditions under which the multiplicative error can be optimally estimated are provided. This novel approach can also be employed for multimodal fusion of other multichannel signals such as MEG and EEG. Although the multiplicative error is estimated based on beamforming, other methods for source analysis can equally be used after the lead-field modification.

Surveillance system and method having parameter estimation and operating mode partitioning

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2003-01-01

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

Method and system of doppler correction for mobile communications systems

NASA Technical Reports Server (NTRS)

Georghiades, Costas N. (Inventor); Spasojevic, Predrag (Inventor)

1999-01-01

Doppler correction system and method comprising receiving a Doppler effected signal comprising a preamble signal (32). A delayed preamble signal (48) may be generated based on the preamble signal (32). The preamble signal (32) may be multiplied by the delayed preamble signal (48) to generate an in-phase preamble signal (60). The in-phase preamble signal (60) may be filtered to generate a substantially constant in-phase preamble signal (62). A plurality of samples of the substantially constant in-phase preamble signal (62) may be accumulated. A phase-shifted signal (76) may also be generated based on the preamble signal (32). The phase-shifted signal (76) may be multiplied by the delayed preamble signal (48) to generate an out-of-phase preamble signal (80). The out-of-phase preamble signal (80) may be filtered to generate a substantially constant out-of-phase preamble signal (82). A plurality of samples of the substantially constant out-of-phase signal (82) may be accumulated. A sum of the in-phase preamble samples and a sum of the out-of-phase preamble samples may be normalized relative to each other to generate an in-phase Doppler estimator (92) and an out-of-phase Doppler estimator (94).

Extracting harmonic signal from a chaotic background with local linear model

NASA Astrophysics Data System (ADS)

Li, Chenlong; Su, Liyun

2017-02-01

In this paper, the problems of blind detection and estimation of harmonic signal in strong chaotic background are analyzed, and new methods by using local linear (LL) model are put forward. The LL model has been exhaustively researched and successfully applied for fitting and forecasting chaotic signal in many chaotic fields. We enlarge the modeling capacity substantially. Firstly, we can predict the short-term chaotic signal and obtain the fitting error based on the LL model. Then we detect the frequencies from the fitting error by periodogram, a property on the fitting error is proposed which has not been addressed before, and this property ensures that the detected frequencies are similar to that of harmonic signal. Secondly, we establish a two-layer LL model to estimate the determinate harmonic signal in strong chaotic background. To estimate this simply and effectively, we develop an efficient backfitting algorithm to select and optimize the parameters that are hard to be exhaustively searched for. In the method, based on sensitivity to initial value of chaos motion, the minimum fitting error criterion is used as the objective function to get the estimation of the parameters of the two-layer LL model. Simulation shows that the two-layer LL model and its estimation technique have appreciable flexibility to model the determinate harmonic signal in different chaotic backgrounds (Lorenz, Henon and Mackey-Glass (M-G) equations). Specifically, the harmonic signal can be extracted well with low SNR and the developed background algorithm satisfies the condition of convergence in repeated 3-5 times.

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

PubMed Central

Hidalgo, Marta R.; Cubuk, Cankut; Amadoz, Alicia; Salavert, Francisco; Carbonell-Caballero, José; Dopazo, Joaquin

2017-01-01

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions. PMID:28042959

Model-based spectral estimation of Doppler signals using parallel genetic algorithms.

PubMed

Solano González, J; Rodríguez Vázquez, K; García Nocetti, D F

2000-05-01

Conventional spectral analysis methods use a fast Fourier transform (FFT) on consecutive or overlapping windowed data segments. For Doppler ultrasound signals, this approach suffers from an inadequate frequency resolution due to the time segment duration and the non-stationarity characteristics of the signals. Parametric or model-based estimators can give significant improvements in the time-frequency resolution at the expense of a higher computational complexity. This work describes an approach which implements in real-time a parametric spectral estimator method using genetic algorithms (GAs) in order to find the optimum set of parameters for the adaptive filter that minimises the error function. The aim is to reduce the computational complexity of the conventional algorithm by using the simplicity associated to GAs and exploiting its parallel characteristics. This will allow the implementation of higher order filters, increasing the spectrum resolution, and opening a greater scope for using more complex methods.

Estimating weak ratiometric signals in imaging data. II. Meta-analysis with multiple, dual-channel datasets.

PubMed

Sornborger, Andrew; Broder, Josef; Majumder, Anirban; Srinivasamoorthy, Ganesh; Porter, Erika; Reagin, Sean S; Keith, Charles; Lauderdale, James D

2008-09-01

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments.

Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2018-01-01

The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.

Time-frequency peak filtering for random noise attenuation of magnetic resonance sounding signal

NASA Astrophysics Data System (ADS)

Lin, Tingting; Zhang, Yang; Yi, Xiaofeng; Fan, Tiehu; Wan, Ling

2018-05-01

When measuring in a geomagnetic field, the method of magnetic resonance sounding (MRS) is often limited because of the notably low signal-to-noise ratio (SNR). Most current studies focus on discarding spiky noise and power-line harmonic noise cancellation. However, the effects of random noise should not be underestimated. The common method for random noise attenuation is stacking, but collecting multiple recordings merely to suppress random noise is time-consuming. Moreover, stacking is insufficient to suppress high-level random noise. Here, we propose the use of time-frequency peak filtering for random noise attenuation, which is performed after the traditional de-spiking and power-line harmonic removal method. By encoding the noisy signal with frequency modulation and estimating the instantaneous frequency using the peak of the time-frequency representation of the encoded signal, the desired MRS signal can be acquired from only one stack. The performance of the proposed method is tested on synthetic envelope signals and field data from different surveys. Good estimations of the signal parameters are obtained at different SNRs. Moreover, an attempt to use the proposed method to handle a single recording provides better results compared to 16 stacks. Our results suggest that the number of stacks can be appropriately reduced to shorten the measurement time and improve the measurement efficiency.

Estimating Fast Neural Input Using Anatomical and Functional Connectivity

PubMed Central

Eriksson, David

2016-01-01

In the last 20 years there has been an increased interest in estimating signals that are sent between neurons and brain areas. During this time many new methods have appeared for measuring those signals. Here we review a wide range of methods for which connected neurons can be identified anatomically, by tracing axons that run between the cells, or functionally, by detecting if the activity of two neurons are correlated with a short lag. The signals that are sent between the neurons are represented by the activity in the neurons that are connected to the target population or by the activity at the corresponding synapses. The different methods not only differ in the accuracy of the signal measurement but they also differ in the type of signal being measured. For example, unselective recording of all neurons in the source population encompasses more indirect pathways to the target population than if one selectively record from the neurons that project to the target population. Infact, this degree of selectivity is similar to that of optogenetic perturbations; one can perturb selectively or unselectively. Thus it becomes possible to match a given signal measurement method with a signal perturbation method, something that allows for an exact input control to any neuronal population. PMID:28066189

Estimation of multiple accelerated motions using chirp-Fourier transform and clustering.

PubMed

Alexiadis, Dimitrios S; Sergiadis, George D

2007-01-01

Motion estimation in the spatiotemporal domain has been extensively studied and many methodologies have been proposed, which, however, cannot handle both time-varying and multiple motions. Extending previously published ideas, we present an efficient method for estimating multiple, linearly time-varying motions. It is shown that the estimation of accelerated motions is equivalent to the parameter estimation of superpositioned chirp signals. From this viewpoint, one can exploit established signal processing tools such as the chirp-Fourier transform. It is shown that accelerated motion results in energy concentration along planes in the 4-D space: spatial frequencies-temporal frequency-chirp rate. Using fuzzy c-planes clustering, we estimate the plane/motion parameters. The effectiveness of our method is verified on both synthetic as well as real sequences and its advantages are highlighted.

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas

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

Washeleski, Robert L.; Meyer, Edmond J. IV; King, Lyon B.

2013-10-15

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. Themore » key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.« less

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas.

PubMed

Washeleski, Robert L; Meyer, Edmond J; King, Lyon B

2013-10-01

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. The key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.

Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

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

Samad, Nassim A.; Kim, Youngki; Siegel, Jason B.

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based onmeasured force (ICF) instead of voltage (ICV). The force ismeasured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replacemore » the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.« less

Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis

DOE PAGES

Samad, Nassim A.; Kim, Youngki; Siegel, Jason B.; ...

2016-05-27

Traditionally health monitoring techniques in lithium-ion batteries rely on voltage and current measurements. A novel method of using a mechanical rather than electrical signal in the incremental capacity analysis (ICA) method is introduced in this paper. This method derives the incremental capacity curves based onmeasured force (ICF) instead of voltage (ICV). The force ismeasured on the surface of a cell under compression in a fixture that replicates a battery pack assembly and preloading. The analysis is performed on data collected from cycling encased prismatic Lithium-ion Nickel-Manganese-Cobalt Oxide (NMC) cells. For the NMC chemistry, the ICF method can complement or replacemore » the ICV method for the following reasons. The identified ICV peaks are centered around 40% of state of charge (SOC) while the peaks of the ICF method are centered around 70% of SOC indicating that the ICF can be used more often because it is more likely that an electric vehicle (EV) or a plug-in hybrid electric vehicle (PHEV) will traverse the 70% SOC range than the 40% SOC. In addition the Signal to Noise ratio (SNR) of the force signal is four times larger than the voltage signal using laboratory grade sensors. The proposed ICF method is shown to achieve 0.42% accuracy in capacity estimation during a low C-rate constant current discharge. Future work will investigate the application of the capacity estimation technique under charging and operation under high C-rates by addressing the transient behavior of force so that an online methodology for capacity estimation is developed.« less

A Novel DFT-Based DOA Estimation by a Virtual Array Extension Using Simple Multiplications for FMCW Radar

PubMed Central

Kim, Bongseok; Kim, Sangdong; Lee, Jonghun

2018-01-01

We propose a novel discrete Fourier transform (DFT)-based direction of arrival (DOA) estimation by a virtual array extension using simple multiplications for frequency modulated continuous wave (FMCW) radar. DFT-based DOA estimation is usually employed in radar systems because it provides the advantage of low complexity for real-time signal processing. In order to enhance the resolution of DOA estimation or to decrease the missing detection probability, it is essential to have a considerable number of channel signals. However, due to constraints of space and cost, it is not easy to increase the number of channel signals. In order to address this issue, we increase the number of effective channel signals by generating virtual channel signals using simple multiplications of the given channel signals. The increase in channel signals allows the proposed scheme to detect DOA more accurately than the conventional scheme while using the same number of channel signals. Simulation results show that the proposed scheme achieves improved DOA estimation compared to the conventional DFT-based method. Furthermore, the effectiveness of the proposed scheme in a practical environment is verified through the experiment. PMID:29758016

Real Time Calibration Method for Signal Conditioning Amplifiers

NASA Technical Reports Server (NTRS)

Medelius, Pedro J. (Inventor); Mata, Carlos T. (Inventor); Eckhoff, Anthony (Inventor); Perotti, Jose (Inventor); Lucena, Angel (Inventor)

2004-01-01

A signal conditioning amplifier receives an input signal from an input such as a transducer. The signal is amplified and processed through an analog to digital converter and sent to a processor. The processor estimates the input signal provided by the transducer to the amplifier via a multiplexer. The estimated input signal is provided as a calibration voltage to the amplifier immediately following the receipt of the amplified input signal. The calibration voltage is amplified by the amplifier and provided to the processor as an amplified calibration voltage. The amplified calibration voltage is compared to the amplified input signal, and if a significant error exists, the gain and/or offset of the amplifier may be adjusted as necessary.

Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal

PubMed Central

Terwilliger, Thomas C.; Bunkóczi, Gábor; Hung, Li-Wei; Zwart, Peter H.; Smith, Janet L.; Akey, David L.; Adams, Paul D.

2016-01-01

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment are described. A simple theoretical framework [Terwilliger et al. (2016 ▸), Acta Cryst. D72, 346–358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. The phenix.plan_sad_experiment tool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimate the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. The phenix.scale_and_merge tool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and the phenix.anomalous_signal tool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing. PMID:26960123

Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI.

PubMed

Kratochvíla, Jiří; Jiřík, Radovan; Bartoš, Michal; Standara, Michal; Starčuk, Zenon; Taxt, Torfinn

2016-03-01

One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup. © 2015 Wiley Periodicals, Inc.

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

Blaut, Arkadiusz; Babak, Stanislav; Krolak, Andrzej

We present data analysis methods used in the detection and estimation of parameters of gravitational-wave signals from the white dwarf binaries in the mock LISA data challenge. Our main focus is on the analysis of challenge 3.1, where the gravitational-wave signals from more than 6x10{sup 7} Galactic binaries were added to the simulated Gaussian instrumental noise. The majority of the signals at low frequencies are not resolved individually. The confusion between the signals is strongly reduced at frequencies above 5 mHz. Our basic data analysis procedure is the maximum likelihood detection method. We filter the data through the template bankmore » at the first step of the search, then we refine parameters using the Nelder-Mead algorithm, we remove the strongest signal found and we repeat the procedure. We detect reliably and estimate parameters accurately of more than ten thousand signals from white dwarf binaries.« less

Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Shibin; Chen, Xuefeng; Selesnick, Ivan W.; Guo, Yanjie; Tong, Chaowei; Zhang, Xingwu

2018-02-01

Synchrosqueezing transform (SST) can effectively improve the readability of the time-frequency (TF) representation (TFR) of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF). However, for signals composed of multiple components with fast varying IF, SST still suffers from TF blurs. In this paper, we introduce a time-frequency analysis (TFA) method called matching synchrosqueezing transform (MSST) that achieves a highly concentrated TF representation comparable to the standard TF reassignment methods (STFRM), even for signals with fast varying IF, and furthermore, MSST retains the reconstruction benefit of SST. MSST captures the philosophy of STFRM to simultaneously consider time and frequency variables, and incorporates three estimators (i.e., the IF estimator, the group delay estimator, and a chirp-rate estimator) into a comprehensive and accurate IF estimator. In this paper, we first introduce the motivation of MSST with three heuristic examples. Then we introduce a precise mathematical definition of a class of chirp-like intrinsic-mode-type functions that locally can be viewed as a sum of a reasonably small number of approximate chirp signals, and we prove that MSST does indeed succeed in estimating chirp-rate and IF of arbitrary functions in this class and succeed in decomposing these functions. Furthermore, we describe an efficient numerical algorithm for the practical implementation of the MSST, and we provide an adaptive IF extraction method for MSST reconstruction. Finally, we verify the effectiveness of the MSST in practical applications for machine fault diagnosis, including gearbox fault diagnosis for a wind turbine in variable speed conditions and rotor rub-impact fault diagnosis for a dual-rotor turbofan engine.

Noise covariance incorporated MEG-MUSIC algorithm: a method for multiple-dipole estimation tolerant of the influence of background brain activity.

PubMed

Sekihara, K; Poeppel, D; Marantz, A; Koizumi, H; Miyashita, Y

1997-09-01

This paper proposes a method of localizing multiple current dipoles from spatio-temporal biomagnetic data. The method is based on the multiple signal classification (MUSIC) algorithm and is tolerant of the influence of background brain activity. In this method, the noise covariance matrix is estimated using a portion of the data that contains noise, but does not contain any signal information. Then, a modified noise subspace projector is formed using the generalized eigenvectors of the noise and measured-data covariance matrices. The MUSIC localizer is calculated using this noise subspace projector and the noise covariance matrix. The results from a computer simulation have verified the effectiveness of the method. The method was then applied to source estimation for auditory-evoked fields elicited by syllable speech sounds. The results strongly suggest the method's effectiveness in removing the influence of background activity.

A theoretical signal processing framework for linear diffusion MRI: Implications for parameter estimation and experiment design.

PubMed

Varadarajan, Divya; Haldar, Justin P

2017-11-01

The data measured in diffusion MRI can be modeled as the Fourier transform of the Ensemble Average Propagator (EAP), a probability distribution that summarizes the molecular diffusion behavior of the spins within each voxel. This Fourier relationship is potentially advantageous because of the extensive theory that has been developed to characterize the sampling requirements, accuracy, and stability of linear Fourier reconstruction methods. However, existing diffusion MRI data sampling and signal estimation methods have largely been developed and tuned without the benefit of such theory, instead relying on approximations, intuition, and extensive empirical evaluation. This paper aims to address this discrepancy by introducing a novel theoretical signal processing framework for diffusion MRI. The new framework can be used to characterize arbitrary linear diffusion estimation methods with arbitrary q-space sampling, and can be used to theoretically evaluate and compare the accuracy, resolution, and noise-resilience of different data acquisition and parameter estimation techniques. The framework is based on the EAP, and makes very limited modeling assumptions. As a result, the approach can even provide new insight into the behavior of model-based linear diffusion estimation methods in contexts where the modeling assumptions are inaccurate. The practical usefulness of the proposed framework is illustrated using both simulated and real diffusion MRI data in applications such as choosing between different parameter estimation methods and choosing between different q-space sampling schemes. Copyright © 2017 Elsevier Inc. All rights reserved.

Maximum-likelihood spectral estimation and adaptive filtering techniques with application to airborne Doppler weather radar. Thesis Technical Report No. 20

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.

Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors.

PubMed

Baek, Hyun Jae; Kim, Ko Keun; Kim, Jung Soo; Lee, Boreom; Park, Kwang Suk

2010-02-01

A new method of blood pressure (BP) estimation using multiple regression with pulse arrival time (PAT) and two confounding factors was evaluated in clinical and unconstrained monitoring situations. For the first analysis with clinical data, electrocardiogram (ECG), photoplethysmogram (PPG) and invasive BP signals were obtained by a conventional patient monitoring device during surgery. In the second analysis, ECG, PPG and non-invasive BP were measured using systems developed to obtain data under conditions in which the subject was not constrained. To enhance the performance of BP estimation methods, heart rate (HR) and arterial stiffness were considered as confounding factors in regression analysis. The PAT and HR were easily extracted from ECG and PPG signals. For arterial stiffness, the duration from the maximum derivative point to the maximum of the dicrotic notch in the PPG signal, a parameter called TDB, was employed. In two experiments that normally cause BP variation, the correlation between measured BP and the estimated BP was investigated. Multiple-regression analysis with the two confounding factors improved correlation coefficients for diastolic blood pressure and systolic blood pressure to acceptable confidence levels, compared to existing methods that consider PAT only. In addition, reproducibility for the proposed method was determined using constructed test sets. Our results demonstrate that non-invasive, non-intrusive BP estimation can be obtained using methods that can be applied in both clinical and daily healthcare situations.

Real-Time Rotational Activity Detection in Atrial Fibrillation

PubMed Central

Ríos-Muñoz, Gonzalo R.; Arenal, Ángel; Artés-Rodríguez, Antonio

2018-01-01

Rotational activations, or spiral waves, are one of the proposed mechanisms for atrial fibrillation (AF) maintenance. We present a system for assessing the presence of rotational activity from intracardiac electrograms (EGMs). Our system is able to operate in real-time with multi-electrode catheters of different topologies in contact with the atrial wall, and it is based on new local activation time (LAT) estimation and rotational activity detection methods. The EGM LAT estimation method is based on the identification of the highest sustained negative slope of unipolar signals. The method is implemented as a linear filter whose output is interpolated on a regular grid to match any catheter topology. Its operation is illustrated on selected signals and compared to the classical Hilbert-Transform-based phase analysis. After the estimation of the LAT on the regular grid, the detection of rotational activity in the atrium is done by a novel method based on the optical flow of the wavefront dynamics, and a rotation pattern match. The methods have been validated using in silico and real AF signals. PMID:29593566

A novel algorithm for laser self-mixing sensors used with the Kalman filter to measure displacement

NASA Astrophysics Data System (ADS)

Sun, Hui; Liu, Ji-Gou

2018-07-01

This paper proposes a simple and effective method for estimating the feedback level factor C in a self-mixing interferometric sensor. It is used with a Kalman filter to retrieve the displacement. Without the complicated and onerous calculation process of the general C estimation method, a final equation is obtained. Thus, the estimation of C only involves a few simple calculations. It successfully retrieves the sinusoidal and aleatory displacement by means of simulated self-mixing signals in both weak and moderate feedback regimes. To deal with the errors resulting from noise and estimate bias of C and to further improve the retrieval precision, a Kalman filter is employed following the general phase unwrapping method. The simulation and experiment results show that the retrieved displacement using the C obtained with the proposed method is comparable to the joint estimation of C and α. Besides, the Kalman filter can significantly decrease measurement errors, especially the error caused by incorrectly locating the peak and valley positions of the signal.

A time-frequency analysis method to obtain stable estimates of magnetotelluric response function based on Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Cai, Jianhua

2017-05-01

The time-frequency analysis method represents signal as a function of time and frequency, and it is considered a powerful tool for handling arbitrary non-stationary time series by using instantaneous frequency and instantaneous amplitude. It also provides a possible alternative to the analysis of the non-stationary magnetotelluric (MT) signal. Based on the Hilbert-Huang transform (HHT), a time-frequency analysis method is proposed to obtain stable estimates of the magnetotelluric response function. In contrast to conventional methods, the response function estimation is performed in the time-frequency domain using instantaneous spectra rather than in the frequency domain, which allows for imaging the response parameter content as a function of time and frequency. The theory of the method is presented and the mathematical model and calculation procedure, which are used to estimate response function based on HHT time-frequency spectrum, are discussed. To evaluate the results, response function estimates are compared with estimates from a standard MT data processing method based on the Fourier transform. All results show that apparent resistivities and phases, which are calculated from the HHT time-frequency method, are generally more stable and reliable than those determined from the simple Fourier analysis. The proposed method overcomes the drawbacks of the traditional Fourier methods, and the resulting parameter minimises the estimation bias caused by the non-stationary characteristics of the MT data.

Strain, curvature, and twist measurements in digital holographic interferometry using pseudo-Wigner-Ville distribution based method

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

Rajshekhar, G.; Gorthi, Sai Siva; Rastogi, Pramod

2009-09-15

Measurement of strain, curvature, and twist of a deformed object play an important role in deformation analysis. Strain depends on the first order displacement derivative, whereas curvature and twist are determined by second order displacement derivatives. This paper proposes a pseudo-Wigner-Ville distribution based method for measurement of strain, curvature, and twist in digital holographic interferometry where the object deformation or displacement is encoded as interference phase. In the proposed method, the phase derivative is estimated by peak detection of pseudo-Wigner-Ville distribution evaluated along each row/column of the reconstructed interference field. A complex exponential signal with unit amplitude and the phasemore » derivative estimate as the argument is then generated and the pseudo-Wigner-Ville distribution along each row/column of this signal is evaluated. The curvature is estimated by using peak tracking strategy for the new distribution. For estimation of twist, the pseudo-Wigner-Ville distribution is evaluated along each column/row (i.e., in alternate direction with respect to the previous one) for the generated complex exponential signal and the corresponding peak detection gives the twist estimate.« less

Search for gravitational waves from LIGO-Virgo science run and data interpretation

NASA Astrophysics Data System (ADS)

Biswas, Rahul

Search for gravitational wave events was performed on data jointly taken during LIGO's fifth science run (S5) and Virgo's first science mn (VSR1). The data taken during this period was broken down into five separate months. I shall report the analysis performed on one of these months. Apart from the search, I shall describe the work related to estimation of rate based on the loudest event in the search. I shall demonstrate methods used in construction of rate intervals at 90% confidence level and combination of rates from multiple experiments of similar duration. To have confidence in our detection, accurate estimation of false alarm probability (F.A.P.) associated with the event candidate is required. Current false alarm estimation techniques limit our ability to measure the F.A.P. to about 1 in 100. I shall describe a method that significantly improves this estimate using information from multiple detectors. Besides accurate knowledge of F.A.P., detection is also dependent on our ability to distinguish real signals to those from noise. Several tests exist which use the quality of the signal to differentiate between real and noise signal. The chi-square test is one such computationally expensive test applied in our search; we shall understand the dependence of the chi-square parameter on the signal to noise ratio (SNR) for a given signal, which will help us to model the chi-square parameter based on SNR. The two detectors at Hanford, WA, H1(4km) and H2(2km), share the same vacuum system and hence their noise is correlated. Our present method of background estimation cannot capture this correlation and often underestimates the background when only H1 and H2 are operating. I shall describe a novel method of time reversed filtering to correctly estimate the background.

A Simple Noise Correction Scheme for Diffusional Kurtosis Imaging

PubMed Central

Glenn, G. Russell; Tabesh, Ali; Jensen, Jens H.

2014-01-01

Purpose Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis. Methods Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates. Results As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR. Conclusion The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms. PMID:25172990

Sensor fusion using a hybrid median filter for artifact removal in intraoperative heart rate monitoring.

PubMed

Yang, Ping; Dumont, Guy A; Ansermino, J Mark

2009-04-01

Intraoperative heart rate is routinely measured independently from the ECG monitor, pulse oximeter, and the invasive blood pressure monitor if available. The presence of artifacts, in one or more of theses signals, especially sustained artifacts, represents a critical challenge for physiological monitoring. When temporal filters are used to suppress sustained artifacts, unwanted delays or signal distortion are often introduced. The aim of this study was to remove artifacts and derive accurate estimates for the heart rate signal by using measurement redundancy. Heart rate measurements from multiple sensors and previous estimates that fall in a short moving window were treated as samples of the same heart rate. A hybrid median filter was used to align these samples into one ordinal series and to select the median as the fused estimate. This method can successfully remove artifacts that are sustained for shorter than half the length of the filter window, or artifacts that are sustained for a longer duration but presented in no more than half of the sensors. The method was tested on both simulated and clinical cases. The performance of the hybrid median filter in the simulated study was compared with that of a two-step estimation process, comprising a threshold-controlled artifact-removal module and a Kalman filter. The estimation accuracy of the hybrid median filter is better than that of the Kalman filter in the presence of artifacts. The hybrid median filter combines the structural and temporal information from two or more sensors and generates a robust estimate of heart rate without requiring strict assumptions about the signal's characteristics. This method is intuitive, computationally simple, and the performance can be easily adjusted. These considerable benefits make this method highly suitable for clinical use.

Parallel interference cancellation for CDMA applications

NASA Technical Reports Server (NTRS)

Divsalar, Dariush (Inventor); Simon, Marvin K. (Inventor); Raphaeli, Dan (Inventor)

1997-01-01

The present invention provides a method of decoding a spread spectrum composite signal, the composite signal comprising plural user signals that have been spread with plural respective codes, wherein each coded signal is despread, averaged to produce a signal value, analyzed to produce a tentative decision, respread, summed with other respread signals to produce combined interference signals, the method comprising scaling the combined interference signals with a weighting factor to produce a scaled combined interference signal, scaling the composite signal with the weighting factor to produce a scaled composite signal, scaling the signal value by the complement of the weighting factor to produce a leakage signal, combining the scaled composite signal, the scaled combined interference signal and the leakage signal to produce an estimate of a respective user signal.

Estimating Isometric Tension of Finger Muscle Using Needle EMG Signals and the Twitch Contraction Model

NASA Astrophysics Data System (ADS)

Tachibana, Hideyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

We address an estimation method of isometric muscle tension of fingers, as fundamental research for a neural signal-based prosthesis of fingers. We utilize needle electromyogram (EMG) signals, which have approximately equivalent information to peripheral neural signals. The estimating algorithm comprised two convolution operations. The first convolution is between normal distribution and a spike array, which is detected by needle EMG signals. The convolution estimates the probability density of spike-invoking time in the muscle. In this convolution, we hypothesize that each motor unit in a muscle activates spikes independently based on a same probability density function. The second convolution is between the result of the previous convolution and isometric twitch, viz., the impulse response of the motor unit. The result of the calculation is the sum of all estimated tensions of whole muscle fibers, i.e., muscle tension. We confirmed that there is good correlation between the estimated tension of the muscle and the actual tension, with >0.9 correlation coefficients at 59%, and >0.8 at 89% of all trials.

Nonnegative definite EAP and ODF estimation via a unified multi-shell HARDI reconstruction.

PubMed

Cheng, Jian; Jiang, Tianzi; Deriche, Rachid

2012-01-01

In High Angular Resolution Diffusion Imaging (HARDI), Orientation Distribution Function (ODF) and Ensemble Average Propagator (EAP) are two important Probability Density Functions (PDFs) which reflect the water diffusion and fiber orientations. Spherical Polar Fourier Imaging (SPFI) is a recent model-free multi-shell HARDI method which estimates both EAP and ODF from the diffusion signals with multiple b values. As physical PDFs, ODFs and EAPs are nonnegative definite respectively in their domains S2 and R3. However, existing ODF/EAP estimation methods like SPFI seldom consider this natural constraint. Although some works considered the nonnegative constraint on the given discrete samples of ODF/EAP, the estimated ODF/EAP is not guaranteed to be nonnegative definite in the whole continuous domain. The Riemannian framework for ODFs and EAPs has been proposed via the square root parameterization based on pre-estimated ODFs and EAPs by other methods like SPFI. However, there is no work on how to estimate the square root of ODF/EAP called as the wavefuntion directly from diffusion signals. In this paper, based on the Riemannian framework for ODFs/EAPs and Spherical Polar Fourier (SPF) basis representation, we propose a unified model-free multi-shell HARDI method, named as Square Root Parameterized Estimation (SRPE), to simultaneously estimate both the wavefunction of EAPs and the nonnegative definite ODFs and EAPs from diffusion signals. The experiments on synthetic data and real data showed SRPE is more robust to noise and has better EAP reconstruction than SPFI, especially for EAP profiles at large radius.

Current-induced alternating reversed dual-echo-steady-state for joint estimation of tissue relaxation and electrical properties.

PubMed

Lee, Hyunyeol; Sohn, Chul-Ho; Park, Jaeseok

2017-07-01

To develop a current-induced, alternating reversed dual-echo-steady-state-based magnetic resonance electrical impedance tomography for joint estimation of tissue relaxation and electrical properties. The proposed method reverses the readout gradient configuration of conventional, in which steady-state-free-precession (SSFP)-ECHO is produced earlier than SSFP-free-induction-decay (FID) while alternating current pulses are applied in between the two SSFPs to secure high sensitivity of SSFP-FID to injection current. Additionally, alternating reversed dual-echo-steady-state signals are modulated by employing variable flip angles over two orthogonal injections of current pulses. Ratiometric signal models are analytically constructed, from which T 1 , T 2 , and current-induced B z are jointly estimated by solving a nonlinear inverse problem for conductivity reconstruction. Numerical simulations and experimental studies are performed to investigate the feasibility of the proposed method in estimating relaxation parameters and conductivity. The proposed method, if compared with conventional magnetic resonance electrical impedance tomography, enables rapid data acquisition and simultaneous estimation of T 1 , T 2 , and current-induced B z , yielding a comparable level of signal-to-noise ratio in the parameter estimates while retaining a relative conductivity contrast. We successfully demonstrated the feasibility of the proposed method in jointly estimating tissue relaxation parameters as well as conductivity distributions. It can be a promising, rapid imaging strategy for quantitative conductivity estimation. Magn Reson Med 78:107-120, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Method for determining waveguide temperature for acoustic transceiver used in a gas turbine engine

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

DeSilva, Upul P.; Claussen, Heiko; Ragunathan, Karthik

A method for determining waveguide temperature for at least one waveguide of a transceiver utilized for generating a temperature map. The transceiver generates an acoustic signal that travels through a measurement space in a hot gas flow path defined by a wall such as in a combustor. The method includes calculating a total time of flight for the acoustic signal and subtracting a waveguide travel time from the total time of flight to obtain a measurement space travel time. A temperature map is calculated based on the measurement space travel time. An estimated wall temperature is obtained from the temperaturemore » map. An estimated waveguide temperature is then calculated based on the estimated wall temperature wherein the estimated waveguide temperature is determined without the use of a temperature sensing device.« less

Human joint motion estimation for electromyography (EMG)-based dynamic motion control.

PubMed

Zhang, Qin; Hosoda, Ryo; Venture, Gentiane

2013-01-01

This study aims to investigate a joint motion estimation method from Electromyography (EMG) signals during dynamic movement. In most EMG-based humanoid or prosthetics control systems, EMG features were directly or indirectly used to trigger intended motions. However, both physiological and nonphysiological factors can influence EMG characteristics during dynamic movements, resulting in subject-specific, non-stationary and crosstalk problems. Particularly, when motion velocity and/or joint torque are not constrained, joint motion estimation from EMG signals are more challenging. In this paper, we propose a joint motion estimation method based on muscle activation recorded from a pair of agonist and antagonist muscles of the joint. A linear state-space model with multi input single output is proposed to map the muscle activity to joint motion. An adaptive estimation method is proposed to train the model. The estimation performance is evaluated in performing a single elbow flexion-extension movement in two subjects. All the results in two subjects at two load levels indicate the feasibility and suitability of the proposed method in joint motion estimation. The estimation root-mean-square error is within 8.3% ∼ 10.6%, which is lower than that being reported in several previous studies. Moreover, this method is able to overcome subject-specific problem and compensate non-stationary EMG properties.

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization.

PubMed

Mäkelä, Niko; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2018-02-15

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications. Copyright © 2017 Elsevier Inc. All rights reserved.

Sequential Total Variation Denoising for the Extraction of Fetal ECG from Single-Channel Maternal Abdominal ECG

PubMed Central

Lee, Kwang Jin; Lee, Boreom

2016-01-01

Fetal heart rate (FHR) is an important determinant of fetal health. Cardiotocography (CTG) is widely used for measuring the FHR in the clinical field. However, fetal movement and blood flow through the maternal blood vessels can critically influence Doppler ultrasound signals. Moreover, CTG is not suitable for long-term monitoring. Therefore, researchers have been developing algorithms to estimate the FHR using electrocardiograms (ECGs) from the abdomen of pregnant women. However, separating the weak fetal ECG signal from the abdominal ECG signal is a challenging problem. In this paper, we propose a method for estimating the FHR using sequential total variation denoising and compare its performance with that of other single-channel fetal ECG extraction methods via simulation using the Fetal ECG Synthetic Database (FECGSYNDB). Moreover, we used real data from PhysioNet fetal ECG databases for the evaluation of the algorithm performance. The R-peak detection rate is calculated to evaluate the performance of our algorithm. Our approach could not only separate the fetal ECG signals from the abdominal ECG signals but also accurately estimate the FHR. PMID:27376296

Sequential Total Variation Denoising for the Extraction of Fetal ECG from Single-Channel Maternal Abdominal ECG.

PubMed

Lee, Kwang Jin; Lee, Boreom

2016-07-01

Fetal heart rate (FHR) is an important determinant of fetal health. Cardiotocography (CTG) is widely used for measuring the FHR in the clinical field. However, fetal movement and blood flow through the maternal blood vessels can critically influence Doppler ultrasound signals. Moreover, CTG is not suitable for long-term monitoring. Therefore, researchers have been developing algorithms to estimate the FHR using electrocardiograms (ECGs) from the abdomen of pregnant women. However, separating the weak fetal ECG signal from the abdominal ECG signal is a challenging problem. In this paper, we propose a method for estimating the FHR using sequential total variation denoising and compare its performance with that of other single-channel fetal ECG extraction methods via simulation using the Fetal ECG Synthetic Database (FECGSYNDB). Moreover, we used real data from PhysioNet fetal ECG databases for the evaluation of the algorithm performance. The R-peak detection rate is calculated to evaluate the performance of our algorithm. Our approach could not only separate the fetal ECG signals from the abdominal ECG signals but also accurately estimate the FHR.

Noise source and reactor stability estimation in a boiling water reactor using a multivariate autoregressive model

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

Kanemoto, S.; Andoh, Y.; Sandoz, S.A.

1984-10-01

A method for evaluating reactor stability in boiling water reactors has been developed. The method is based on multivariate autoregressive (M-AR) modeling of steady-state neutron and process noise signals. In this method, two kinds of power spectral densities (PSDs) for the measured neutron signal and the corresponding noise source signal are separately identified by the M-AR modeling. The closed- and open-loop stability parameters are evaluated from these PSDs. The method is applied to actual plant noise data that were measured together with artificial perturbation test data. Stability parameters identified from noise data are compared to those from perturbation test data,more » and it is shown that both results are in good agreement. In addition to these stability estimations, driving noise sources for the neutron signal are evaluated by the M-AR modeling. Contributions from void, core flow, and pressure noise sources are quantitatively evaluated, and the void noise source is shown to be the most dominant.« less

Improved estimation of Mars ionosphere total electron content

NASA Astrophysics Data System (ADS)

Cartacci, M.; Sánchez-Cano, B.; Orosei, R.; Noschese, R.; Cicchetti, A.; Witasse, O.; Cantini, F.; Rossi, A. P.

2018-01-01

We describe an improved method to estimate the Total Electron Content (TEC) of the Mars ionosphere from the echoes recorded by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) (Picardi et al., 2005; Orosei et al., 2015) onboard Mars Express in its subsurface sounding mode. In particular, we demonstrate that this method solves the issue of the former algorithm described at (Cartacci et al., 2013), which produced an overestimation of TEC estimates on the day side. The MARSIS signal is affected by a phase distortion introduced by the Mars ionosphere that produces a variation of the signal shape and a delay in its travel time. The new TEC estimation is achieved correlating the parameters obtained through the correction of the aforementioned effects. In detail, the knowledge of the quadratic term of the phase distortion estimated by the Contrast Method (Cartacci et al., 2013), together with the linear term (i.e. the extra time delay), estimated through a radar signal simulator, allows to develop a new algorithm particularly well suited to estimate the TEC for solar zenith angles (SZA) lower than 95° The new algorithm for the dayside has been validated with independent data from MARSIS in its Active Ionospheric Sounding (AIS) operational mode, with comparisons with other previous algorithms based on MARSIS subsurface data, with modeling and with modeling ionospheric distortion TEC reconstruction.

P wave detection in ECG signals using an extended Kalman filter: an evaluation in different arrhythmia contexts.

PubMed

Rahimpour, M; Mohammadzadeh Asl, B

2016-07-01

Monitoring atrial activity via P waves, is an important feature of the arrhythmia detection procedure. The aim of this paper is to present an algorithm for P wave detection in normal and some abnormal records by improving existing methods in the field of signal processing. In contrast to the classical approaches, which are completely blind to signal dynamics, our proposed method uses the extended Kalman filter, EKF25, to estimate the state variables of the equations modeling the dynamic of an ECG signal. This method is a modified version of the nonlinear dynamical model previously introduced for a generation of synthetic ECG signals and fiducial point extraction in normal ones. It is capable of estimating the separate types of activity of the heart with reasonable accuracy and performs well in the presence of morphological variations in the waveforms and ectopic beats. The MIT-BIH Arrhythmia and QT databases have been used to evaluate the performance of the proposed method. The results show that this method has Se  =  98.38% and Pr  =  96.74% in the overall records (considering normal and abnormal rhythms).

Detection the nonlinear ultrasonic signals based on modified Duffing equations

NASA Astrophysics Data System (ADS)

Zhang, Yuhua; Mao, Hanling; Mao, Hanying; Huang, Zhenfeng

The nonlinear ultrasonic signals, like second harmonic generation (SHG) signals, could reflect the nonlinearity of material induced by fatigue damage in nonlinear ultrasonic technique which are weak nonlinear signals and usually submerged by strong background noise. In this paper the modified Duffing equations are applied to detect the SHG signals relating to the fatigue damage of material. Due to the Duffing equation could only detect the signal with specific frequency and initial phase, firstly the frequency transformation is carried on the Duffing equation which could detect the signal with any frequency. Then the influence of initial phases of to-be-detected signal and reference signal on the detection result is studied in detail, four modified Duffing equations are proposed to detect actual engineering signals with any initial phase. The relationship between the response amplitude and the total driving force is applied to estimate the amplitude of weak periodic signal. The detection results show the modified Duffing equations could effectively detect the second harmonic in SHG signals. When the SHG signals include strong background noise, the noise doesn't change the motion state of Duffing equation and the second harmonic signal could be detected until the SNR of noisy SHG signals are -26.3, yet the frequency spectrum method could only identify when the SNR is greater than 0.5. When estimation the amplitude of second harmonic signal, the estimation error of Duffing equation is obviously less than the frequency spectrum analysis method under the same noise level, which illustrates the Duffing equation has the noise immune capacity. The presence of the second harmonic signal in nonlinear ultrasonic experiments could provide an insight about the early fatigue damage of engineering components.

Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.

PubMed

Engemann, Denis A; Gramfort, Alexandre

2015-03-01

Magnetoencephalography and electroencephalography (M/EEG) measure non-invasively the weak electromagnetic fields induced by post-synaptic neural currents. The estimation of the spatial covariance of the signals recorded on M/EEG sensors is a building block of modern data analysis pipelines. Such covariance estimates are used in brain-computer interfaces (BCI) systems, in nearly all source localization methods for spatial whitening as well as for data covariance estimation in beamformers. The rationale for such models is that the signals can be modeled by a zero mean Gaussian distribution. While maximizing the Gaussian likelihood seems natural, it leads to a covariance estimate known as empirical covariance (EC). It turns out that the EC is a poor estimate of the true covariance when the number of samples is small. To address this issue the estimation needs to be regularized. The most common approach downweights off-diagonal coefficients, while more advanced regularization methods are based on shrinkage techniques or generative models with low rank assumptions: probabilistic PCA (PPCA) and factor analysis (FA). Using cross-validation all of these models can be tuned and compared based on Gaussian likelihood computed on unseen data. We investigated these models on simulations, one electroencephalography (EEG) dataset as well as magnetoencephalography (MEG) datasets from the most common MEG systems. First, our results demonstrate that different models can be the best, depending on the number of samples, heterogeneity of sensor types and noise properties. Second, we show that the models tuned by cross-validation are superior to models with hand-selected regularization. Hence, we propose an automated solution to the often overlooked problem of covariance estimation of M/EEG signals. The relevance of the procedure is demonstrated here for spatial whitening and source localization of MEG signals. Copyright © 2015 Elsevier Inc. All rights reserved.

Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel

PubMed Central

Akbari, Mohsen; Manesh, Mohsen Riahi

2014-01-01

In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods. PMID:25045725

Heart rate detection from an electronic weighing scale.

PubMed

González-Landaeta, R; Casas, O; Pallàs-Areny, R

2007-01-01

We propose a novel technique for heart rate detection on a subject that stands on a common electronic weighing scale. The detection relies on sensing force variations related to the blood acceleration in the aorta, works even if wearing footwear, and does not require any sensors attached to the body. We have applied our method to three different weighing scales, and estimated whether their sensitivity and frequency response suited heart rate detection. Scale sensitivities were from 490 nV/V/N to 1670 nV/V/N, all had an underdamped transient response and their dynamic gain error was below 19% at 10 Hz, which are acceptable values for heart rate estimation. We also designed a pulse detection system based on off-the-shelf integrated circuits, whose gain was about 70x10(3) and able to sense force variations about 240 mN. The signal-to-noise ratio (SNR) of the main peaks of the pulse signal detected was higher than 48 dB, which is large enough to estimate the heart rate by simple signal processing methods. To validate the method, the ECG and the force signal were simultaneously recorded on 12 volunteers. The maximal error obtained from heart rates determined from these two signals was +/-0.6 beats/minute.

Estimating weak ratiometric signals in imaging data. I. Dual-channel data.

PubMed

Broder, Josef; Majumder, Anirban; Porter, Erika; Srinivasamoorthy, Ganesh; Keith, Charles; Lauderdale, James; Sornborger, Andrew

2007-09-01

Ratiometric fluorescent indicators are becoming increasingly prevalent in many areas of biology. They are used for making quantitative measurements of intracellular free calcium both in vitro and in vivo, as well as measuring membrane potentials, pH, and other important physiological variables of interest to researchers in many subfields. Often, functional changes in the fluorescent yield of ratiometric indicators are small, and the signal-to-noise ratio (SNR) is of order unity or less. In particular, variability in the denominator of the ratio can lead to very poor ratio estimates. We present a statistical optimization method for objectively detecting and estimating ratiometric signals in dual-wavelength measurements of fluorescent, ratiometric indicators that improves on standard methods. With the use of an appropriate statistical model for ratiometric signals and by taking the pixel-pixel covariance of an imaging dataset into account, we are able to extract user-independent spatiotemporal information that retains high resolution in both space and time.

Multiple resolution chirp reflectometry for fault localization and diagnosis in a high voltage cable in automotive electronics

NASA Astrophysics Data System (ADS)

Chang, Seung Jin; Lee, Chun Ku; Shin, Yong-June; Park, Jin Bae

2016-12-01

A multiple chirp reflectometry system with a fault estimation process is proposed to obtain multiple resolution and to measure the degree of fault in a target cable. A multiple resolution algorithm has the ability to localize faults, regardless of fault location. The time delay information, which is derived from the normalized cross-correlation between the incident signal and bandpass filtered reflected signals, is converted to a fault location and cable length. The in-phase and quadrature components are obtained by lowpass filtering of the mixed signal of the incident signal and the reflected signal. Based on in-phase and quadrature components, the reflection coefficient is estimated by the proposed fault estimation process including the mixing and filtering procedure. Also, the measurement uncertainty for this experiment is analyzed according to the Guide to the Expression of Uncertainty in Measurement. To verify the performance of the proposed method, we conduct comparative experiments to detect and measure faults under different conditions. Considering the installation environment of the high voltage cable used in an actual vehicle, target cable length and fault position are designed. To simulate the degree of fault, the variety of termination impedance (10 Ω , 30 Ω , 50 Ω , and 1 \\text{k} Ω ) are used and estimated by the proposed method in this experiment. The proposed method demonstrates advantages in that it has multiple resolution to overcome the blind spot problem, and can assess the state of the fault.

Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.

PubMed

Ma, Chao; Lam, Fan; Johnson, Curtis L; Liang, Zhi-Pei

2016-02-01

To remove nuisance signals (e.g., water and lipid signals) for (1) H MRSI data collected from the brain with limited and/or sparse (k, t)-space coverage. A union-of-subspace model is proposed for removing nuisance signals. The model exploits the partial separability of both the nuisance signals and the metabolite signal, and decomposes an MRSI dataset into several sets of generalized voxels that share the same spectral distributions. This model enables the estimation of the nuisance signals from an MRSI dataset that has limited and/or sparse (k, t)-space coverage. The proposed method has been evaluated using in vivo MRSI data. For conventional chemical shift imaging data with limited k-space coverage, the proposed method produced "lipid-free" spectra without lipid suppression during data acquisition at 130 ms echo time. For sparse (k, t)-space data acquired with conventional pulses for water and lipid suppression, the proposed method was also able to remove the remaining water and lipid signals with negligible residuals. Nuisance signals in (1) H MRSI data reside in low-dimensional subspaces. This property can be utilized for estimation and removal of nuisance signals from (1) H MRSI data even when they have limited and/or sparse coverage of (k, t)-space. The proposed method should prove useful especially for accelerated high-resolution (1) H MRSI of the brain. © 2015 Wiley Periodicals, Inc.

Features selection and classification to estimate elbow movements

NASA Astrophysics Data System (ADS)

Rubiano, A.; Ramírez, J. L.; El Korso, M. N.; Jouandeau, N.; Gallimard, L.; Polit, O.

2015-11-01

In this paper, we propose a novel method to estimate the elbow motion, through the features extracted from electromyography (EMG) signals. The features values are normalized and then compared to identify potential relationships between the EMG signal and the kinematic information as angle and angular velocity. We propose and implement a method to select the best set of features, maximizing the distance between the features that correspond to flexion and extension movements. Finally, we test the selected features as inputs to a non-linear support vector machine in the presence of non-idealistic conditions, obtaining an accuracy of 99.79% in the motion estimation results.

Bayesian estimation of self-similarity exponent

NASA Astrophysics Data System (ADS)

Makarava, Natallia; Benmehdi, Sabah; Holschneider, Matthias

2011-08-01

In this study we propose a Bayesian approach to the estimation of the Hurst exponent in terms of linear mixed models. Even for unevenly sampled signals and signals with gaps, our method is applicable. We test our method by using artificial fractional Brownian motion of different length and compare it with the detrended fluctuation analysis technique. The estimation of the Hurst exponent of a Rosenblatt process is shown as an example of an H-self-similar process with non-Gaussian dimensional distribution. Additionally, we perform an analysis with real data, the Dow-Jones Industrial Average closing values, and analyze its temporal variation of the Hurst exponent.

Cross Time-Frequency Analysis for Combining Information of Several Sources: Application to Estimation of Spontaneous Respiratory Rate from Photoplethysmography

PubMed Central

Peláez-Coca, M. D.; Orini, M.; Lázaro, J.; Bailón, R.; Gil, E.

2013-01-01

A methodology that combines information from several nonstationary biological signals is presented. This methodology is based on time-frequency coherence, that quantifies the similarity of two signals in the time-frequency domain. A cross time-frequency analysis method, based on quadratic time-frequency distribution, has been used for combining information of several nonstationary biomedical signals. In order to evaluate this methodology, the respiratory rate from the photoplethysmographic (PPG) signal is estimated. The respiration provokes simultaneous changes in the pulse interval, amplitude, and width of the PPG signal. This suggests that the combination of information from these sources will improve the accuracy of the estimation of the respiratory rate. Another target of this paper is to implement an algorithm which provides a robust estimation. Therefore, respiratory rate was estimated only in those intervals where the features extracted from the PPG signals are linearly coupled. In 38 spontaneous breathing subjects, among which 7 were characterized by a respiratory rate lower than 0.15 Hz, this methodology provided accurate estimates, with the median error {0.00; 0.98} mHz ({0.00; 0.31}%) and the interquartile range error {4.88; 6.59} mHz ({1.60; 1.92}%). The estimation error of the presented methodology was largely lower than the estimation error obtained without combining different PPG features related to respiration. PMID:24363777

Transient high frequency signal estimation: A model-based processing approach

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

Barnes, F.L.

1985-03-22

By utilizing the superposition property of linear systems a method of estimating the incident signal from reflective nondispersive data is developed. One of the basic merits of this approach is that, the reflections were removed by direct application of a Weiner type estimation algorithm, after the appropriate input was synthesized. The structure of the nondispersive signal model is well documented, and thus its' credence is established. The model is stated and more effort is devoted to practical methods of estimating the model parameters. Though a general approach was developed for obtaining the reflection weights, a simpler approach was employed here,more » since a fairly good reflection model is available. The technique essentially consists of calculating ratios of the autocorrelation function at lag zero and that lag where the incident and first reflection coincide. We initially performed our processing procedure on a measurement of a single signal. Multiple application of the processing procedure was required when we applied the reflection removal technique on a measurement containing information from the interaction of two physical phenomena. All processing was performed using SIG, an interactive signal processing package. One of the many consequences of using SIG was that repetitive operations were, for the most part, automated. A custom menu was designed to perform the deconvolution process.« less

System and method for optical fiber based image acquisition suitable for use in turbine engines

DOEpatents

Baleine, Erwan; A V, Varun; Zombo, Paul J.; Varghese, Zubin

2017-05-16

A system and a method for image acquisition suitable for use in a turbine engine are disclosed. Light received from a field of view in an object plane is projected onto an image plane through an optical modulation device and is transferred through an image conduit to a sensor array. The sensor array generates a set of sampled image signals in a sensing basis based on light received from the image conduit. Finally, the sampled image signals are transformed from the sensing basis to a representation basis and a set of estimated image signals are generated therefrom. The estimated image signals are used for reconstructing an image and/or a motion-video of a region of interest within a turbine engine.

A novel estimating method for steering efficiency of the driver with electromyography signals

NASA Astrophysics Data System (ADS)

Liu, Yahui; Ji, Xuewu; Hayama, Ryouhei; Mizuno, Takahiro

2014-05-01

The existing research of steering efficiency mainly focuses on the mechanism efficiency of steering system, aiming at designing and optimizing the mechanism of steering system. In the development of assist steering system especially the evaluation of its comfort, the steering efficiency of driver physiological output usually are not considered, because this physiological output is difficult to measure or to estimate, and the objective evaluation of steering comfort therefore cannot be conducted with movement efficiency perspective. In order to take a further step to the objective evaluation of steering comfort, an estimating method for the steering efficiency of the driver was developed based on the research of the relationship between the steering force and muscle activity. First, the steering forces in the steering wheel plane and the electromyography (EMG) signals of the primary muscles were measured. These primary muscles are the muscles in shoulder and upper arm which mainly produced the steering torque, and their functions in steering maneuver were identified previously. Next, based on the multiple regressions of the steering force and EMG signals, both the effective steering force and the total force capacity of driver in steering maneuver were calculated. Finally, the steering efficiency of driver was estimated by means of the estimated effective force and the total force capacity, which represented the information of driver physiological output of the primary muscles. This research develops a novel estimating method for driver steering efficiency of driver physiological output, including the estimation of both steering force and the force capacity of primary muscles with EMG signals, and will benefit to evaluate the steering comfort with an objective perspective.

Viability estimation of pepper seeds using time-resolved photothermal signal characterization

NASA Astrophysics Data System (ADS)

Kim, Ghiseok; Kim, Geon-Hee; Lohumi, Santosh; Kang, Jum-Soon; Cho, Byoung-Kwan

2014-11-01

We used infrared thermal signal measurement system and photothermal signal and image reconstruction techniques for viability estimation of pepper seeds. Photothermal signals from healthy and aged seeds were measured for seven periods (24, 48, 72, 96, 120, 144, and 168 h) using an infrared camera and analyzed by a regression method. The photothermal signals were regressed using a two-term exponential decay curve with two amplitudes and two time variables (lifetime) as regression coefficients. The regression coefficients of the fitted curve showed significant differences for each seed groups, depending on the aging times. In addition, the viability of a single seed was estimated by imaging of its regression coefficient, which was reconstructed from the measured photothermal signals. The time-resolved photothermal characteristics, along with the regression coefficient images, can be used to discriminate the aged or dead pepper seeds from the healthy seeds.

A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

PubMed Central

Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang

2014-01-01

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method. PMID:24573313

A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.

PubMed

Wang, Xianpeng; Wang, Wei; Li, Xin; Wang, Junxiang

2014-02-25

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

A generalised random encounter model for estimating animal density with remote sensor data.

PubMed

Lucas, Tim C D; Moorcroft, Elizabeth A; Freeman, Robin; Rowcliffe, J Marcus; Jones, Kate E

2015-05-01

Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals.We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement.We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM.We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.

Power system frequency estimation based on an orthogonal decomposition method

NASA Astrophysics Data System (ADS)

Lee, Chih-Hung; Tsai, Men-Shen

2018-06-01

In recent years, several frequency estimation techniques have been proposed by which to estimate the frequency variations in power systems. In order to properly identify power quality issues under asynchronously-sampled signals that are contaminated with noise, flicker, and harmonic and inter-harmonic components, a good frequency estimator that is able to estimate the frequency as well as the rate of frequency changes precisely is needed. However, accurately estimating the fundamental frequency becomes a very difficult task without a priori information about the sampling frequency. In this paper, a better frequency evaluation scheme for power systems is proposed. This method employs a reconstruction technique in combination with orthogonal filters, which may maintain the required frequency characteristics of the orthogonal filters and improve the overall efficiency of power system monitoring through two-stage sliding discrete Fourier transforms. The results showed that this method can accurately estimate the power system frequency under different conditions, including asynchronously sampled signals contaminated by noise, flicker, and harmonic and inter-harmonic components. The proposed approach also provides high computational efficiency.

Model-free and analytical EAP reconstruction via spherical polar Fourier diffusion MRI.

PubMed

Cheng, Jian; Ghosh, Aurobrata; Jiang, Tianzi; Deriche, Rachid

2010-01-01

How to estimate the diffusion Ensemble Average Propagator (EAP) from the DWI signals in q-space is an open problem in diffusion MRI field. Many methods were proposed to estimate the Orientation Distribution Function (ODF) that is used to describe the fiber direction. However, ODF is just one of the features of the EAP. Compared with ODF, EAP has the full information about the diffusion process which reflects the complex tissue micro-structure. Diffusion Orientation Transform (DOT) and Diffusion Spectrum Imaging (DSI) are two important methods to estimate the EAP from the signal. However, DOT is based on mono-exponential assumption and DSI needs a lot of samplings and very large b values. In this paper, we propose Spherical Polar Fourier Imaging (SPFI), a novel model-free fast robust analytical EAP reconstruction method, which almost does not need any assumption of data and does not need too many samplings. SPFI naturally combines the DWI signals with different b-values. It is an analytical linear transformation from the q-space signal to the EAP profile represented by Spherical Harmonics (SH). We validated the proposed methods in synthetic data, phantom data and real data. It works well in all experiments, especially for the data with low SNR, low anisotropy, and non-exponential decay.

Estimation of heart rate variability using a compact radiofrequency motion sensor.

PubMed

Sugita, Norihiro; Matsuoka, Narumi; Yoshizawa, Makoto; Abe, Makoto; Homma, Noriyasu; Otake, Hideharu; Kim, Junghyun; Ohtaki, Yukio

2015-12-01

Physiological indices that reflect autonomic nervous activity are considered useful for monitoring peoples' health on a daily basis. A number of such indices are derived from heart rate variability, which is obtained by a radiofrequency (RF) motion sensor without making physical contact with the user's body. However, the bulkiness of RF motion sensors used in previous studies makes them unsuitable for home use. In this study, a new method to measure heart rate variability using a compact RF motion sensor that is sufficiently small to fit in a user's shirt pocket is proposed. To extract a heart rate related component from the sensor signal, an algorithm that optimizes a digital filter based on the power spectral density of the signal is proposed. The signals of the RF motion sensor were measured for 29 subjects during the resting state and their heart rate variability was estimated from the measured signals using the proposed method and a conventional method. A correlation coefficient between true heart rate and heart rate estimated from the proposed method was 0.69. Further, the experimental results showed the viability of the RF sensor for monitoring autonomic nervous activity. However, some improvements such as controlling the direction of sensing were necessary for stable measurement. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Improved vertical streambed flux estimation using multiple diurnal temperature methods in series

USGS Publications Warehouse

Irvine, Dylan J.; Briggs, Martin A.; Cartwright, Ian; Scruggs, Courtney; Lautz, Laura K.

2017-01-01

Analytical solutions that use diurnal temperature signals to estimate vertical fluxes between groundwater and surface water based on either amplitude ratios (Ar) or phase shifts (Δϕ) produce results that rarely agree. Analytical solutions that simultaneously utilize Ar and Δϕ within a single solution have more recently been derived, decreasing uncertainty in flux estimates in some applications. Benefits of combined (ArΔϕ) methods also include that thermal diffusivity and sensor spacing can be calculated. However, poor identification of either Ar or Δϕ from raw temperature signals can lead to erratic parameter estimates from ArΔϕ methods. An add-on program for VFLUX 2 is presented to address this issue. Using thermal diffusivity selected from an ArΔϕ method during a reliable time period, fluxes are recalculated using an Ar method. This approach maximizes the benefits of the Ar and ArΔϕ methods. Additionally, sensor spacing calculations can be used to identify periods with unreliable flux estimates, or to assess streambed scour. Using synthetic and field examples, the use of these solutions in series was particularly useful for gaining conditions where fluxes exceeded 1 m/d.

Multiunit Activity-Based Real-Time Limb-State Estimation from Dorsal Root Ganglion Recordings

PubMed Central

Han, Sungmin; Chu, Jun-Uk; Kim, Hyungmin; Park, Jong Woong; Youn, Inchan

2017-01-01

Proprioceptive afferent activities could be useful for providing sensory feedback signals for closed-loop control during functional electrical stimulation (FES). However, most previous studies have used the single-unit activity of individual neurons to extract sensory information from proprioceptive afferents. This study proposes a new decoding method to estimate ankle and knee joint angles using multiunit activity data. Proprioceptive afferent signals were recorded from a dorsal root ganglion with a single-shank microelectrode during passive movements of the ankle and knee joints, and joint angles were measured as kinematic data. The mean absolute value (MAV) was extracted from the multiunit activity data, and a dynamically driven recurrent neural network (DDRNN) was used to estimate ankle and knee joint angles. The multiunit activity-based MAV feature was sufficiently informative to estimate limb states, and the DDRNN showed a better decoding performance than conventional linear estimators. In addition, processing time delay satisfied real-time constraints. These results demonstrated that the proposed method could be applicable for providing real-time sensory feedback signals in closed-loop FES systems. PMID:28276474

On a Chirplet Transform Based Method for Co-channel Voice Separation

NASA Astrophysics Data System (ADS)

Dugnol, B.; Fernández, C.; Galiano, G.; Velasco, J.

We use signal and image theory based algorithms to produce estimations of the number of wolves emitting howls or barks in a given field recording as an individuals counting alternative to the traditional trace collecting methodologies. We proceed in two steps. Firstly, we clean and enhance the signal by using PDE based image processing algorithms applied to the signal spectrogram. Secondly, assuming that the wolves chorus may be modelled as an addition of nonlinear chirps, we use the quadratic energy distribution corresponding to the Chirplet Transform of the signal to produce estimates of the corresponding instantaneous frequencies, chirp-rates and amplitudes at each instant of the recording. We finally establish suitable criteria to decide how such estimates are connected in time.

A labview-based GUI for the measurement of otoacoustic emissions.

PubMed

Wu, Ye; McNamara, D M; Ziarani, A K

2006-01-01

This paper presents the outcome of a software development project aimed at creating a stand-alone user-friendly signal processing algorithm for the estimation of distortion product otoacoustic emission (OAE) signals. OAE testing is one of the most commonly used methods of first screening of newborns' hearing. Most of the currently available commercial devices rely upon averaging long strings of data and subsequent discrete Fourier analysis to estimate low level OAE signals from within the background noise in the presence of the strong stimuli. The main shortcoming of the presently employed technology is the need for long measurement time and its low noise immunity. The result of the software development project presented here is a graphical user interface (GUI) module that implements a recently introduced adaptive technique of OAE signal estimation. This software module is easy to use and is freely disseminated on the Internet for the use of the hearing research community. This GUI module allows loading of the a priori recorded OAE signals into the workspace, and provides the user with interactive instructions for the OAE signal estimation. Moreover, the user can generate simulated OAE signals to objectively evaluate the performance capability of the implemented signal processing technique.

Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals.

PubMed

Faes, L; Porta, A; Cucino, R; Cerutti, S; Antolini, R; Nollo, G

2004-06-01

Although the concept of transfer function is intrinsically related to an input-output relationship, the traditional and widely used estimation method merges both feedback and feedforward interactions between the two analyzed signals. This limitation may endanger the reliability of transfer function analysis in biological systems characterized by closed loop interactions. In this study, a method for estimating the transfer function between closed loop interacting signals was proposed and validated in the field of cardiovascular and cardiorespiratory variability. The two analyzed signals x and y were described by a bivariate autoregressive model, and the causal transfer function from x to y was estimated after imposing causality by setting to zero the model coefficients representative of the reverse effects from y to x. The method was tested in simulations reproducing linear open and closed loop interactions, showing a better adherence of the causal transfer function to the theoretical curves with respect to the traditional approach in presence of non-negligible reverse effects. It was then applied in ten healthy young subjects to characterize the transfer functions from respiration to heart period (RR interval) and to systolic arterial pressure (SAP), and from SAP to RR interval. In the first two cases, the causal and non-causal transfer function estimates were comparable, indicating that respiration, acting as exogenous signal, sets an open loop relationship upon SAP and RR interval. On the contrary, causal and traditional transfer functions from SAP to RR were significantly different, suggesting the presence of a considerable influence on the opposite causal direction. Thus, the proposed causal approach seems to be appropriate for the estimation of parameters, like the gain and the phase lag from SAP to RR interval, which have a large clinical and physiological relevance.

Sparse estimation of model-based diffuse thermal dust emission

NASA Astrophysics Data System (ADS)

Irfan, Melis O.; Bobin, Jérôme

2018-03-01

Component separation for the Planck High Frequency Instrument (HFI) data is primarily concerned with the estimation of thermal dust emission, which requires the separation of thermal dust from the cosmic infrared background (CIB). For that purpose, current estimation methods rely on filtering techniques to decouple thermal dust emission from CIB anisotropies, which tend to yield a smooth, low-resolution, estimation of the dust emission. In this paper, we present a new parameter estimation method, premise: Parameter Recovery Exploiting Model Informed Sparse Estimates. This method exploits the sparse nature of thermal dust emission to calculate all-sky maps of thermal dust temperature, spectral index, and optical depth at 353 GHz. premise is evaluated and validated on full-sky simulated data. We find the percentage difference between the premise results and the true values to be 2.8, 5.7, and 7.2 per cent at the 1σ level across the full sky for thermal dust temperature, spectral index, and optical depth at 353 GHz, respectively. A comparison between premise and a GNILC-like method over selected regions of our sky simulation reveals that both methods perform comparably within high signal-to-noise regions. However, outside of the Galactic plane, premise is seen to outperform the GNILC-like method with increasing success as the signal-to-noise ratio worsens.

On protecting the planet against cosmic attack: Ultrafast real-time estimate of the asteroid's radial velocity

NASA Astrophysics Data System (ADS)

Zakharchenko, V. D.; Kovalenko, I. G.

2014-05-01

A new method for the line-of-sight velocity estimation of a high-speed near-Earth object (asteroid, meteorite) is suggested. The method is based on the use of fractional, one-half order derivative of a Doppler signal. The algorithm suggested is much simpler and more economical than the classical one, and it appears preferable for use in orbital weapon systems of threat response. Application of fractional differentiation to quick evaluation of mean frequency location of the reflected Doppler signal is justified. The method allows an assessment of the mean frequency in the time domain without spectral analysis. An algorithm structure for the real-time estimation is presented. The velocity resolution estimates are made for typical asteroids in the X-band. It is shown that the wait time can be shortened by orders of magnitude compared with similar value in the case of a standard spectral processing.

Method and system for controlling a permanent magnet machine

DOEpatents

Walters, James E.

2003-05-20

Method and system for controlling the start of a permanent magnet machine are provided. The method allows to assign a parameter value indicative of an estimated initial rotor position of the machine. The method further allows to energize the machine with a level of current being sufficiently high to start rotor motion in a desired direction in the event the initial rotor position estimate is sufficiently close to the actual rotor position of the machine. A sensing action allows to sense whether any incremental changes in rotor position occur in response to the energizing action. In the event no changes in rotor position are sensed, the method allows to incrementally adjust the estimated rotor position by a first set of angular values until changes in rotor position are sensed. In the event changes in rotor position are sensed, the method allows to provide a rotor alignment signal as rotor motion continues. The alignment signal allows to align the estimated rotor position relative to the actual rotor position. This alignment action allows for operating the machine over a wide speed range.

Comparison Between Sea Surface Wind Speed Estimates From Reflected GPS Signals and Buoy Measurements

NASA Technical Reports Server (NTRS)

Garrison, James L.; Katzberg, Steven J.; Zavorotny, Valery U.

2000-01-01

Reflected signals from the Global Positioning System (GPS) have been collected from an aircraft at approximately 3.7 km altitude on 5 different days. Estimation of surface wind speed by matching the shape of the reflected signal correlation function against analytical models was demonstrated. Wind speed obtained from this method agreed with that recorded from buoys to with a bias of less than 0.1 m/s, and with a standard derivation of 1.3 meters per second.

Performance analysis of structured gradient algorithm. [for adaptive beamforming linear arrays

NASA Technical Reports Server (NTRS)

Godara, Lal C.

1990-01-01

The structured gradient algorithm uses a structured estimate of the array correlation matrix (ACM) to estimate the gradient required for the constrained least-mean-square (LMS) algorithm. This structure reflects the structure of the exact array correlation matrix for an equispaced linear array and is obtained by spatial averaging of the elements of the noisy correlation matrix. In its standard form the LMS algorithm does not exploit the structure of the array correlation matrix. The gradient is estimated by multiplying the array output with the receiver outputs. An analysis of the two algorithms is presented to show that the covariance of the gradient estimated by the structured method is less sensitive to the look direction signal than that estimated by the standard method. The effect of the number of elements on the signal sensitivity of the two algorithms is studied.

A Doppler centroid estimation algorithm for SAR systems optimized for the quasi-homogeneous source

NASA Technical Reports Server (NTRS)

Jin, Michael Y.

1989-01-01

Radar signal processing applications frequently require an estimate of the Doppler centroid of a received signal. The Doppler centroid estimate is required for synthetic aperture radar (SAR) processing. It is also required for some applications involving target motion estimation and antenna pointing direction estimation. In some cases, the Doppler centroid can be accurately estimated based on available information regarding the terrain topography, the relative motion between the sensor and the terrain, and the antenna pointing direction. Often, the accuracy of the Doppler centroid estimate can be improved by analyzing the characteristics of the received SAR signal. This kind of signal processing is also referred to as clutterlock processing. A Doppler centroid estimation (DCE) algorithm is described which contains a linear estimator optimized for the type of terrain surface that can be modeled by a quasi-homogeneous source (QHS). Information on the following topics is presented: (1) an introduction to the theory of Doppler centroid estimation; (2) analysis of the performance characteristics of previously reported DCE algorithms; (3) comparison of these analysis results with experimental results; (4) a description and performance analysis of a Doppler centroid estimator which is optimized for a QHS; and (5) comparison of the performance of the optimal QHS Doppler centroid estimator with that of previously reported methods.

Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

PubMed Central

Pappachan, Bobby K; Caesarendra, Wahyu; Tjahjowidodo, Tegoeh; Wijaya, Tomi

2017-01-01

Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process. PMID:28556809

Uncertainty Estimates of Psychoacoustic Thresholds Obtained from Group Tests

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Christian, Andrew

2016-01-01

Adaptive psychoacoustic test methods, in which the next signal level depends on the response to the previous signal, are the most efficient for determining psychoacoustic thresholds of individual subjects. In many tests conducted in the NASA psychoacoustic labs, the goal is to determine thresholds representative of the general population. To do this economically, non-adaptive testing methods are used in which three or four subjects are tested at the same time with predetermined signal levels. This approach requires us to identify techniques for assessing the uncertainty in resulting group-average psychoacoustic thresholds. In this presentation we examine the Delta Method of frequentist statistics, the Generalized Linear Model (GLM), the Nonparametric Bootstrap, a frequentist method, and Markov Chain Monte Carlo Posterior Estimation and a Bayesian approach. Each technique is exercised on a manufactured, theoretical dataset and then on datasets from two psychoacoustics facilities at NASA. The Delta Method is the simplest to implement and accurate for the cases studied. The GLM is found to be the least robust, and the Bootstrap takes the longest to calculate. The Bayesian Posterior Estimate is the most versatile technique examined because it allows the inclusion of prior information.

A joint tracking method for NSCC based on WLS algorithm

NASA Astrophysics Data System (ADS)

Luo, Ruidan; Xu, Ying; Yuan, Hong

2017-12-01

Navigation signal based on compound carrier (NSCC), has the flexible multi-carrier scheme and various scheme parameters configuration, which enables it to possess significant efficiency of navigation augmentation in terms of spectral efficiency, tracking accuracy, multipath mitigation capability and anti-jamming reduction compared with legacy navigation signals. Meanwhile, the typical scheme characteristics can provide auxiliary information for signal synchronism algorithm design. This paper, based on the characteristics of NSCC, proposed a kind of joint tracking method utilizing Weighted Least Square (WLS) algorithm. In this method, the LS algorithm is employed to jointly estimate each sub-carrier frequency shift with the frequency-Doppler linear relationship, by utilizing the known sub-carrier frequency. Besides, the weighting matrix is set adaptively according to the sub-carrier power to ensure the estimation accuracy. Both the theory analysis and simulation results illustrate that the tracking accuracy and sensitivity of this method outperforms the single-carrier algorithm with lower SNR.

Spatially Correlated Sparse MIMO Channel Path Delay Estimation in Scattering Environments Based on Signal Subspace Tracking

PubMed Central

Chargé, Pascal; Bazzi, Oussama; Ding, Yuehua

2018-01-01

A parametric scheme for spatially correlated sparse multiple-input multiple-output (MIMO) channel path delay estimation in scattering environments is presented in this paper. In MIMO outdoor communication scenarios, channel impulse responses (CIRs) of different transmit–receive antenna pairs are often supposed to be sparse due to a few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit–receive antenna pair. In some existing works, an exact common support condition is exploited, where the path delays are considered equal for every transmit–receive antenna pair, meanwhile ignoring the influence of scattering. A more realistic channel model is proposed in this paper, where due to scatterers in the environment, the received signals are modeled as clusters of multi-rays around a nominal or mean time delay at different antenna elements, resulting in a non-strictly exact common support phenomenon. A method for estimating the channel mean path delays is then derived based on the subspace approach, and the tracking of the effective dimension of the signal subspace that changes due to the wireless environment. The proposed method shows an improved channel mean path delays estimation performance in comparison with the conventional estimation methods. PMID:29734797

Spatially Correlated Sparse MIMO Channel Path Delay Estimation in Scattering Environments Based on Signal Subspace Tracking.

PubMed

Mohydeen, Ali; Chargé, Pascal; Wang, Yide; Bazzi, Oussama; Ding, Yuehua

2018-05-06

A parametric scheme for spatially correlated sparse multiple-input multiple-output (MIMO) channel path delay estimation in scattering environments is presented in this paper. In MIMO outdoor communication scenarios, channel impulse responses (CIRs) of different transmit⁻receive antenna pairs are often supposed to be sparse due to a few significant scatterers, and share a common sparse pattern, such that path delays are assumed to be equal for every transmit⁻receive antenna pair. In some existing works, an exact common support condition is exploited, where the path delays are considered equal for every transmit⁻receive antenna pair, meanwhile ignoring the influence of scattering. A more realistic channel model is proposed in this paper, where due to scatterers in the environment, the received signals are modeled as clusters of multi-rays around a nominal or mean time delay at different antenna elements, resulting in a non-strictly exact common support phenomenon. A method for estimating the channel mean path delays is then derived based on the subspace approach, and the tracking of the effective dimension of the signal subspace that changes due to the wireless environment. The proposed method shows an improved channel mean path delays estimation performance in comparison with the conventional estimation methods.

Direct estimation of evoked hemoglobin changes by multimodality fusion imaging

PubMed Central

Huppert, Theodore J.; Diamond, Solomon G.; Boas, David A.

2009-01-01

In the last two decades, both diffuse optical tomography (DOT) and blood oxygen level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) methods have been developed as noninvasive tools for imaging evoked cerebral hemodynamic changes in studies of brain activity. Although these two technologies measure functional contrast from similar physiological sources, i.e., changes in hemoglobin levels, these two modalities are based on distinct physical and biophysical principles leading to both limitations and strengths to each method. In this work, we describe a unified linear model to combine the complimentary spatial, temporal, and spectroscopic resolutions of concurrently measured optical tomography and fMRI signals. Using numerical simulations, we demonstrate that concurrent optical and BOLD measurements can be used to create cross-calibrated estimates of absolute micromolar deoxyhemoglobin changes. We apply this new analysis tool to experimental data acquired simultaneously with both DOT and BOLD imaging during a motor task, demonstrate the ability to more robustly estimate hemoglobin changes in comparison to DOT alone, and show how this approach can provide cross-calibrated estimates of hemoglobin changes. Using this multimodal method, we estimate the calibration of the 3 tesla BOLD signal to be −0.55% ± 0.40% signal change per micromolar change of deoxyhemoglobin. PMID:19021411

Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution

PubMed Central

Vrancken, Bram; Lemey, Philippe; Rambaut, Andrew; Bedford, Trevor; Longdon, Ben; Günthard, Huldrych F.; Suchard, Marc A.

2014-01-01

Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel’s λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution. PMID:25780554

Fast Estimation of Defect Profiles from the Magnetic Flux Leakage Signal Based on a Multi-Power Affine Projection Algorithm

PubMed Central

Han, Wenhua; Shen, Xiaohui; Xu, Jun; Wang, Ping; Tian, Guiyun; Wu, Zhengyang

2014-01-01

Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection. PMID:25192314

Fast estimation of defect profiles from the magnetic flux leakage signal based on a multi-power affine projection algorithm.

PubMed

Han, Wenhua; Shen, Xiaohui; Xu, Jun; Wang, Ping; Tian, Guiyun; Wu, Zhengyang

2014-09-04

Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection.

Towards Photoplethysmography-Based Estimation of Instantaneous Heart Rate During Physical Activity.

PubMed

Jarchi, Delaram; Casson, Alexander J

2017-09-01

Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise. For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill. Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals. Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise. Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals.

Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity.

PubMed

Akdenur, B; Okkesum, S; Kara, S; Günes, S

2009-11-01

In this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg-Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8-13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum-maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent performance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.

Range estimation of passive infrared targets through the atmosphere

NASA Astrophysics Data System (ADS)

Cho, Hoonkyung; Chun, Joohwan; Seo, Doochun; Choi, Seokweon

2013-04-01

Target range estimation is traditionally based on radar and active sonar systems in modern combat systems. However, jamming signals tremendously degrade the performance of such active sensor devices. We introduce a simple target range estimation method and the fundamental limits of the proposed method based on the atmosphere propagation model. Since passive infrared (IR) sensors measure IR signals radiating from objects in different wavelengths, this method has robustness against electromagnetic jamming. The measured target radiance of each wavelength at the IR sensor depends on the emissive properties of target material and various attenuation factors (i.e., the distance between sensor and target and atmosphere environment parameters). MODTRAN is a tool that models atmospheric propagation of electromagnetic radiation. Based on the results from MODTRAN and atmosphere propagation-based modeling, the target range can be estimated. To analyze the proposed method's performance statistically, we use maximum likelihood estimation (MLE) and evaluate the Cramer-Rao lower bound (CRLB) via the probability density function of measured radiance. We also compare CRLB and the variance of MLE using Monte-Carlo simulation.

Automated Method of Frequency Determination in Software Metric Data Through the Use of the Multiple Signal Classification (MUSIC) Algorithm

DTIC Science & Technology

1998-06-26

METHOD OF FREQUENCY DETERMINATION 4 IN SOFTWARE METRIC DATA THROUGH THE USE OF THE 5 MULTIPLE SIGNAL CLASSIFICATION ( MUSIC ) ALGORITHM 6 7 STATEMENT OF...graph showing the estimated power spectral 12 density (PSD) generated by the multiple signal classification 13 ( MUSIC ) algorithm from the data set used...implemented in this module; however, it is preferred to use 1 the Multiple Signal Classification ( MUSIC ) algorithm. The MUSIC 2 algorithm is

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

Mosher, J.C.; Leahy, R.M.

A new method for source localization is described that is based on a modification of the well known multiple signal classification (MUSIC) algorithm. In classical MUSIC, the array manifold vector is projected onto an estimate of the signal subspace, but errors in the estimate can make location of multiple sources difficult. Recursively applied and projected (RAP) MUSIC uses each successively located source to form an intermediate array gain matrix, and projects both the array manifold and the signal subspace estimate into its orthogonal complement. The MUSIC projection is then performed in this reduced subspace. Using the metric of principal angles,more » the authors describe a general form of the RAP-MUSIC algorithm for the case of diversely polarized sources. Through a uniform linear array simulation, the authors demonstrate the improved Monte Carlo performance of RAP-MUSIC relative to MUSIC and two other sequential subspace methods, S and IES-MUSIC.« less

Modelling ultrasound guided wave propagation for plate thickness measurement

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Dabak, Anand; Murthy, Nitish Krishna

2014-03-01

Structural Health monitoring refers to monitoring the health of plate-like walls of large reactors, pipelines and other structures in terms of corrosion detection and thickness estimation. The objective of this work is modeling the ultrasonic guided waves generated in a plate. The piezoelectric is excited by an input pulse to generate ultrasonic guided lamb waves in the plate that are received by another piezoelectric transducer. In contrast with existing methods, we develop a mathematical model of the direct component of the signal (DCS) recorded at the terminals of the piezoelectric transducer. The DCS model uses maximum likelihood technique to estimate the different parameters, namely the time delay of the signal due to the transducer delay and amplitude scaling of all the lamb wave modes due to attenuation, while taking into account the received signal spreading in time due to dispersion. The maximum likelihood estimate minimizes the energy difference between the experimental and the DCS model-generated signal. We demonstrate that the DCS model matches closely with experimentally recorded signals and show it can be used to estimate thickness of the plate. The main idea of the thickness estimation algorithm is to generate a bank of DCS model-generated signals, each corresponding to a different thickness of the plate and then find the closest match among these signals to the received signal, resulting in an estimate of the thickness of the plate. Therefore our approach provides a complementary suite of analytics to the existing thickness monitoring approaches.

A Timing Estimation Method Based-on Skewness Analysis in Vehicular Wireless Networks.

PubMed

Cui, Xuerong; Li, Juan; Wu, Chunlei; Liu, Jian-Hang

2015-11-13

Vehicle positioning technology has drawn more and more attention in vehicular wireless networks to reduce transportation time and traffic accidents. Nowadays, global navigation satellite systems (GNSS) are widely used in land vehicle positioning, but most of them are lack precision and reliability in situations where their signals are blocked. Positioning systems base-on short range wireless communication are another effective way that can be used in vehicle positioning or vehicle ranging. IEEE 802.11p is a new real-time short range wireless communication standard for vehicles, so a new method is proposed to estimate the time delay or ranges between vehicles based on the IEEE 802.11p standard which includes three main steps: cross-correlation between the received signal and the short preamble, summing up the correlated results in groups, and finding the maximum peak using a dynamic threshold based on the skewness analysis. With the range between each vehicle or road-side infrastructure, the position of neighboring vehicles can be estimated correctly. Simulation results were presented in the International Telecommunications Union (ITU) vehicular multipath channel, which show that the proposed method provides better precision than some well-known timing estimation techniques, especially in low signal to noise ratio (SNR) environments.

Non-Gaussian probabilistic MEG source localisation based on kernel density estimation☆

PubMed Central

Mohseni, Hamid R.; Kringelbach, Morten L.; Woolrich, Mark W.; Baker, Adam; Aziz, Tipu Z.; Probert-Smith, Penny

2014-01-01

There is strong evidence to suggest that data recorded from magnetoencephalography (MEG) follows a non-Gaussian distribution. However, existing standard methods for source localisation model the data using only second order statistics, and therefore use the inherent assumption of a Gaussian distribution. In this paper, we present a new general method for non-Gaussian source estimation of stationary signals for localising brain activity from MEG data. By providing a Bayesian formulation for MEG source localisation, we show that the source probability density function (pdf), which is not necessarily Gaussian, can be estimated using multivariate kernel density estimators. In the case of Gaussian data, the solution of the method is equivalent to that of widely used linearly constrained minimum variance (LCMV) beamformer. The method is also extended to handle data with highly correlated sources using the marginal distribution of the estimated joint distribution, which, in the case of Gaussian measurements, corresponds to the null-beamformer. The proposed non-Gaussian source localisation approach is shown to give better spatial estimates than the LCMV beamformer, both in simulations incorporating non-Gaussian signals, and in real MEG measurements of auditory and visual evoked responses, where the highly correlated sources are known to be difficult to estimate. PMID:24055702

Transmuted of Rayleigh Distribution with Estimation and Application on Noise Signal

NASA Astrophysics Data System (ADS)

Ahmed, Suhad; Qasim, Zainab

2018-05-01

This paper deals with transforming one parameter Rayleigh distribution, into transmuted probability distribution through introducing a new parameter (λ), since this studied distribution is necessary in representing signal data distribution and failure data model the value of this transmuted parameter |λ| ≤ 1, is also estimated as well as the original parameter (⊖) by methods of moments and maximum likelihood using different sample size (n=25, 50, 75, 100) and comparing the results of estimation by statistical measure (mean square error, MSE).

Robust Foot Clearance Estimation Based on the Integration of Foot-Mounted IMU Acceleration Data

PubMed Central

Benoussaad, Mourad; Sijobert, Benoît; Mombaur, Katja; Azevedo Coste, Christine

2015-01-01

This paper introduces a method for the robust estimation of foot clearance during walking, using a single inertial measurement unit (IMU) placed on the subject’s foot. The proposed solution is based on double integration and drift cancellation of foot acceleration signals. The method is insensitive to misalignment of IMU axes with respect to foot axes. Details are provided regarding calibration and signal processing procedures. Experimental validation was performed on 10 healthy subjects under three walking conditions: normal, fast and with obstacles. Foot clearance estimation results were compared to measurements from an optical motion capture system. The mean error between them is significantly less than 15% under the various walking conditions. PMID:26703622

Online frequency estimation with applications to engine and generator sets

NASA Astrophysics Data System (ADS)

Manngård, Mikael; Böling, Jari M.

2017-07-01

Frequency and spectral analysis based on the discrete Fourier transform is a fundamental task in signal processing and machine diagnostics. This paper aims at presenting computationally efficient methods for real-time estimation of stationary and time-varying frequency components in signals. A brief survey of the sliding time window discrete Fourier transform and Goertzel filter is presented, and two filter banks consisting of: (i) sliding time window Goertzel filters (ii) infinite impulse response narrow bandpass filters are proposed for estimating instantaneous frequencies. The proposed methods show excellent results on both simulation studies and on a case study using angular speed data measurements of the crankshaft of a marine diesel engine-generator set.

SVM-Based Spectral Analysis for Heart Rate from Multi-Channel WPPG Sensor Signals.

PubMed

Xiong, Jiping; Cai, Lisang; Wang, Fei; He, Xiaowei

2017-03-03

Although wrist-type photoplethysmographic (hereafter referred to as WPPG) sensor signals can measure heart rate quite conveniently, the subjects' hand movements can cause strong motion artifacts, and then the motion artifacts will heavily contaminate WPPG signals. Hence, it is challenging for us to accurately estimate heart rate from WPPG signals during intense physical activities. The WWPG method has attracted more attention thanks to the popularity of wrist-worn wearable devices. In this paper, a mixed approach called Mix-SVM is proposed, it can use multi-channel WPPG sensor signals and simultaneous acceleration signals to measurement heart rate. Firstly, we combine the principle component analysis and adaptive filter to remove a part of the motion artifacts. Due to the strong relativity between motion artifacts and acceleration signals, the further denoising problem is regarded as a sparse signals reconstruction problem. Then, we use a spectrum subtraction method to eliminate motion artifacts effectively. Finally, the spectral peak corresponding to heart rate is sought by an SVM-based spectral analysis method. Through the public PPG database in the 2015 IEEE Signal Processing Cup, we acquire the experimental results, i.e., the average absolute error was 1.01 beat per minute, and the Pearson correlation was 0.9972. These results also confirm that the proposed Mix-SVM approach has potential for multi-channel WPPG-based heart rate estimation in the presence of intense physical exercise.

Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal

DOE PAGES

Terwilliger, Thomas C.; Bunkóczi, Gábor; Hung, Li-Wei; ...

2016-03-01

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, we describe algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment. A simple theoretical framework [Terwilliger et al.(2016),Acta Cryst.D72, 346–358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. Thephenix.plan_sad_experimenttool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimatemore » the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. Thephenix.scale_and_mergetool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and thephenix.anomalous_signaltool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.« less

Effective direction of arrival estimation of gunshot signals from an in-flight unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Ribeiro, Juliano G. C.; Serrenho, Felipe G.; Apolinário, José A.; Ramos, António L. L.

2018-04-01

Spotting a shooter from a drone has been the subject of great interest lately due to its many applications in the fields of defense and security and law enforcement. Using a drone can be an effective way to detect potential threats in many real-life scenarios. Nevertheless, acoustic signals recorded from a drone usually exhibit a very low SNR, mainly due to the distance to the source and the proximity of the sensors to the propellers. This is a serious limiting factor and, therefore, the use of signal enhancement techniques is required. This work addresses the problem of determining the Direction-of-Arrival (DoA) of the muzzle blast, captured using a planar microphone array mounted on a commercial DJI PHANTOM 4 drone in flight. This new shooter localization method that relies solely on detecting and estimating the DoA of the muzzle blast. However, the typical low SNR in this scenario requires the use of preprocessing techniques, such as signal clipping and median filtering, to enhance the signal of interest (muzzle blast). In addition, we employ a recently introduced improved data selection DoA estimation method suitable for gunshot signals recorded from a low to medium altitude mobile aerial platform. Positive results achieved indicate that this approach is effective and of practical interest.

Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal

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

Terwilliger, Thomas C.; Bunkóczi, Gábor; Hung, Li-Wei

A key challenge in the SAD phasing method is solving a structure when the anomalous signal-to-noise ratio is low. Here, we describe algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment. A simple theoretical framework [Terwilliger et al.(2016),Acta Cryst.D72, 346–358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. Thephenix.plan_sad_experimenttool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimatemore » the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. Thephenix.scale_and_mergetool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and thephenix.anomalous_signaltool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.« less

An approach to source characterization of tremor signals associated with eruptions and lahars

NASA Astrophysics Data System (ADS)

Kumagai, Hiroyuki; Mothes, Patricia; Ruiz, Mario; Maeda, Yuta

2015-11-01

Tremor signals are observed in association with eruption activity and lahar descents. Reduced displacement ( D R) derived from tremor signals has been used to quantify tremor sources. However, tremor duration is not considered in D R, which makes it difficult to compare D R values estimated for different tremor episodes. We propose application of the amplitude source location (ASL) method to characterize the sources of tremor signals. We used this method to estimate the tremor source location and source amplitude from high-frequency (5-10 Hz) seismic amplitudes under the assumption of isotropic S-wave radiation. We considered the source amplitude to be the maximum value during tremor. We estimated the cumulative source amplitude ( I s) as the offset value of the time-integrated envelope of the vertical seismogram of tremor corrected for geometrical spreading and medium attenuation in the 5-10-Hz band. For eruption tremor signals, we also estimated the cumulative source pressure ( I p) from an infrasonic envelope waveform corrected for geometrical spreading. We studied these parameters of tremor signals associated with eruptions and lahars and explosion events at Tungurahua volcano, Ecuador. We identified two types of eruption tremor at Tungurahua: noise-like inharmonic waveforms and harmonic oscillatory signals. We found that I s increased linearly with increasing source amplitude for lahar tremor signals and explosion events, but I s increased exponentially with increasing source amplitude for inharmonic eruption tremor signals. The source characteristics of harmonic eruption tremor signals differed from those of inharmonic tremor signals. We found a linear relation between I s and I p for both explosion events and eruption tremor. Because I p may be proportional to the total mass involved during an eruption episode, this linear relation suggests that I s may be useful to quantify eruption size. The I s values we estimated for inharmonic eruption tremor were consistent with previous estimates of volumes of tephra fallout. The scaling relations among source parameters that we identified will contribute to our understanding of the dynamic processes associated with eruptions and lahars. This new approach is applicable in analyzing tremor sources in real time and may contribute to early assessment of the size of eruptions and lahars.

Nonlinear Blind Compensation for Array Signal Processing Application

PubMed Central

Ma, Hong; Jin, Jiang; Zhang, Hua

2018-01-01

Recently, nonlinear blind compensation technique has attracted growing attention in array signal processing application. However, due to the nonlinear distortion stemming from array receiver which consists of multi-channel radio frequency (RF) front-ends, it is too difficult to estimate the parameters of array signal accurately. A novel nonlinear blind compensation algorithm aims at the nonlinearity mitigation of array receiver and its spurious-free dynamic range (SFDR) improvement, which will be more precise to estimate the parameters of target signals such as their two-dimensional directions of arrival (2-D DOAs). Herein, the suggested method is designed as follows: the nonlinear model parameters of any channel of RF front-end are extracted to synchronously compensate the nonlinear distortion of the entire receiver. Furthermore, a verification experiment on the array signal from a uniform circular array (UCA) is adopted to testify the validity of our approach. The real-world experimental results show that the SFDR of the receiver is enhanced, leading to a significant improvement of the 2-D DOAs estimation performance for weak target signals. And these results demonstrate that our nonlinear blind compensation algorithm is effective to estimate the parameters of weak array signal in concomitance with strong jammers. PMID:29690571

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

PubMed

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its capability to capture the nonlinearities of the model better. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimation of Instantaneous Gas Exchange in Flow-Through Respirometry Systems: A Modern Revision of Bartholomew's Z-Transform Method

PubMed Central

Pendar, Hodjat; Socha, John J.

2015-01-01

Flow-through respirometry systems provide accurate measurement of gas exchange over long periods of time. However, these systems have limitations in tracking rapid changes. When an animal infuses a metabolic gas into the respirometry chamber in a short burst, diffusion and airflow in the chamber gradually alter the original signal before it arrives at the gas analyzer. For single or multiple bursts, the recorded signal is smeared or mixed, which may result in dramatically altered recordings compared to the emitted signal. Recovering the original metabolic signal is a difficult task because of the inherent ill conditioning problem. Here, we present two new methods to recover the fast dynamics of metabolic patterns from recorded data. We first re-derive the equations of the well-known Z-transform method (ZT method) to show the source of imprecision in this method. Then, we develop a new model of analysis for respirometry systems based on the experimentally determined impulse response, which is the response of the system to a very short unit input. As a result, we present a major modification of the ZT method (dubbed the ‘EZT method’) by using a new model for the impulse response, enhancing its precision to recover the true metabolic signals. The second method, the generalized Z-transform (GZT) method, was then developed by generalizing the EZT method; it can be applied to any flow-through respirometry system with any arbitrary impulse response. Experiments verified that the accuracy of recovering the true metabolic signals is significantly improved by the new methods. These new methods can be used more broadly for input estimation in variety of physiological systems. PMID:26466361

Suppression of the cardiopulmonary resuscitation artefacts using the instantaneous chest compression rate extracted from the thoracic impedance.

PubMed

Aramendi, E; Ayala, U; Irusta, U; Alonso, E; Eftestøl, T; Kramer-Johansen, J

2012-06-01

To demonstrate that the instantaneous chest compression rate can be accurately estimated from the transthoracic impedance (TTI), and that this estimated rate can be used in a method to suppress cardiopulmonary resuscitation (CPR) artefacts. A database of 372 records, 87 shockable and 285 non-shockable, from out-of-hospital cardiac arrest episodes, corrupted by CPR artefacts, was analysed. Each record contained the ECG and TTI obtained from the defibrillation pads and the compression depth (CD) obtained from a sternal CPR pad. The chest compression rates estimated using TTI and CD were compared. The CPR artefacts were then filtered using the instantaneous chest compression rates estimated from the TTI or CD signals. The filtering results were assessed in terms of the sensitivity and specificity of the shock advice algorithm of a commercial automated external defibrillator. The correlation between the mean chest compression rates estimated using TTI or CD was r=0.98 (95% confidence interval, 0.97-0.98). The sensitivity and specificity after filtering using CD were 95.4% (88.4-98.6%) and 87.0% (82.6-90.5%), respectively. The sensitivity and specificity after filtering using TTI were 95.4% (88.4-98.6%) and 86.3% (81.8-89.9%), respectively. The instantaneous chest compression rate can be accurately estimated from TTI. The sensitivity and specificity after filtering are similar to those obtained using the CD signal. Our CPR suppression method based exclusively on signals acquired through the defibrillation pads is as accurate as methods based on signals obtained from CPR feedback devices. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information.

PubMed

Cui, Dong; Pu, Weiting; Liu, Jing; Bian, Zhijie; Li, Qiuli; Wang, Lei; Gu, Guanghua

2016-10-01

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal and adaptive methods of processing hydroacoustic signals (review)

NASA Astrophysics Data System (ADS)

Malyshkin, G. S.; Sidel'nikov, G. B.

2014-09-01

Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.

Parameter estimation for the exponential-normal convolution model for background correction of affymetrix GeneChip data.

PubMed

McGee, Monnie; Chen, Zhongxue

2006-01-01

There are many methods of correcting microarray data for non-biological sources of error. Authors routinely supply software or code so that interested analysts can implement their methods. Even with a thorough reading of associated references, it is not always clear how requisite parts of the method are calculated in the software packages. However, it is important to have an understanding of such details, as this understanding is necessary for proper use of the output, or for implementing extensions to the model. In this paper, the calculation of parameter estimates used in Robust Multichip Average (RMA), a popular preprocessing algorithm for Affymetrix GeneChip brand microarrays, is elucidated. The background correction method for RMA assumes that the perfect match (PM) intensities observed result from a convolution of the true signal, assumed to be exponentially distributed, and a background noise component, assumed to have a normal distribution. A conditional expectation is calculated to estimate signal. Estimates of the mean and variance of the normal distribution and the rate parameter of the exponential distribution are needed to calculate this expectation. Simulation studies show that the current estimates are flawed; therefore, new ones are suggested. We examine the performance of preprocessing under the exponential-normal convolution model using several different methods to estimate the parameters.

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition.

PubMed

Parto Dezfouli, Mohammad Ali; Dezfouli, Mohsen Parto; Rad, Hamidreza Saligheh

2014-01-01

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

Noncoherent sampling technique for communications parameter estimations

NASA Technical Reports Server (NTRS)

Su, Y. T.; Choi, H. J.

1985-01-01

This paper presents a method of noncoherent demodulation of the PSK signal for signal distortion analysis at the RF interface. The received RF signal is downconverted and noncoherently sampled for further off-line processing. Any mismatch in phase and frequency is then compensated for by the software using the estimation techniques to extract the baseband waveform, which is needed in measuring various signal parameters. In this way, various kinds of modulated signals can be treated uniformly, independent of modulation format, and additional distortions introduced by the receiver or the hardware measurement instruments can thus be eliminated. Quantization errors incurred by digital sampling and ensuing software manipulations are analyzed and related numerical results are presented also.

Developing Bayesian adaptive methods for estimating sensitivity thresholds (d′) in Yes-No and forced-choice tasks

PubMed Central

Lesmes, Luis A.; Lu, Zhong-Lin; Baek, Jongsoo; Tran, Nina; Dosher, Barbara A.; Albright, Thomas D.

2015-01-01

Motivated by Signal Detection Theory (SDT), we developed a family of novel adaptive methods that estimate the sensitivity threshold—the signal intensity corresponding to a pre-defined sensitivity level (d′ = 1)—in Yes-No (YN) and Forced-Choice (FC) detection tasks. Rather than focus stimulus sampling to estimate a single level of %Yes or %Correct, the current methods sample psychometric functions more broadly, to concurrently estimate sensitivity and decision factors, and thereby estimate thresholds that are independent of decision confounds. Developed for four tasks—(1) simple YN detection, (2) cued YN detection, which cues the observer's response state before each trial, (3) rated YN detection, which incorporates a Not Sure response, and (4) FC detection—the qYN and qFC methods yield sensitivity thresholds that are independent of the task's decision structure (YN or FC) and/or the observer's subjective response state. Results from simulation and psychophysics suggest that 25 trials (and sometimes less) are sufficient to estimate YN thresholds with reasonable precision (s.d. = 0.10–0.15 decimal log units), but more trials are needed for FC thresholds. When the same subjects were tested across tasks of simple, cued, rated, and FC detection, adaptive threshold estimates exhibited excellent agreement with the method of constant stimuli (MCS), and with each other. These YN adaptive methods deliver criterion-free thresholds that have previously been exclusive to FC methods. PMID:26300798

Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

PubMed Central

Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.

2014-01-01

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269

Advanced Physiological Estimation of Cognitive Status. Part 2

DTIC Science & Technology

2011-05-24

Neurofeedback Algorithms and Gaze Controller EEG Sensor System g.USBamp *, ** • internal 24-bit ADC and digital signal processor • 16 channels (expandable...SUBJECT TERMS EEG eye-tracking mental state estimation machine learning Leonard J. Trejo Pacific Development and Technology LLC 999 Commercial St. Palo...fatigue, overload) Technology Transfer Opportunity Technology from PDT – Methods to acquire various physiological signals ( EEG , EOG, EMG, ECG, etc

Low-Cost 3-D Flow Estimation of Blood With Clutter.

PubMed

Wei, Siyuan; Yang, Ming; Zhou, Jian; Sampson, Richard; Kripfgans, Oliver D; Fowlkes, J Brian; Wenisch, Thomas F; Chakrabarti, Chaitali

2017-05-01

Volumetric flow rate estimation is an important ultrasound medical imaging modality that is used for diagnosing cardiovascular diseases. Flow rates are obtained by integrating velocity estimates over a cross-sectional plane. Speckle tracking is a promising approach that overcomes the angle dependency of traditional Doppler methods, but suffers from poor lateral resolution. Recent work improves lateral velocity estimation accuracy by reconstructing a synthetic lateral phase (SLP) signal. However, the estimation accuracy of such approaches is compromised by the presence of clutter. Eigen-based clutter filtering has been shown to be effective in removing the clutter signal; but it is computationally expensive, precluding its use at high volume rates. In this paper, we propose low-complexity schemes for both velocity estimation and clutter filtering. We use a two-tiered motion estimation scheme to combine the low complexity sum-of-absolute-difference and SLP methods to achieve subpixel lateral accuracy. We reduce the complexity of eigen-based clutter filtering by processing in subgroups and replacing singular value decomposition with less compute-intensive power iteration and subspace iteration methods. Finally, to improve flow rate estimation accuracy, we use kernel power weighting when integrating the velocity estimates. We evaluate our method for fast- and slow-moving clutter for beam-to-flow angles of 90° and 60° using Field II simulations, demonstrating high estimation accuracy across scenarios. For instance, for a beam-to-flow angle of 90° and fast-moving clutter, our estimation method provides a bias of -8.8% and standard deviation of 3.1% relative to the actual flow rate.

Energy Measurement Studies for CO2 Measurement with a Coherent Doppler Lidar System

NASA Technical Reports Server (NTRS)

Beyon, Jeffrey Y.; Koch, Grady J.; Vanvalkenburg, Randal L.; Yu, Jirong; Singh, Upendra N.; Kavaya, Michael J.

2010-01-01

The accurate measurement of energy in the application of lidar system for CO2 measurement is critical. Different techniques of energy estimation in the online and offline pulses are investigated for post processing of lidar returns. The cornerstone of the techniques is the accurate estimation of the spectrum of lidar signal and background noise. Since the background noise is not the ideal white Gaussian noise, simple average level estimation of noise level is not well fit in the energy estimation of lidar signal and noise. A brief review of the methods is presented in this paper.

Development and application of the maximum entropy method and other spectral estimation techniques

NASA Astrophysics Data System (ADS)

King, W. R.

1980-09-01

This summary report is a collection of four separate progress reports prepared under three contracts, which are all sponsored by the Office of Naval Research in Arlington, Virginia. This report contains the results of investigations into the application of the maximum entropy method (MEM), a high resolution, frequency and wavenumber estimation technique. The report also contains a description of two, new, stable, high resolution spectral estimation techniques that is provided in the final report section. Many examples of wavenumber spectral patterns for all investigated techniques are included throughout the report. The maximum entropy method is also known as the maximum entropy spectral analysis (MESA) technique, and both names are used in the report. Many MEM wavenumber spectral patterns are demonstrated using both simulated and measured radar signal and noise data. Methods for obtaining stable MEM wavenumber spectra are discussed, broadband signal detection using the MEM prediction error transform (PET) is discussed, and Doppler radar narrowband signal detection is demonstrated using the MEM technique. It is also shown that MEM cannot be applied to randomly sampled data. The two new, stable, high resolution, spectral estimation techniques discussed in the final report section, are named the Wiener-King and the Fourier spectral estimation techniques. The two new techniques have a similar derivation based upon the Wiener prediction filter, but the two techniques are otherwise quite different. Further development of the techniques and measurement of the technique spectral characteristics is recommended for subsequent investigation.

Coherent broadband sonar signal processing with the environmentally corrected matched filter

NASA Astrophysics Data System (ADS)

Camin, Henry John, III

The matched filter is the standard approach for coherently processing active sonar signals, where knowledge of the transmitted waveform is used in the detection and parameter estimation of received echoes. Matched filtering broadband signals provides higher levels of range resolution and reverberation noise suppression than can be realized through narrowband processing. Since theoretical processing gains are proportional to the signal bandwidth, it is typically desirable to utilize the widest band signals possible. However, as signal bandwidth increases, so do environmental effects that tend to decrease correlation between the received echo and the transmitted waveform. This is especially true for ultra wideband signals, where the bandwidth exceeds an octave or approximately 70% fractional bandwidth. This loss of coherence often results in processing gains and range resolution much lower than theoretically predicted. Wiener filtering, commonly used in image processing to improve distorted and noisy photos, is investigated in this dissertation as an approach to correct for these environmental effects. This improved signal processing, Environmentally Corrected Matched Filter (ECMF), first uses a Wiener filter to estimate the environmental transfer function and then again to correct the received signal using this estimate. This process can be viewed as a smarter inverse or whitening filter that adjusts behavior according to the signal to noise ratio across the spectrum. Though the ECMF is independent of bandwidth, it is expected that ultra wideband signals will see the largest improvement, since they tend to be more impacted by environmental effects. The development of the ECMF and demonstration of improved parameter estimation with its use are the primary emphases in this dissertation. Additionally, several new contributions to the field of sonar signal processing made in conjunction with the development of the ECMF are described. A new, nondimensional wideband ambiguity function is presented as a way to view the behavior of the matched filter with and without the decorrelating environmental effects; a new, integrated phase broadband angle estimation method is developed and compared to existing methods; and a new, asymptotic offset phase angle variance model is presented. Several data sets are used to demonstrate these new contributions. High fidelity Sonar Simulation Toolset (SST) synthetic data is used to characterize the theoretical performance. Two in-water data sets were used to verify assumptions that were made during the development of the ECMF. Finally, a newly collected in-air data set containing ultra wideband signals was used in lieu of a cost prohibitive underwater experiment to demonstrate the effectiveness of the ECMF at improving parameter estimates.

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

PubMed Central

Lin, Tiger W.; Das, Anup; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.

2017-01-01

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. PMID:28777719

Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

PubMed

Lin, Tiger W; Das, Anup; Krishnan, Giri P; Bazhenov, Maxim; Sejnowski, Terrence J

2017-10-01

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

New GRACE-Derived Storage Change Estimates Using Empirical Mode Extraction

NASA Astrophysics Data System (ADS)

Aierken, A.; Lee, H.; Yu, H.; Ate, P.; Hossain, F.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Shum, C. K.

2017-12-01

Estimated mass change from GRACE spherical harmonic solutions have north/south stripes and east/west banded errors due to random noise and modeling errors. Low pass filters like decorrelation and Gaussian smoothing are typically applied to reduce noise and errors. However, these filters introduce leakage errors that need to be addressed. GRACE mascon estimates (JPL and CSR mascon solutions) do not need decorrelation or Gaussian smoothing and offer larger signal magnitudes compared to the GRACE spherical harmonics (SH) filtered results. However, a recent study [Chen et al., JGR, 2017] demonstrated that both JPL and CSR mascon solutions also have leakage errors. We developed a new postprocessing method based on empirical mode decomposition to estimate mass change from GRACE SH solutions without decorrelation and Gaussian smoothing, the two main sources of leakage errors. We found that, without any post processing, the noise and errors in spherical harmonic solutions introduced very clear high frequency components in the spatial domain. By removing these high frequency components and reserve the overall pattern of the signal, we obtained better mass estimates with minimum leakage errors. The new global mass change estimates captured all the signals observed by GRACE without the stripe errors. Results were compared with traditional methods over the Tonle Sap Basin in Cambodia, Northwestern India, Central Valley in California, and the Caspian Sea. Our results provide larger signal magnitudes which are in good agreement with the leakage corrected (forward modeled) SH results.

Method and apparatus for reconstructing in-cylinder pressure and correcting for signal decay

DOEpatents

Huang, Jian

2013-03-12

A method comprises steps for reconstructing in-cylinder pressure data from a vibration signal collected from a vibration sensor mounted on an engine component where it can generate a signal with a high signal-to-noise ratio, and correcting the vibration signal for errors introduced by vibration signal charge decay and sensor sensitivity. The correction factors are determined as a function of estimated motoring pressure and the measured vibration signal itself with each of these being associated with the same engine cycle. Accordingly, the method corrects for charge decay and changes in sensor sensitivity responsive to different engine conditions to allow greater accuracy in the reconstructed in-cylinder pressure data. An apparatus is also disclosed for practicing the disclosed method, comprising a vibration sensor, a data acquisition unit for receiving the vibration signal, a computer processing unit for processing the acquired signal and a controller for controlling the engine operation based on the reconstructed in-cylinder pressure.

Occupant traffic estimation through structural vibration sensing

NASA Astrophysics Data System (ADS)

Pan, Shijia; Mirshekari, Mostafa; Zhang, Pei; Noh, Hae Young

2016-04-01

The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of +/-0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation

PubMed Central

Li, Hong; Lu, Mingquan

2017-01-01

Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks. PMID:28665318

Pseudorange Measurement Method Based on AIS Signals.

PubMed

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-05-22

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

Pseudorange Measurement Method Based on AIS Signals

PubMed Central

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-01-01

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system. PMID:28531153

GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation.

PubMed

Wang, Fei; Li, Hong; Lu, Mingquan

2017-06-30

Spoofing attacks are threatening the global navigation satellite system (GNSS). The maximum likelihood estimation (MLE)-based positioning technique is a direct positioning method originally developed for multipath rejection and weak signal processing. We find this method also has a potential ability for GNSS anti-spoofing since a spoofing attack that misleads the positioning and timing result will cause distortion to the MLE cost function. Based on the method, an estimation-cancellation approach is presented to detect spoofing attacks and recover the navigation solution. A statistic is derived for spoofing detection with the principle of the generalized likelihood ratio test (GLRT). Then, the MLE cost function is decomposed to further validate whether the navigation solution obtained by MLE-based positioning is formed by consistent signals. Both formulae and simulations are provided to evaluate the anti-spoofing performance. Experiments with recordings in real GNSS spoofing scenarios are also performed to validate the practicability of the approach. Results show that the method works even when the code phase differences between the spoofing and authentic signals are much less than one code chip, which can improve the availability of GNSS service greatly under spoofing attacks.

Blind estimation of reverberation time

NASA Astrophysics Data System (ADS)

Ratnam, Rama; Jones, Douglas L.; Wheeler, Bruce C.; O'Brien, William D.; Lansing, Charissa R.; Feng, Albert S.

2003-11-01

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. Many state-of-the-art audio signal processing algorithms, for example in hearing-aids and telephony, are expected to have the ability to characterize the listening environment, and turn on an appropriate processing strategy accordingly. Thus, a method for characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, a method for estimating RT without prior knowledge of sound sources or room geometry is presented. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time-constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

Online estimation of room reverberation time

NASA Astrophysics Data System (ADS)

Ratnam, Rama; Jones, Douglas L.; Wheeler, Bruce C.; Feng, Albert S.

2003-04-01

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. State-of-the-art signal processing algorithms for hearing aids are expected to have the ability to evaluate the characteristics of the listening environment and turn on an appropriate processing strategy accordingly. Thus, a method for the characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method or regression, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, we describe a method for estimating RT without prior knowledge of sound sources or room geometry. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

NASA Astrophysics Data System (ADS)

Zhou, Peng; Peng, Zhike; Chen, Shiqian; Yang, Yang; Zhang, Wenming

2018-06-01

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency (TF) pattern of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized time-frequency transform (GPTFT). The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kernel. A multi-component instantaneous frequency (IF) extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index (SCI). Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

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

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

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

Magnetic Flux Leakage Sensing and Artificial Neural Network Pattern Recognition-Based Automated Damage Detection and Quantification for Wire Rope Non-Destructive Evaluation.

PubMed

Kim, Ju-Won; Park, Seunghee

2018-01-02

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Vehicle Position Estimation Based on Magnetic Markers: Enhanced Accuracy by Compensation of Time Delays.

PubMed

Byun, Yeun-Sub; Jeong, Rag-Gyo; Kang, Seok-Won

2015-11-13

The real-time recognition of absolute (or relative) position and orientation on a network of roads is a core technology for fully automated or driving-assisted vehicles. This paper presents an empirical investigation of the design, implementation, and evaluation of a self-positioning system based on a magnetic marker reference sensing method for an autonomous vehicle. Specifically, the estimation accuracy of the magnetic sensing ruler (MSR) in the up-to-date estimation of the actual position was successfully enhanced by compensating for time delays in signal processing when detecting the vertical magnetic field (VMF) in an array of signals. In this study, the signal processing scheme was developed to minimize the effects of the distortion of measured signals when estimating the relative positional information based on magnetic signals obtained using the MSR. In other words, the center point in a 2D magnetic field contour plot corresponding to the actual position of magnetic markers was estimated by tracking the errors between pre-defined reference models and measured magnetic signals. The algorithm proposed in this study was validated by experimental measurements using a test vehicle on a pilot network of roads. From the results, the positioning error was found to be less than 0.04 m on average in an operational test.

Vehicle Position Estimation Based on Magnetic Markers: Enhanced Accuracy by Compensation of Time Delays

PubMed Central

Byun, Yeun-Sub; Jeong, Rag-Gyo; Kang, Seok-Won

2015-01-01

The real-time recognition of absolute (or relative) position and orientation on a network of roads is a core technology for fully automated or driving-assisted vehicles. This paper presents an empirical investigation of the design, implementation, and evaluation of a self-positioning system based on a magnetic marker reference sensing method for an autonomous vehicle. Specifically, the estimation accuracy of the magnetic sensing ruler (MSR) in the up-to-date estimation of the actual position was successfully enhanced by compensating for time delays in signal processing when detecting the vertical magnetic field (VMF) in an array of signals. In this study, the signal processing scheme was developed to minimize the effects of the distortion of measured signals when estimating the relative positional information based on magnetic signals obtained using the MSR. In other words, the center point in a 2D magnetic field contour plot corresponding to the actual position of magnetic markers was estimated by tracking the errors between pre-defined reference models and measured magnetic signals. The algorithm proposed in this study was validated by experimental measurements using a test vehicle on a pilot network of roads. From the results, the positioning error was found to be less than 0.04 m on average in an operational test. PMID:26580622

DOLPHIn—Dictionary Learning for Phase Retrieval

NASA Astrophysics Data System (ADS)

Tillmann, Andreas M.; Eldar, Yonina C.; Mairal, Julien

2016-12-01

We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent phase retrieval algorithms exploit underlying sparsity of the unknown signal in order to improve recovery performance. In this work, we consider such a sparse signal prior in the context of phase retrieval, when the sparsifying dictionary is not known in advance. Our algorithm jointly reconstructs the unknown signal - possibly corrupted by noise - and learns a dictionary such that each patch of the estimated image can be sparsely represented. Numerical experiments demonstrate that our approach can obtain significantly better reconstructions for phase retrieval problems with noise than methods that cannot exploit such "hidden" sparsity. Moreover, on the theoretical side, we provide a convergence result for our method.

Analysis of Synchronization Phenomena in Broadband Signals with Nonlinear Excitable Media

NASA Astrophysics Data System (ADS)

Chernihovskyi, Anton; Elger, Christian E.; Lehnertz, Klaus

2009-12-01

We apply the method of frequency-selective excitation waves in excitable media to characterize synchronization phenomena in interacting complex dynamical systems by measuring coincidence rates of induced excitations. We relax the frequency-selectivity of excitable media and demonstrate two applications of the method to signals with broadband spectra. Findings obtained from analyzing time series of coupled chaotic oscillators as well as electroencephalographic (EEG) recordings from an epilepsy patient indicate that this method can provide an alternative and complementary way to estimate the degree of phase synchronization in noisy signals.

Asymptotic Analysis Of The Total Least Squares ESPRIT Algorithm'

NASA Astrophysics Data System (ADS)

Ottersten, B. E.; Viberg, M.; Kailath, T.

1989-11-01

This paper considers the problem of estimating the parameters of multiple narrowband signals arriving at an array of sensors. Modern approaches to this problem often involve costly procedures for calculating the estimates. The ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm was recently proposed as a means for obtaining accurate estimates without requiring a costly search of the parameter space. This method utilizes an array invariance to arrive at a computationally efficient multidimensional estimation procedure. Herein, the asymptotic distribution of the estimation error is derived for the Total Least Squares (TLS) version of ESPRIT. The Cramer-Rao Bound (CRB) for the ESPRIT problem formulation is also derived and found to coincide with the variance of the asymptotic distribution through numerical examples. The method is also compared to least squares ESPRIT and MUSIC as well as to the CRB for a calibrated array. Simulations indicate that the theoretic expressions can be used to accurately predict the performance of the algorithm.

Methodology and method and apparatus for signaling with capacity optimized constellations

NASA Technical Reports Server (NTRS)

Barsoum, Maged F. (Inventor); Jones, Christopher R. (Inventor)

2011-01-01

Communication systems having transmitter, includes a coder configured to receive user bits and output encoded bits at an expanded output encoded bit rate, a mapper configured to map encoded bits to symbols in a symbol constellation, a modulator configured to generate a signal for transmission via the communication channel using symbols generated by the mapper. In addition, the receiver includes a demodulator configured to demodulate the received signal via the communication channel, a demapper configured to estimate likelihoods from the demodulated signal, a decoder that is configured to estimate decoded bits from the likelihoods generated by the demapper. Furthermore, the symbol constellation is a capacity optimized geometrically spaced symbol constellation that provides a given capacity at a reduced signal-to-noise ratio compared to a signal constellation that maximizes d.sub.min.

Spread Spectrum Signal Characteristic Estimation Using Exponential Averaging and an AD-HOC Chip rate Estimator

DTIC Science & Technology

2007-03-01

Quadrature QPSK Quadrature Phase-Shift Keying RV Random Variable SHAC Single-Hop-Observation Auto- Correlation SINR Signal-to-Interference...The fast Fourier transform ( FFT ) accumulation method and the strip spectral correlation algorithm subdivide the support region in the bi-frequency...diamond shapes, while the strip spectral correlation algorithm subdivides the region into strips. Each strip covers a number of the FFT accumulation

Particle swarm optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

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

Wang Yan; Mohanty, Soumya D.; Center for Gravitational Wave Astronomy, Department of Physics and Astronomy, University of Texas at Brownsville, 80 Fort Brown, Brownsville, Texas 78520

2010-03-15

The detection and estimation of gravitational wave signals belonging to a parameterized family of waveforms requires, in general, the numerical maximization of a data-dependent function of the signal parameters. Because of noise in the data, the function to be maximized is often highly multimodal with numerous local maxima. Searching for the global maximum then becomes computationally expensive, which in turn can limit the scientific scope of the search. Stochastic optimization is one possible approach to reducing computational costs in such applications. We report results from a first investigation of the particle swarm optimization method in this context. The method ismore » applied to a test bed motivated by the problem of detection and estimation of a binary inspiral signal. Our results show that particle swarm optimization works well in the presence of high multimodality, making it a viable candidate method for further applications in gravitational wave data analysis.« less

Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles

NASA Astrophysics Data System (ADS)

Zheng, Yuejiu; Ouyang, Minggao; Han, Xuebing; Lu, Languang; Li, Jianqiu

2018-02-01

Sate of charge (SOC) estimation is generally acknowledged as one of the most important functions in battery management system for lithium-ion batteries in new energy vehicles. Though every effort is made for various online SOC estimation methods to reliably increase the estimation accuracy as much as possible within the limited on-chip resources, little literature discusses the error sources for those SOC estimation methods. This paper firstly reviews the commonly studied SOC estimation methods from a conventional classification. A novel perspective focusing on the error analysis of the SOC estimation methods is proposed. SOC estimation methods are analyzed from the views of the measured values, models, algorithms and state parameters. Subsequently, the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation methods in new energy vehicles. Finally, with the consideration of the working conditions, choosing more reliable and applicable SOC estimation methods is discussed, and the future development of the promising online SOC estimation methods is suggested.

Kernel and divergence techniques in high energy physics separations

NASA Astrophysics Data System (ADS)

Bouř, Petr; Kůs, Václav; Franc, Jiří

2017-10-01

Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.

Measuring the electrical properties of soil using a calibrated ground-coupled GPR system

USGS Publications Warehouse

Oden, C.P.; Olhoeft, G.R.; Wright, D.L.; Powers, M.H.

2008-01-01

Traditional methods for estimating vadose zone soil properties using ground penetrating radar (GPR) include measuring travel time, fitting diffraction hyperbolae, and other methods exploiting geometry. Additional processing techniques for estimating soil properties are possible with properly calibrated GPR systems. Such calibration using ground-coupled antennas must account for the effects of the shallow soil on the antenna's response, because changing soil properties result in a changing antenna response. A prototype GPR system using ground-coupled antennas was calibrated using laboratory measurements and numerical simulations of the GPR components. Two methods for estimating subsurface properties that utilize the calibrated response were developed. First, a new nonlinear inversion algorithm to estimate shallow soil properties under ground-coupled antennas was evaluated. Tests with synthetic data showed that the inversion algorithm is well behaved across the allowed range of soil properties. A preliminary field test gave encouraging results, with estimated soil property uncertainties (????) of ??1.9 and ??4.4 mS/m for the relative dielectric permittivity and the electrical conductivity, respectively. Next, a deconvolution method for estimating the properties of subsurface reflectors with known shapes (e.g., pipes or planar interfaces) was developed. This method uses scattering matrices to account for the response of subsurface reflectors. The deconvolution method was evaluated for use with noisy data using synthetic data. Results indicate that the deconvolution method requires reflected waves with a signal/noise ratio of about 10:1 or greater. When applied to field data with a signal/noise ratio of 2:1, the method was able to estimate the reflection coefficient and relative permittivity, but the large uncertainty in this estimate precluded inversion for conductivity. ?? Soil Science Society of America.

Position Estimation Method of Medical Implanted Devices Using Estimation of Propagation Velocity inside Human Body

NASA Astrophysics Data System (ADS)

Kawasaki, Makoto; Kohno, Ryuji

Wireless communication devices in the field of medical implant, such as cardiac pacemakers and capsule endoscopes, have been studied and developed to improve healthcare systems. Especially it is very important to know the range and position of each device because it will contribute to an optimization of the transmission power. We adopt the time-based approach of position estimation using ultra wideband signals. However, the propagation velocity inside the human body differs in each tissue and each frequency. Furthermore, the human body is formed of various tissues with complex structures. For this reason, propagation velocity is different at a different point inside human body and the received signal so distorted through the channel inside human body. In this paper, we apply an adaptive template synthesis method in multipath channel for calculate the propagation time accurately based on the output of the correlator between the transmitter and the receiver. Furthermore, we propose a position estimation method using an estimation of the propagation velocity inside the human body. In addition, we show by computer simulation that the proposal method can perform accurate positioning with a size of medical implanted devices such as a medicine capsule.

Enhancement of Signal-to-noise Ratio in Natural-source Transient Magnetotelluric Data with Wavelet Transform

NASA Astrophysics Data System (ADS)

Zhang, Y.; Paulson, K. V.

For audio-frequency magnetotelluric surveys where the signals are lightning-stroke transients, the conventional Fourier transform method often fails to produce a high quality impedance tensor. An alternative approach is to use the wavelet transform method which is capable of localizing target information simultaneously in both the temporal and frequency domains. Unlike Fourier analysis that yields an average amplitude and phase, the wavelet transform produces an instantaneous estimate of the amplitude and phase of a signal. In this paper a complex well-localized wavelet, the Morlet wavelet, has been used to transform and analyze audio-frequency magnetotelluric data. With the Morlet wavelet, the magnetotelluric impedance tensor can be computed directly in the wavelet transform domain. The lightning-stroke transients are easily identified on the dilation-translation plane. Choosing those wavelet transform values where the signals are located, a higher signal-to-noise ratio estimation of the impedance tensor can be obtained. In a test using real data, the wavelet transform showed a significant improvement in the signal-to-noise ratio over the conventional Fourier transform.

An Analysis of Periodic Components in BL Lac Object S5 0716 +714 with MUSIC Method

NASA Astrophysics Data System (ADS)

Tang, J.

2012-01-01

Multiple signal classification (MUSIC) algorithms are introduced to the estimation of the period of variation of BL Lac objects.The principle of MUSIC spectral analysis method and theoretical analysis of the resolution of frequency spectrum using analog signals are included. From a lot of literatures, we have collected a lot of effective observation data of BL Lac object S5 0716 + 714 in V, R, I bands from 1994 to 2008. The light variation periods of S5 0716 +714 are obtained by means of the MUSIC spectral analysis method and periodogram spectral analysis method. There exist two major periods: (3.33±0.08) years and (1.24±0.01) years for all bands. The estimation of the period of variation of the algorithm based on the MUSIC spectral analysis method is compared with that of the algorithm based on the periodogram spectral analysis method. It is a super-resolution algorithm with small data length, and could be used to detect the period of variation of weak signals.

Multi-Phenomenological Analysis of the 12 August 2015 Tianjin, China Chemical Explosion

NASA Astrophysics Data System (ADS)

Pasyanos, M.; Kim, K.; Park, J.; Stump, B. W.; Hayward, C.; Che, I. Y.; Zhao, L.; Myers, S. C.

2016-12-01

We perform a multi-phenomenological analysis of the massive near-surface chemical explosions that occurred in Tianjin, China on 12 August 2015. A recent assessment of these events was performed by Zhao et al. (2016) using local (< 100 km) seismic data. This study considers a regional assessment of the same sequence in the absence of having any local data. We provide additional insight by combining regional seismic analysis with the use of infrasound signals and an assessment of the event crater. Event locations using infrasound signals recorded at Korean and IMS arrays are estimated based on the Bayesian Infrasonic Source Location (BISL) method (Modrak et al., 2010), and improved with azimuthal corrections using a raytracing (Blom and Waxler, 2012) and the Ground-to-Space (G2S) atmospheric models (Drob et al., 2003). The location information provided from the infrasound signals is then merged with the regional seismic arrivals to produce a joint event location. The yields of the events are estimated from seismic and infrasonic observations. Seismic waveform envelope method (Pasyanos et al., 2012) including the free surface effect (Pasyanos and Ford, 2015) is applied to regional seismic signals. Waveform inversion method (Kim and Rodgers, 2016) is used for infrasound signals. A combination of the seismic and acoustic signals can provide insights on the energy partitioning and break the tradeoffs between the yield and the depth/height of explosions, resulting in a more robust estimation of event yield. The yield information from the different phenomenologies are combined through the use of likelihood functions.

Derivation of respiration rate from ambulatory ECG and PPG using Ensemble Empirical Mode Decomposition: Comparison and fusion.

PubMed

Orphanidou, Christina

2017-02-01

A new method for extracting the respiratory rate from ECG and PPG obtained via wearable sensors is presented. The proposed technique employs Ensemble Empirical Mode Decomposition in order to identify the respiration "mode" from the noise-corrupted Heart Rate Variability/Pulse Rate Variability and Amplitude Modulation signals extracted from ECG and PPG signals. The technique was validated with respect to a Respiratory Impedance Pneumography (RIP) signal using the mean absolute and the average relative errors for a group ambulatory hospital patients. We compared approaches using single respiration-induced modulations on the ECG and PPG signals with approaches fusing the different modulations. Additionally, we investigated whether the presence of both the simultaneously recorded ECG and PPG signals provided a benefit in the overall system performance. Our method outperformed state-of-the-art ECG- and PPG-based algorithms and gave the best results over the whole database with a mean error of 1.8bpm for 1min estimates when using the fused ECG modulations, which was a relative error of 10.3%. No statistically significant differences were found when comparing the ECG-, PPG- and ECG/PPG-based approaches, indicating that the PPG can be used as a valid alternative to the ECG for applications using wearable sensors. While the presence of both the ECG and PPG signals did not provide an improvement in the estimation error, it increased the proportion of windows for which an estimate was obtained by at least 9%, indicating that the use of two simultaneously recorded signals might be desirable in high-acuity cases where an RR estimate is required more frequently. Copyright © 2016 Elsevier Ltd. All rights reserved.

Non-linear auto-regressive models for cross-frequency coupling in neural time series

PubMed Central

Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

2017-01-01

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

Parameter estimation of a three-axis spacecraft simulator using recursive least-squares approach with tracking differentiator and Extended Kalman Filter

NASA Astrophysics Data System (ADS)

Xu, Zheyao; Qi, Naiming; Chen, Yukun

2015-12-01

Spacecraft simulators are widely used to study the dynamics, guidance, navigation, and control of a spacecraft on the ground. A spacecraft simulator can have three rotational degrees of freedom by using a spherical air-bearing to simulate a frictionless and micro-gravity space environment. The moment of inertia and center of mass are essential for control system design of ground-based three-axis spacecraft simulators. Unfortunately, they cannot be known precisely. This paper presents two approaches, i.e. a recursive least-squares (RLS) approach with tracking differentiator (TD) and Extended Kalman Filter (EKF) method, to estimate inertia parameters. The tracking differentiator (TD) filter the noise coupled with the measured signals and generate derivate of the measured signals. Combination of two TD filters in series obtains the angular accelerations that are required in RLS (TD-TD-RLS). Another method that does not need to estimate the angular accelerations is using the integrated form of dynamics equation. An extended TD (ETD) filter which can also generate the integration of the function of signals is presented for RLS (denoted as ETD-RLS). States and inertia parameters are estimated simultaneously using EKF. The observability is analyzed. All proposed methods are illustrated by simulations and experiments.

Estimating statistical isotropy violation in CMB due to non-circular beam and complex scan in minutes

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

Pant, Nidhi; Das, Santanu; Mitra, Sanjit

Mild, unavoidable deviations from circular-symmetry of instrumental beams along with scan strategy can give rise to measurable Statistical Isotropy (SI) violation in Cosmic Microwave Background (CMB) experiments. If not accounted properly, this spurious signal can complicate the extraction of other SI violation signals (if any) in the data. However, estimation of this effect through exact numerical simulation is computationally intensive and time consuming. A generalized analytical formalism not only provides a quick way of estimating this signal, but also gives a detailed understanding connecting the leading beam anisotropy components to a measurable BipoSH characterisation of SI violation. In this paper,more » we provide an approximate generic analytical method for estimating the SI violation generated due to a non-circular (NC) beam and arbitrary scan strategy, in terms of the Bipolar Spherical Harmonic (BipoSH) spectra. Our analytical method can predict almost all the features introduced by a NC beam in a complex scan and thus reduces the need for extensive numerical simulation worth tens of thousands of CPU hours into minutes long calculations. As an illustrative example, we use WMAP beams and scanning strategy to demonstrate the easability, usability and efficiency of our method. We test all our analytical results against that from exact numerical simulations.« less

Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario

NASA Astrophysics Data System (ADS)

Rajagopal, R.; Challa, Subhash; Faruqi, Farhan A.; Rao, P. R.

1997-06-01

In a realistic passive sonar environment, the received signal consists of multipath arrivals from closely separated moving targets. The signals are contaminated by spatially correlated noise. The differential MUSIC has been proposed to estimate the DOAs in such a scenario. This method estimates the 'noise subspace' in order to estimate the DOAs. However, the 'noise subspace' estimate has to be updated as and when new data become available. In order to save the computational costs, a new adaptive noise subspace estimation algorithm is proposed in this paper. The salient features of the proposed algorithm are: (1) Noise subspace estimation is done by QR decomposition of the difference matrix which is formed from the data covariance matrix. Thus, as compared to standard eigen-decomposition based methods which require O(N3) computations, the proposed method requires only O(N2) computations. (2) Noise subspace is updated by updating the QR decomposition. (3) The proposed algorithm works in a realistic sonar environment. In the second part of the paper, the estimated bearing values are used to track multiple targets. In order to achieve this, the nonlinear system/linear measurement extended Kalman filtering proposed is applied. Computer simulation results are also presented to support the theory.

Estimation of crossing conflict at signalized intersection using high-resolution traffic data : final report.

DOT National Transportation Integrated Search

2017-03-01

This project explores the possibility of using high-resolution traffic signal data to evaluate intersection safety. : Traditional methods using historical crash data collected from infrequently and randomly occurring vehicle : collisions can require ...

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

NASA Astrophysics Data System (ADS)

Wang, Pan-Pan; Yu, Qiang; Hu, Yong-Jun; Miao, Chang-Xin

2017-11-01

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Methods and systems for low frequency seismic and infrasound detection of geo-pressure transition zones

DOEpatents

Shook, G. Michael; LeRoy, Samuel D.; Benzing, William M.

2006-07-18

Methods for determining the existence and characteristics of a gradational pressurized zone within a subterranean formation are disclosed. One embodiment involves employing an attenuation relationship between a seismic response signal and increasing wavelet wavelength, which relationship may be used to detect a gradational pressurized zone and/or determine characteristics thereof. In another embodiment, a method for analyzing data contained within a response signal for signal characteristics that may change in relation to the distance between an input signal source and the gradational pressurized zone is disclosed. In a further embodiment, the relationship between response signal wavelet frequency and comparative amplitude may be used to estimate an optimal wavelet wavelength or range of wavelengths used for data processing or input signal selection. Systems for seismic exploration and data analysis for practicing the above-mentioned method embodiments are also disclosed.

Accurate determination of brain metabolite concentrations using ERETIC as external reference.

PubMed

Zoelch, Niklaus; Hock, Andreas; Heinzer-Schweizer, Susanne; Avdievitch, Nikolai; Henning, Anke

2017-08-01

Magnetic Resonance Spectroscopy (MRS) can provide in vivo metabolite concentrations in standard concentration units if a reliable reference signal is available. For 1 H MRS in the human brain, typically the signal from the tissue water is used as the (internal) reference signal. However, a concentration determination based on the tissue water signal most often requires a reliable estimate of the water concentration present in the investigated tissue. Especially in clinically interesting cases, this estimation might be difficult. To avoid assumptions about the water in the investigated tissue, the Electric REference To access In vivo Concentrations (ERETIC) method has been proposed. In this approach, the metabolite signal is compared with a reference signal acquired in a phantom and potential coil-loading differences are corrected using a synthetic reference signal. The aim of this study, conducted with a transceiver quadrature head coil, was to increase the accuracy of the ERETIC method by correcting the influence of spatial B 1 inhomogeneities and to simplify the quantification with ERETIC by incorporating an automatic phase correction for the ERETIC signal. Transmit field ( B1+) differences are minimized with a volume-selective power optimization, whereas reception sensitivity changes are corrected using contrast-minimized images of the brain and by adapting the voxel location in the phantom measurement closely to the position measured in vivo. By applying the proposed B 1 correction scheme, the mean metabolite concentrations determined with ERETIC in 21 healthy subjects at three different positions agree with concentrations derived with the tissue water signal as reference. In addition, brain water concentrations determined with ERETIC were in agreement with estimations derived using tissue segmentation and literature values for relative water densities. Based on the results, the ERETIC method presented here is a valid tool to derive in vivo metabolite concentration, with potential advantages compared with internal water referencing in diseased tissue. Copyright © 2017 John Wiley & Sons, Ltd.

Super-resolution imaging using multi- electrode CMUTs: theoretical design and simulation using point targets.

PubMed

You, Wei; Cretu, Edmond; Rohling, Robert

2013-11-01

This paper investigates a low computational cost, super-resolution ultrasound imaging method that leverages the asymmetric vibration mode of CMUTs. Instead of focusing on the broadband received signal on the entire CMUT membrane, we utilize the differential signal received on the left and right part of the membrane obtained by a multi-electrode CMUT structure. The differential signal reflects the asymmetric vibration mode of the CMUT cell excited by the nonuniform acoustic pressure field impinging on the membrane, and has a resonant component in immersion. To improve the resolution, we propose an imaging method as follows: a set of manifold matrices of CMUT responses for multiple focal directions are constructed off-line with a grid of hypothetical point targets. During the subsequent imaging process, the array sequentially steers to multiple angles, and the amplitudes (weights) of all hypothetical targets at each angle are estimated in a maximum a posteriori (MAP) process with the manifold matrix corresponding to that angle. Then, the weight vector undergoes a directional pruning process to remove the false estimation at other angles caused by the side lobe energy. Ultrasound imaging simulation is performed on ring and linear arrays with a simulation program adapted with a multi-electrode CMUT structure capable of obtaining both average and differential received signals. Because the differential signals from all receiving channels form a more distinctive temporal pattern than the average signals, better MAP estimation results are expected than using the average signals. The imaging simulation shows that using differential signals alone or in combination with the average signals produces better lateral resolution than the traditional phased array or using the average signals alone. This study is an exploration into the potential benefits of asymmetric CMUT responses for super-resolution imaging.

Spectral and correlation analysis with applications to middle-atmosphere radars

NASA Technical Reports Server (NTRS)

Rastogi, Prabhat K.

1989-01-01

The correlation and spectral analysis methods for uniformly sampled stationary random signals, estimation of their spectral moments, and problems arising due to nonstationary are reviewed. Some of these methods are already in routine use in atmospheric radar experiments. Other methods based on the maximum entropy principle and time series models have been used in analyzing data, but are just beginning to receive attention in the analysis of radar signals. These methods are also briefly discussed.

Coherent-subspace array processing based on wavelet covariance: an application to broad-band, seismo-volcanic signals

NASA Astrophysics Data System (ADS)

Saccorotti, G.; Nisii, V.; Del Pezzo, E.

2008-07-01

Long-Period (LP) and Very-Long-Period (VLP) signals are the most characteristic seismic signature of volcano dynamics, and provide important information about the physical processes occurring in magmatic and hydrothermal systems. These events are usually characterized by sharp spectral peaks, which may span several frequency decades, by emergent onsets, and by a lack of clear S-wave arrivals. These two latter features make both signal detection and location a challenging task. In this paper, we propose a processing procedure based on Continuous Wavelet Transform of multichannel, broad-band data to simultaneously solve the signal detection and location problems. Our method consists of two steps. First, we apply a frequency-dependent threshold to the estimates of the array-averaged WCO in order to locate the time-frequency regions spanned by coherent arrivals. For these data, we then use the time-series of the complex wavelet coefficients for deriving the elements of the spatial Cross-Spectral Matrix. From the eigenstructure of this matrix, we eventually estimate the kinematic signals' parameters using the MUltiple SIgnal Characterization (MUSIC) algorithm. The whole procedure greatly facilitates the detection and location of weak, broad-band signals, in turn avoiding the time-frequency resolution trade-off and frequency leakage effects which affect conventional covariance estimates based upon Windowed Fourier Transform. The method is applied to explosion signals recorded at Stromboli volcano by either a short-period, small aperture antenna, or a large-aperture, broad-band network. The LP (0.2 < T < 2s) components of the explosive signals are analysed using data from the small-aperture array and under the plane-wave assumption. In this manner, we obtain a precise time- and frequency-localization of the directional properties for waves impinging at the array. We then extend the wavefield decomposition method using a spherical wave front model, and analyse the VLP components (T > 2s) of the explosion recordings from the broad-band network. Source locations obtained this way are fully compatible with those retrieved from application of more traditional (and computationally expensive) time-domain techniques, such as the Radial Semblance method.

Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estimators.

PubMed

Lin, Jen-Jen; Cheng, Jung-Yu; Huang, Li-Fei; Lin, Ying-Hsiu; Wan, Yung-Liang; Tsui, Po-Hsiang

2017-05-01

The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE 1 and MLE 2 , respectively), and Greenwood approximation (MLE gw ) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE 1 , the MLE 2 and MLE gw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE 2 and MLE gw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE 2 and MLE gw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Improvement of the fringe analysis algorithm for wavelength scanning interferometry based on filter parameter optimization.

PubMed

Zhang, Tao; Gao, Feng; Muhamedsalih, Hussam; Lou, Shan; Martin, Haydn; Jiang, Xiangqian

2018-03-20

The phase slope method which estimates height through fringe pattern frequency and the algorithm which estimates height through the fringe phase are the fringe analysis algorithms widely used in interferometry. Generally they both extract the phase information by filtering the signal in frequency domain after Fourier transform. Among the numerous papers in the literature about these algorithms, it is found that the design of the filter, which plays an important role, has never been discussed in detail. This paper focuses on the filter design in these algorithms for wavelength scanning interferometry (WSI), trying to optimize the parameters to acquire the optimal results. The spectral characteristics of the interference signal are analyzed first. The effective signal is found to be narrow-band (near single frequency), and the central frequency is calculated theoretically. Therefore, the position of the filter pass-band is determined. The width of the filter window is optimized with the simulation to balance the elimination of the noise and the ringing of the filter. Experimental validation of the approach is provided, and the results agree very well with the simulation. The experiment shows that accuracy can be improved by optimizing the filter design, especially when the signal quality, i.e., the signal noise ratio (SNR), is low. The proposed method also shows the potential of improving the immunity to the environmental noise by adapting the signal to acquire the optimal results through designing an adaptive filter once the signal SNR can be estimated accurately.

A Robust Wrap Reduction Algorithm for Fringe Projection Profilometry and Applications in Magnetic Resonance Imaging.

PubMed

Arevalillo-Herraez, Miguel; Cobos, Maximo; Garcia-Pineda, Miguel

2017-03-01

In this paper, we present an effective algorithm to reduce the number of wraps in a 2D phase signal provided as input. The technique is based on an accurate estimate of the fundamental frequency of a 2D complex signal with the phase given by the input, and the removal of a dependent additive term from the phase map. Unlike existing methods based on the discrete Fourier transform (DFT), the frequency is computed by using noise-robust estimates that are not restricted to integer values. Then, to deal with the problem of a non-integer shift in the frequency domain, an equivalent operation is carried out on the original phase signal. This consists of the subtraction of a tilted plane whose slope is computed from the frequency, followed by a re-wrapping operation. The technique has been exhaustively tested on fringe projection profilometry (FPP) and magnetic resonance imaging (MRI) signals. In addition, the performance of several frequency estimation methods has been compared. The proposed methodology is particularly effective on FPP signals, showing a higher performance than the state-of-the-art wrap reduction approaches. In this context, it contributes to canceling the carrier effect at the same time as it eliminates any potential slope that affects the entire signal. Its effectiveness on other carrier-free phase signals, e.g., MRI, is limited to the case that inherent slopes are present in the phase data.

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

USGS Publications Warehouse

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.

Joint Carrier-Phase Synchronization and LDPC Decoding

NASA Technical Reports Server (NTRS)

Simon, Marvin; Valles, Esteban

2009-01-01

A method has been proposed to increase the degree of synchronization of a radio receiver with the phase of a suppressed carrier signal modulated with a binary- phase-shift-keying (BPSK) or quaternary- phase-shift-keying (QPSK) signal representing a low-density parity-check (LDPC) code. This method is an extended version of the method described in Using LDPC Code Constraints to Aid Recovery of Symbol Timing (NPO-43112), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 54. Both methods and the receiver architectures in which they would be implemented belong to a class of timing- recovery methods and corresponding receiver architectures characterized as pilotless in that they do not require transmission and reception of pilot signals. The proposed method calls for the use of what is known in the art as soft decision feedback to remove the modulation from a replica of the incoming signal prior to feeding this replica to a phase-locked loop (PLL) or other carrier-tracking stage in the receiver. Soft decision feedback refers to suitably processed versions of intermediate results of iterative computations involved in the LDPC decoding process. Unlike a related prior method in which hard decision feedback (the final sequence of decoded symbols) is used to remove the modulation, the proposed method does not require estimation of the decoder error probability. In a basic digital implementation of the proposed method, the incoming signal (having carrier phase theta theta (sub c) plus noise would first be converted to inphase (I) and quadrature (Q) baseband signals by mixing it with I and Q signals at the carrier frequency [wc/(2 pi)] generated by a local oscillator. The resulting demodulated signals would be processed through one-symbol-period integrate and- dump filters, the outputs of which would be sampled and held, then multiplied by a soft-decision version of the baseband modulated signal. The resulting I and Q products consist of terms proportional to the cosine and sine of the carrier phase cc as well as correlated noise components. These products would be fed as inputs to a digital PLL that would include a number-controlled oscillator (NCO), which provides an estimate of the carrier phase, theta(sub c).

Parameter estimation in linear models of the human operator in a closed loop with application of deterministic test signals

NASA Technical Reports Server (NTRS)

Vanlunteren, A.; Stassen, H. G.

1973-01-01

Parameter estimation techniques are discussed with emphasis on unbiased estimates in the presence of noise. A distinction between open and closed loop systems is made. A method is given based on the application of external forcing functions consisting of a sun of sinusoids; this method is thus based on the estimation of Fourier coefficients and is applicable for models with poles and zeros in open and closed loop systems.

Evaluation of Simultaneous Multisine Excitation of the Joined Wing SensorCraft Aeroelastic Wind Tunnel Model

NASA Technical Reports Server (NTRS)

Heeg, Jennifer; Morelli, Eugene A.

2011-01-01

Multiple mutually orthogonal signals comprise excitation data sets for aeroservoelastic system identification. A multisine signal is a sum of harmonic sinusoid components. A set of these signals is made orthogonal by distribution of the frequency content such that each signal contains unique frequencies. This research extends the range of application of an excitation method developed for stability and control flight testing to aeroservoelastic modeling from wind tunnel testing. Wind tunnel data for the Joined Wing SensorCraft model validates this method, demonstrating that these signals applied simultaneously reproduce the frequency response estimates achieved from one-at-a-time excitation.

Signal-to-noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images.

PubMed

Sim, K S; Yeap, Z X; Tso, C P

2016-11-01

An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

Blind identification of nonlinear models with non-Gaussian inputs

NASA Astrophysics Data System (ADS)

Prakriya, Shankar; Pasupathy, Subbarayan; Hatzinakos, Dimitrios

1995-12-01

Some methods are proposed for the blind identification of finite-order discrete-time nonlinear models with non-Gaussian circular inputs. The nonlinear models consist of two finite memory linear time invariant (LTI) filters separated by a zero-memory nonlinearity (ZMNL) of the polynomial type (the LTI-ZMNL-LTI models). The linear subsystems are allowed to be of non-minimum phase (NMP). The methods base their estimates of the impulse responses on slices of the N plus 1th order polyspectra of the output sequence. It is shown that the identification of LTI-ZMNL systems requires only a 1-D moment or polyspectral slice. The coefficients of the ZMNL are not estimated, and need not be known. The order of the nonlinearity can, in theory, be estimated from the received signal. These methods possess several noise and interference suppression characteristics, and have applications in modeling nonlinearly amplified QAM/QPSK signals in digital satellite and microwave communications.

Sparse Bayesian learning for DOA estimation with mutual coupling.

PubMed

Dai, Jisheng; Hu, Nan; Xu, Weichao; Chang, Chunqi

2015-10-16

Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise.

Statistical study of generalized nonlinear phase step estimation methods in phase-shifting interferometry.

PubMed

Langoju, Rajesh; Patil, Abhijit; Rastogi, Pramod

2007-11-20

Signal processing methods based on maximum-likelihood theory, discrete chirp Fourier transform, and spectral estimation methods have enabled accurate measurement of phase in phase-shifting interferometry in the presence of nonlinear response of the piezoelectric transducer to the applied voltage. We present the statistical study of these generalized nonlinear phase step estimation methods to identify the best method by deriving the Cramér-Rao bound. We also address important aspects of these methods for implementation in practical applications and compare the performance of the best-identified method with other bench marking algorithms in the presence of harmonics and noise.

Artifacts in Digital Coincidence Timing

PubMed Central

Moses, W. W.; Peng, Q.

2014-01-01

Digital methods are becoming increasingly popular for measuring time differences, and are the de facto standard in PET cameras. These methods usually include a master system clock and a (digital) arrival time estimate for each detector that is obtained by comparing the detector output signal to some reference portion of this clock (such as the rising edge). Time differences between detector signals are then obtained by subtracting the digitized estimates from a detector pair. A number of different methods can be used to generate the digitized arrival time of the detector output, such as sending a discriminator output into a time to digital converter (TDC) or digitizing the waveform and applying a more sophisticated algorithm to extract a timing estimator. All measurement methods are subject to error, and one generally wants to minimize these errors and so optimize the timing resolution. A common method for optimizing timing methods is to measure the coincidence timing resolution between two timing signals whose time difference should be constant (such as detecting gammas from positron annihilation) and selecting the method that minimizes the width of the distribution (i.e., the timing resolution). Unfortunately, a common form of error (a nonlinear transfer function) leads to artifacts that artificially narrow this resolution, which can lead to erroneous selection of the “optimal” method. The purpose of this note is to demonstrate the origin of this artifact and suggest that caution should be used when optimizing time digitization systems solely on timing resolution minimization. PMID:25321885

Artifacts in digital coincidence timing

DOE PAGES

Moses, W. W.; Peng, Q.

2014-10-16

Digital methods are becoming increasingly popular for measuring time differences, and are the de facto standard in PET cameras. These methods usually include a master system clock and a (digital) arrival time estimate for each detector that is obtained by comparing the detector output signal to some reference portion of this clock (such as the rising edge). Time differences between detector signals are then obtained by subtracting the digitized estimates from a detector pair. A number of different methods can be used to generate the digitized arrival time of the detector output, such as sending a discriminator output into amore » time to digital converter (TDC) or digitizing the waveform and applying a more sophisticated algorithm to extract a timing estimator.All measurement methods are subject to error, and one generally wants to minimize these errors and so optimize the timing resolution. A common method for optimizing timing methods is to measure the coincidence timing resolution between two timing signals whose time difference should be constant (such as detecting gammas from positron annihilation) and selecting the method that minimizes the width of the distribution (i.e. the timing resolution). Unfortunately, a common form of error (a nonlinear transfer function) leads to artifacts that artificially narrow this resolution, which can lead to erroneous selection of the 'optimal' method. In conclusion, the purpose of this note is to demonstrate the origin of this artifact and suggest that caution should be used when optimizing time digitization systems solely on timing resolution minimization.« less

Artifacts in digital coincidence timing

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

Moses, W. W.; Peng, Q.

Digital methods are becoming increasingly popular for measuring time differences, and are the de facto standard in PET cameras. These methods usually include a master system clock and a (digital) arrival time estimate for each detector that is obtained by comparing the detector output signal to some reference portion of this clock (such as the rising edge). Time differences between detector signals are then obtained by subtracting the digitized estimates from a detector pair. A number of different methods can be used to generate the digitized arrival time of the detector output, such as sending a discriminator output into amore » time to digital converter (TDC) or digitizing the waveform and applying a more sophisticated algorithm to extract a timing estimator.All measurement methods are subject to error, and one generally wants to minimize these errors and so optimize the timing resolution. A common method for optimizing timing methods is to measure the coincidence timing resolution between two timing signals whose time difference should be constant (such as detecting gammas from positron annihilation) and selecting the method that minimizes the width of the distribution (i.e. the timing resolution). Unfortunately, a common form of error (a nonlinear transfer function) leads to artifacts that artificially narrow this resolution, which can lead to erroneous selection of the 'optimal' method. In conclusion, the purpose of this note is to demonstrate the origin of this artifact and suggest that caution should be used when optimizing time digitization systems solely on timing resolution minimization.« less

One-Channel Surface Electromyography Decomposition for Muscle Force Estimation.

PubMed

Sun, Wentao; Zhu, Jinying; Jiang, Yinlai; Yokoi, Hiroshi; Huang, Qiang

2018-01-01

Estimating muscle force by surface electromyography (sEMG) is a non-invasive and flexible way to diagnose biomechanical diseases and control assistive devices such as prosthetic hands. To estimate muscle force using sEMG, a supervised method is commonly adopted. This requires simultaneous recording of sEMG signals and muscle force measured by additional devices to tune the variables involved. However, recording the muscle force of the lost limb of an amputee is challenging, and the supervised method has limitations in this regard. Although the unsupervised method does not require muscle force recording, it suffers from low accuracy due to a lack of reference data. To achieve accurate and easy estimation of muscle force by the unsupervised method, we propose a decomposition of one-channel sEMG signals into constituent motor unit action potentials (MUAPs) in two steps: (1) learning an orthogonal basis of sEMG signals through reconstruction independent component analysis; (2) extracting spike-like MUAPs from the basis vectors. Nine healthy subjects were recruited to evaluate the accuracy of the proposed approach in estimating muscle force of the biceps brachii. The results demonstrated that the proposed approach based on decomposed MUAPs explains more than 80% of the muscle force variability recorded at an arbitrary force level, while the conventional amplitude-based approach explains only 62.3% of this variability. With the proposed approach, we were also able to achieve grip force control of a prosthetic hand, which is one of the most important clinical applications of the unsupervised method. Experiments on two trans-radial amputees indicated that the proposed approach improves the performance of the prosthetic hand in grasping everyday objects.

Novel Image Quality Control Systems(Add-On). Innovative Computational Methods for Inverse Problems in Optical and SAR Imaging

DTIC Science & Technology

2007-02-28

Iterative Ultrasonic Signal and Image Deconvolution for Estimation of the Complex Medium Response, International Journal of Imaging Systems and...1767-1782, 2006. 31. Z. Mu, R. Plemmons, and P. Santago. Iterative Ultrasonic Signal and Image Deconvolution for Estimation of the Complex...rigorous mathematical and computational research on inverse problems in optical imaging of direct interest to the Army and also the intelligence agencies

[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].

PubMed

Schmidt, K; Witte, H

1999-11-01

Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.

Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking

NASA Technical Reports Server (NTRS)

Park, Han G. (Inventor); Zak, Michail (Inventor); James, Mark L. (Inventor); Mackey, Ryan M. E. (Inventor)

2003-01-01

A general method of anomaly detection from time-correlated sensor data is disclosed. Multiple time-correlated signals are received. Their cross-signal behavior is compared against a fixed library of invariants. The library is constructed during a training process, which is itself data-driven using the same time-correlated signals. The method is applicable to a broad class of problems and is designed to respond to any departure from normal operation, including faults or events that lie outside the training envelope.

An algorithm for separation of mixed sparse and Gaussian sources

PubMed Central

Akkalkotkar, Ameya

2017-01-01

Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition. PMID:28414814

An algorithm for separation of mixed sparse and Gaussian sources.

PubMed

Akkalkotkar, Ameya; Brown, Kevin Scott

2017-01-01

Independent component analysis (ICA) is a ubiquitous method for decomposing complex signal mixtures into a small set of statistically independent source signals. However, in cases in which the signal mixture consists of both nongaussian and Gaussian sources, the Gaussian sources will not be recoverable by ICA and will pollute estimates of the nongaussian sources. Therefore, it is desirable to have methods for mixed ICA/PCA which can separate mixtures of Gaussian and nongaussian sources. For mixtures of purely Gaussian sources, principal component analysis (PCA) can provide a basis for the Gaussian subspace. We introduce a new method for mixed ICA/PCA which we call Mixed ICA/PCA via Reproducibility Stability (MIPReSt). Our method uses a repeated estimations technique to rank sources by reproducibility, combined with decomposition of multiple subsamplings of the original data matrix. These multiple decompositions allow us to assess component stability as the size of the data matrix changes, which can be used to determinine the dimension of the nongaussian subspace in a mixture. We demonstrate the utility of MIPReSt for signal mixtures consisting of simulated sources and real-word (speech) sources, as well as mixture of unknown composition.

Analysis of Ion Composition Estimation Accuracy for Incoherent Scatter Radars

NASA Astrophysics Data System (ADS)

Martínez Ledesma, M.; Diaz, M. A.

2017-12-01

The Incoherent Scatter Radar (ISR) is one of the most powerful sounding methods developed to estimate the Ionosphere. This radar system determines the plasma parameters by sending powerful electromagnetic pulses to the Ionosphere and analyzing the received backscatter. This analysis provides information about parameters such as electron and ion temperatures, electron densities, ion composition, and ion drift velocities. Nevertheless in some cases the ISR analysis has ambiguities in the determination of the plasma characteristics. It is of particular relevance the ion composition and temperature ambiguity obtained between the F1 and the lower F2 layers. In this case very similar signals are obtained with different mixtures of molecular ions (NO2+ and O2+) and atomic oxygen ions (O+), and consequently it is not possible to completely discriminate between them. The most common solution to solve this problem is the use of empirical or theoretical models of the ionosphere in the fitting of ambiguous data. More recent works take use of parameters estimated from the Plasma Line band of the radar to reduce the number of parameters to determine. In this work we propose to determine the error estimation of the ion composition ambiguity when using Plasma Line electron density measurements. The sensibility of the ion composition estimation has been also calculated depending on the accuracy of the ionospheric model, showing that the correct estimation is highly dependent on the capacity of the model to approximate the real values. Monte Carlo simulations of data fitting at different signal to noise (SNR) ratios have been done to obtain valid and invalid estimation probability curves. This analysis provides a method to determine the probability of erroneous estimation for different signal fluctuations. Also it can be used as an empirical method to compare the efficiency of the different algorithms and methods on when solving the ion composition ambiguity.

Parameter estimates in binary black hole collisions using neural networks

NASA Astrophysics Data System (ADS)

Carrillo, M.; Gracia-Linares, M.; González, J. A.; Guzmán, F. S.

2016-10-01

We present an algorithm based on artificial neural networks (ANNs), that estimates the mass ratio in a binary black hole collision out of given gravitational wave (GW) strains. In this analysis, the ANN is trained with a sample of GW signals generated with numerical simulations. The effectiveness of the algorithm is evaluated with GWs generated also with simulations for given mass ratios unknown to the ANN. We measure the accuracy of the algorithm in the interpolation and extrapolation regimes. We present the results for noise free signals and signals contaminated with Gaussian noise, in order to foresee the dependence of the method accuracy in terms of the signal to noise ratio.

Comparative study of signalling methods for high-speed backplane transceiver

NASA Astrophysics Data System (ADS)

Wu, Kejun

2017-11-01

A combined analysis of transient simulation and statistical method is proposed for comparative study of signalling methods applied to high-speed backplane transceivers. This method enables fast and accurate signal-to-noise ratio and symbol error rate estimation of a serial link based on a four-dimension design space, including channel characteristics, noise scenarios, equalisation schemes, and signalling methods. The proposed combined analysis method chooses an efficient sampling size for performance evaluation. A comparative study of non-return-to-zero (NRZ), PAM-4, and four-phase shifted sinusoid symbol (PSS-4) using parameterised behaviour-level simulation shows PAM-4 and PSS-4 has substantial advantages over conventional NRZ in most of the cases. A comparison between PAM-4 and PSS-4 shows PAM-4 gets significant bit error rate degradation when noise level is enhanced.

A double-gaussian, percentile-based method for estimating maximum blood flow velocity.

PubMed

Marzban, Caren; Illian, Paul R; Morison, David; Mourad, Pierre D

2013-11-01

Transcranial Doppler sonography allows for the estimation of blood flow velocity, whose maximum value, especially at systole, is often of clinical interest. Given that observed values of flow velocity are subject to noise, a useful notion of "maximum" requires a criterion for separating the signal from the noise. All commonly used criteria produce a point estimate (ie, a single value) of maximum flow velocity at any time and therefore convey no information on the distribution or uncertainty of flow velocity. This limitation has clinical consequences especially for patients in vasospasm, whose largest flow velocities can be difficult to measure. Therefore, a method for estimating flow velocity and its uncertainty is desirable. A gaussian mixture model is used to separate the noise from the signal distribution. The time series of a given percentile of the latter, then, provides a flow velocity envelope. This means of estimating the flow velocity envelope naturally allows for displaying several percentiles (e.g., 95th and 99th), thereby conveying uncertainty in the highest flow velocity. Such envelopes were computed for 59 patients and were shown to provide reasonable and useful estimates of the largest flow velocities compared to a standard algorithm. Moreover, we found that the commonly used envelope was generally consistent with the 90th percentile of the signal distribution derived via the gaussian mixture model. Separating the observed distribution of flow velocity into a noise component and a signal component, using a double-gaussian mixture model, allows for the percentiles of the latter to provide meaningful measures of the largest flow velocities and their uncertainty.

Blind Source Separation of Seismic Events with Independent Component Analysis: CTBT related exercise

NASA Astrophysics Data System (ADS)

Rozhkov, Mikhail; Kitov, Ivan

2015-04-01

Blind Source Separation (BSS) methods used in signal recovery applications are attractive for they use minimal a priori information about the signals they are dealing with. Homomorphic deconvolution and cepstrum estimation are probably the only methods used in certain extent in CTBT applications that can be attributed to the given branch of technology. However Expert Technical Analysis (ETA) conducted in CTBTO to improve the estimated values for the standard signal and event parameters according to the Protocol to the CTBT may face problems which cannot be resolved with certified CTBTO applications and may demand specific techniques not presently used. The problem to be considered within the ETA framework is the unambiguous separation of signals with close arrival times. Here, we examine two scenarios of interest: (1) separation of two almost co-located explosions conducted within fractions of seconds, and (2) extraction of explosion signals merged with wavetrains from strong earthquake. The importance of resolving the problem related to case 1 is connected with the correct explosion yield estimation. Case 2 is a well-known scenario of conducting clandestine nuclear tests. While the first case can be approached somehow with the means of cepstral methods, the second case can hardly be resolved with the conventional methods implemented at the International Data Centre, especially if the signals have close slowness and azimuth. Independent Component Analysis (in its FastICA implementation) implying non-Gaussianity of the underlying processes signal's mixture is a blind source separation method that we apply to resolve the mentioned above problems. We have tested this technique with synthetic waveforms, seismic data from DPRK explosions and mining blasts conducted within East-European platform as well as with signals from strong teleseismic events (Sumatra, April 2012 Mw=8.6, and Tohoku, March 2011 Mw=9.0 earthquakes). The data was recorded by seismic arrays of the International Monitoring System of CTBTO and by small-aperture seismic array Mikhnevo (MHVAR) operated by the Institute of Geosphere Dynamics, Russian Academy of Sciences. Our approach demonstrated a good ability of separation of seismic sources with very close origin times and locations (hundreds of meters), and/or having close arrival times (fractions of seconds), and recovering their waveforms from the mixture. Perspectives and limitations of the method are discussed.

An Innovations-Based Noise Cancelling Technique on Inverse Kepstrum Whitening Filter and Adaptive FIR Filter in Beamforming Structure

PubMed Central

Jeong, Jinsoo

2011-01-01

This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure. PMID:22163987

Numerical simulation of Bragg scattering of sound by surface roughness for different values of the Rayleigh parameter

NASA Astrophysics Data System (ADS)

Salin, M. B.; Dosaev, A. S.; Konkov, A. I.; Salin, B. M.

2014-07-01

Numerical simulation methods are described for the spectral characteristics of an acoustic signal scattered by multiscale surface waves. The methods include the algorithms for calculating the scattered field by the Kirchhoff method and with the use of an integral equation, as well as the algorithms of surface waves generation with allowance for nonlinear hydrodynamic effects. The paper focuses on studying the spectrum of Bragg scattering caused by surface waves whose frequency exceeds the fundamental low-frequency component of the surface waves by several octaves. The spectrum broadening of the backscattered signal is estimated. The possibility of extending the range of applicability of the computing method developed under small perturbation conditions to cases characterized by a Rayleigh parameter of ≥1 is estimated.

Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator

PubMed Central

Li, Qiao; Mark, Roger G; Clifford, Gari D

2009-01-01

Background Within the intensive care unit (ICU), arterial blood pressure (ABP) is typically recorded at different (and sometimes uneven) sampling frequencies, and from different sensors, and is often corrupted by different artifacts and noise which are often non-Gaussian, nonlinear and nonstationary. Extracting robust parameters from such signals, and providing confidences in the estimates is therefore difficult and requires an adaptive filtering approach which accounts for artifact types. Methods Using a large ICU database, and over 6000 hours of simultaneously acquired electrocardiogram (ECG) and ABP waveforms sampled at 125 Hz from a 437 patient subset, we documented six general types of ABP artifact. We describe a new ABP signal quality index (SQI), based upon the combination of two previously reported signal quality measures weighted together. One index measures morphological normality, and the other degradation due to noise. After extracting a 6084-hour subset of clean data using our SQI, we evaluated a new robust tracking algorithm for estimating blood pressure and heart rate (HR) based upon a Kalman Filter (KF) with an update sequence modified by the KF innovation sequence and the value of the SQI. In order to do this, we have created six novel models of different categories of artifacts that we have identified in our ABP waveform data. These artifact models were then injected into clean ABP waveforms in a controlled manner. Clinical blood pressure (systolic, mean and diastolic) estimates were then made from the ABP waveforms for both clean and corrupted data. The mean absolute error for systolic, mean and diastolic blood pressure was then calculated for different levels of artifact pollution to provide estimates of expected errors given a single value of the SQI. Results Our artifact models demonstrate that artifact types have differing effects on systolic, diastolic and mean ABP estimates. We show that, for most artifact types, diastolic ABP estimates are less noise-sensitive than mean ABP estimates, which in turn are more robust than systolic ABP estimates. We also show that our SQI can provide error bounds for both HR and ABP estimates. Conclusion The KF/SQI-fusion method described in this article was shown to provide an accurate estimate of blood pressure and HR derived from the ABP waveform even in the presence of high levels of persistent noise and artifact, and during extreme bradycardia and tachycardia. Differences in error between artifact types, measurement sensors and the quality of the source signal can be factored into physiological estimation using an unbiased adaptive filter, signal innovation and signal quality measures. PMID:19586547

Minimal Polynomial Method for Estimating Parameters of Signals Received by an Antenna Array

NASA Astrophysics Data System (ADS)

Ermolaev, V. T.; Flaksman, A. G.; Elokhin, A. V.; Kuptsov, V. V.

2018-01-01

The effectiveness of the projection minimal polynomial method for solving the problem of determining the number of sources of signals acting on an antenna array (AA) with an arbitrary configuration and their angular directions has been studied. The method proposes estimating the degree of the minimal polynomial of the correlation matrix (CM) of the input process in the AA on the basis of a statistically validated root-mean-square criterion. Special attention is paid to the case of the ultrashort sample of the input process when the number of samples is considerably smaller than the number of AA elements, which is important for multielement AAs. It is shown that the proposed method is more effective in this case than methods based on the AIC (Akaike's Information Criterion) or minimum description length (MDL) criterion.

Toward a Smartphone Application for Estimation of Pulse Transit Time

PubMed Central

Liu, He; Ivanov, Kamen; Wang, Yadong; Wang, Lei

2015-01-01

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications. PMID:26516861

Toward a Smartphone Application for Estimation of Pulse Transit Time.

PubMed

Liu, He; Ivanov, Kamen; Wang, Yadong; Wang, Lei

2015-10-27

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications.

[A method for obtaining redshifts of quasars based on wavelet multi-scaling feature matching].

PubMed

Liu, Zhong-Tian; Li, Xiang-Ru; Wu, Fu-Chao; Zhao, Yong-Heng

2006-09-01

The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc.

An analytical method to simulate the H I 21-cm visibility signal for intensity mapping experiments

NASA Astrophysics Data System (ADS)

Sarkar, Anjan Kumar; Bharadwaj, Somnath; Marthi, Visweshwar Ram

2018-01-01

Simulations play a vital role in testing and validating H I 21-cm power spectrum estimation techniques. Conventional methods use techniques like N-body simulations to simulate the sky signal which is then passed through a model of the instrument. This makes it necessary to simulate the H I distribution in a large cosmological volume, and incorporate both the light-cone effect and the telescope's chromatic response. The computational requirements may be particularly large if one wishes to simulate many realizations of the signal. In this paper, we present an analytical method to simulate the H I visibility signal. This is particularly efficient if one wishes to simulate a large number of realizations of the signal. Our method is based on theoretical predictions of the visibility correlation which incorporate both the light-cone effect and the telescope's chromatic response. We have demonstrated this method by applying it to simulate the H I visibility signal for the upcoming Ooty Wide Field Array Phase I.

Single Platform Geolocation of Radio Frequency Emitters

DTIC Science & Technology

2015-03-26

Error SNR Signal to Noise Ratio SOI Signal of Interest STK Systems Tool Kit UCA Uniform Circular Array WGS World Geodetic System xv SINGLE PLATFORM...Section 2.6 describes a method to visualize the confidence of estimated parameters. 2.1 Coordinate Systems and Reference Frames The following...be used to visualize the confidence surface using the method developed in Section 2.6. The NLO method will be shown to be the minimization of the

Systems for low frequency seismic and infrasound detection of geo-pressure transition zones

DOEpatents

Shook, G. Michael; LeRoy, Samuel D.; Benzing, William M.

2007-10-16

Methods for determining the existence and characteristics of a gradational pressurized zone within a subterranean formation are disclosed. One embodiment involves employing an attenuation relationship between a seismic response signal and increasing wavelet wavelength, which relationship may be used to detect a gradational pressurized zone and/or determine characteristics thereof. In another embodiment, a method for analyzing data contained within a response signal for signal characteristics that may change in relation to the distance between an input signal source and the gradational pressurized zone is disclosed. In a further embodiment, the relationship between response signal wavelet frequency and comparative amplitude may be used to estimate an optimal wavelet wavelength or range of wavelengths used for data processing or input signal selection. Systems for seismic exploration and data analysis for practicing the above-mentioned method embodiments are also disclosed.

Radar Imaging of Non-Uniformly Rotating Targets via a Novel Approach for Multi-Component AM-FM Signal Parameter Estimation

PubMed Central

Wang, Yong

2015-01-01

A novel radar imaging approach for non-uniformly rotating targets is proposed in this study. It is assumed that the maneuverability of the non-cooperative target is severe, and the received signal in a range cell can be modeled as multi-component amplitude-modulated and frequency-modulated (AM-FM) signals after motion compensation. Then, the modified version of Chirplet decomposition (MCD) based on the integrated high order ambiguity function (IHAF) is presented for the parameter estimation of AM-FM signals, and the corresponding high quality instantaneous ISAR images can be obtained from the estimated parameters. Compared with the MCD algorithm based on the generalized cubic phase function (GCPF) in the authors’ previous paper, the novel algorithm presented in this paper is more accurate and efficient, and the results with simulated and real data demonstrate the superiority of the proposed method. PMID:25806870

Time-Varying Delay Estimation Applied to the Surface Electromyography Signals Using the Parametric Approach

NASA Astrophysics Data System (ADS)

Luu, Gia Thien; Boualem, Abdelbassit; Duy, Tran Trung; Ravier, Philippe; Butteli, Olivier

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over time, which induces time-varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In this paper, the problem of TVD estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a deterministic optimization technique (Newton) and stochastic optimization technique, called simulated annealing (SA). The performance of the two techniques is also compared. We also derive two appropriate Cramer-Rao Lower Bounds (CRLB) for the estimated TVD model parameters and for the TVD waveforms. Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the derived CRBs. A comparison with non-parametric approaches of the TVD estimation is also presented and shows the superiority of the method proposed.

Objectivity and validity of EMG method in estimating anaerobic threshold.

PubMed

Kang, S-K; Kim, J; Kwon, M; Eom, H

2014-08-01

The purposes of this study were to verify and compare the performances of anaerobic threshold (AT) point estimates among different filtering intervals (9, 15, 20, 25, 30 s) and to investigate the interrelationships of AT point estimates obtained by ventilatory threshold (VT) and muscle fatigue thresholds using electromyographic (EMG) activity during incremental exercise on a cycle ergometer. 69 untrained male university students, yet pursuing regular exercise voluntarily participated in this study. The incremental exercise protocol was applied with a consistent stepwise increase in power output of 20 watts per minute until exhaustion. AT point was also estimated in the same manner using V-slope program with gas exchange parameters. In general, the estimated values of AT point-time computed by EMG method were more consistent across 5 filtering intervals and demonstrated higher correlations among themselves when compared with those values obtained by VT method. The results found in the present study suggest that the EMG signals could be used as an alternative or a new option in estimating AT point. Also the proposed computing procedure implemented in Matlab for the analysis of EMG signals appeared to be valid and reliable as it produced nearly identical values and high correlations with VT estimates. © Georg Thieme Verlag KG Stuttgart · New York.

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

Efficient methods for joint estimation of multiple fundamental frequencies in music signals

NASA Astrophysics Data System (ADS)

Pertusa, Antonio; Iñesta, José M.

2012-12-01

This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.

Separation of arteries and veins in the cerebral cortex using physiological oscillations by optical imaging of intrinsic signal

NASA Astrophysics Data System (ADS)

Hu, Dewen; Wang, Yucheng; Liu, Yadong; Li, Ming; Liu, Fayi

2010-05-01

An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.

Separation of arteries and veins in the cerebral cortex using physiological oscillations by optical imaging of intrinsic signal.

PubMed

Hu, Dewen; Wang, Yucheng; Liu, Yadong; Li, Ming; Liu, Fayi

2010-01-01

An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.

Effects of electrocardiography contamination and comparison of ECG removal methods on upper trapezius electromyography recordings.

PubMed

Marker, Ryan J; Maluf, Katrina S

2014-12-01

Electromyography (EMG) recordings from the trapezius are often contaminated by the electrocardiography (ECG) signal, making it difficult to distinguish low-level muscle activity from muscular rest. This study investigates the influence of ECG contamination on EMG amplitude and frequency estimations in the upper trapezius during muscular rest and low-level contractions. A new method of ECG contamination removal, filtered template subtraction (FTS), is described and compared to 30 Hz high-pass filter (HPF) and averaged template subtraction (ATS) methods. FTS creates a unique template of each ECG artifact using a low-pass filtered copy of the contaminated signal, which is subtracted from contaminated periods in the original signal. ECG contamination results in an over-estimation of EMG amplitude during rest in the upper trapezius, with negligible effects on amplitude and frequency estimations during low-intensity isometric contractions. FTS and HPF successfully removed ECG contamination from periods of muscular rest, yet introduced errors during muscle contraction. Conversely, ATS failed to fully remove ECG contamination during muscular rest, yet did not introduce errors during muscle contraction. The relative advantages and disadvantages of different ECG contamination removal methods should be considered in the context of the specific motor tasks that require analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.

A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation.

PubMed

Holland, Alexander; Aboy, Mateo

2009-07-01

We present a novel method to iteratively calculate discrete Fourier transforms for discrete time signals with sample time intervals that may be widely nonuniform. The proposed recursive Fourier transform (RFT) does not require interpolation of the samples to uniform time intervals, and each iterative transform update of N frequencies has computational order N. Because of the inherent non-uniformity in the time between successive heart beats, an application particularly well suited for this transform is power spectral density (PSD) estimation for heart rate variability. We compare RFT based spectrum estimation with Lomb-Scargle Transform (LST) based estimation. PSD estimation based on the LST also does not require uniform time samples, but the LST has a computational order greater than Nlog(N). We conducted an assessment study involving the analysis of quasi-stationary signals with various levels of randomly missing heart beats. Our results indicate that the RFT leads to comparable estimation performance to the LST with significantly less computational overhead and complexity for applications requiring iterative spectrum estimations.

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

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

Aziz, H. M. Abdul; Ukkusuri, Satish V.

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less

A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

DOE PAGES

Aziz, H. M. Abdul; Ukkusuri, Satish V.

2017-06-29

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less

Adaptive Modal Identification for Flutter Suppression Control

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Drew, Michael; Swei, Sean S.

2016-01-01

In this paper, we will develop an adaptive modal identification method for identifying the frequencies and damping of a flutter mode based on model-reference adaptive control (MRAC) and least-squares methods. The least-squares parameter estimation will achieve parameter convergence in the presence of persistent excitation whereas the MRAC parameter estimation does not guarantee parameter convergence. Two adaptive flutter suppression control approaches are developed: one based on MRAC and the other based on the least-squares method. The MRAC flutter suppression control is designed as an integral part of the parameter estimation where the feedback signal is used to estimate the modal information. On the other hand, the separation principle of control and estimation is applied to the least-squares method. The least-squares modal identification is used to perform parameter estimation.

A parametric method for determining the number of signals in narrow-band direction finding

NASA Astrophysics Data System (ADS)

Wu, Qiang; Fuhrmann, Daniel R.

1991-08-01

A novel and more accurate method to determine the number of signals in the multisource direction finding problem is developed. The information-theoretic criteria of Yin and Krishnaiah (1988) are applied to a set of quantities which are evaluated from the log-likelihood function. Based on proven asymptotic properties of the maximum likelihood estimation, these quantities have the properties required by the criteria. Since the information-theoretic criteria use these quantities instead of the eigenvalues of the estimated correlation matrix, this approach possesses the advantage of not requiring a subjective threshold, and also provides higher performance than when eigenvalues are used. Simulation results are presented and compared to those obtained from the nonparametric method given by Wax and Kailath (1985).

Robust estimation of fetal heart rate from US Doppler signals

NASA Astrophysics Data System (ADS)

Voicu, Iulian; Girault, Jean-Marc; Roussel, Catherine; Decock, Aliette; Kouame, Denis

2010-01-01

Introduction: In utero, Monitoring of fetal wellbeing or suffering is today an open challenge, due to the high number of clinical parameters to be considered. An automatic monitoring of fetal activity, dedicated for quantifying fetal wellbeing, becomes necessary. For this purpose and in a view to supply an alternative for the Manning test, we used an ultrasound multitransducer multigate Doppler system. One important issue (and first step in our investigation) is the accurate estimation of fetal heart rate (FHR). An estimation of the FHR is obtained by evaluating the autocorrelation function of the Doppler signals for ills and healthiness foetus. However, this estimator is not enough robust since about 20% of FHR are not detected in comparison to a reference system. These non detections are principally due to the fact that the Doppler signal generated by the fetal moving is strongly disturbed by the presence of others several Doppler sources (mother' s moving, pseudo breathing, etc.). By modifying the existing method (autocorrelation method) and by proposing new time and frequency estimators used in the audio' s domain, we reduce to 5% the probability of non-detection of the fetal heart rate. These results are really encouraging and they enable us to plan the use of automatic classification techniques in order to discriminate between healthy and in suffering foetus.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Source localization of non-stationary acoustic data using time-frequency analysis

NASA Astrophysics Data System (ADS)

Stoughton, Jack; Edmonson, William

2005-04-01

An improvement in temporal locality of the generalized cross-correlation (GCC) for angle of arrival (AOA) estimation can be achieved by employing 2-D cross-correlation of infrasonic sensor data transformed to its time-frequency (TF) representation. Intermediate to the AOA evaluation is the time delay between pairs of sensors. The signal class of interest includes far field sources which are partially coherent across the array, nonstationary, and wideband. In addition, signals can occur as multiple short bursts, for which TF representations may be more appropriate for time delay estimation. The GCC tends to smooth out such temporal energy bursts. Simulation and experimental results will demonstrate the improvement in using a TF-based GCC, using the Cohen class, over the classic GCC method. Comparative demonstration of the methods will be performed on data captured on an infrasonic sensor array located at NASA Langley Research Center (LaRC). The infrasonic data sources include Delta IV and Space Shuttle launches from Kennedy Space Center which belong to the stated signal class. Of interest is to apply this method to the AOA estimation of atmospheric turbulence. [Work supported by NASA LaRC Creativity and Innovation project: Infrasonic Detection of Clear Air Turbulence and Severe Storms.

The Novel Nonlinear Adaptive Doppler Shift Estimation Technique and the Coherent Doppler Lidar System Validation Lidar

NASA Technical Reports Server (NTRS)

Beyon, Jeffrey Y.; Koch, Grady J.

2006-01-01

The signal processing aspect of a 2-m wavelength coherent Doppler lidar system under development at NASA Langley Research Center in Virginia is investigated in this paper. The lidar system is named VALIDAR (validation lidar) and its signal processing program estimates and displays various wind parameters in real-time as data acquisition occurs. The goal is to improve the quality of the current estimates such as power, Doppler shift, wind speed, and wind direction, especially in low signal-to-noise-ratio (SNR) regime. A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is developed on such behalf and its performance is analyzed using the wind data acquired over a long period of time by VALIDAR. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly as SNR decreases. NADSET compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in a strong SNR range and identifying sporadic range bins where good Doppler shift estimates are found. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the long-period lidar return data.

Kernel Wiener filter and its application to pattern recognition.

PubMed

Yoshino, Hirokazu; Dong, Chen; Washizawa, Yoshikazu; Yamashita, Yukihiko

2010-11-01

The Wiener filter (WF) is widely used for inverse problems. From an observed signal, it provides the best estimated signal with respect to the squared error averaged over the original and the observed signals among linear operators. The kernel WF (KWF), extended directly from WF, has a problem that an additive noise has to be handled by samples. Since the computational complexity of kernel methods depends on the number of samples, a huge computational cost is necessary for the case. By using the first-order approximation of kernel functions, we realize KWF that can handle such a noise not by samples but as a random variable. We also propose the error estimation method for kernel filters by using the approximations. In order to show the advantages of the proposed methods, we conducted the experiments to denoise images and estimate errors. We also apply KWF to classification since KWF can provide an approximated result of the maximum a posteriori classifier that provides the best recognition accuracy. The noise term in the criterion can be used for the classification in the presence of noise or a new regularization to suppress changes in the input space, whereas the ordinary regularization for the kernel method suppresses changes in the feature space. In order to show the advantages of the proposed methods, we conducted experiments of binary and multiclass classifications and classification in the presence of noise.

Analysis and improved design considerations for airborne pulse Doppler radar signal processing in the detection of hazardous windshear

NASA Technical Reports Server (NTRS)

Lee, Jonggil

1990-01-01

High resolution windspeed profile measurements are needed to provide reliable detection of hazardous low altitude windshear with an airborne pulse Doppler radar. The system phase noise in a Doppler weather radar may degrade the spectrum moment estimation quality and the clutter cancellation capability which are important in windshear detection. Also the bias due to weather return Doppler spectrum skewness may cause large errors in pulse pair spectral parameter estimates. These effects are analyzed for the improvement of an airborne Doppler weather radar signal processing design. A method is presented for the direct measurement of windspeed gradient using low pulse repetition frequency (PRF) radar. This spatial gradient is essential in obtaining the windshear hazard index. As an alternative, the modified Prony method is suggested as a spectrum mode estimator for both the clutter and weather signal. Estimation of Doppler spectrum modes may provide the desired windshear hazard information without the need of any preliminary processing requirement such as clutter filtering. The results obtained by processing a NASA simulation model output support consideration of mode identification as one component of a windshear detection algorithm.

NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model.

PubMed

Sood, Mehak; Besson, Pierre; Muthalib, Makii; Jindal, Utkarsh; Perrey, Stephane; Dutta, Anirban; Hayashibe, Mitsuhiro

2016-12-01

Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2mA, 10min) using a 4×1 ring high-definition montage. Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5-11.25Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. Published by Elsevier B.V.

Bird population density estimated from acoustic signals

USGS Publications Warehouse

Dawson, D.K.; Efford, M.G.

2009-01-01

Many animal species are detected primarily by sound. Although songs, calls and other sounds are often used for population assessment, as in bird point counts and hydrophone surveys of cetaceans, there are few rigorous methods for estimating population density from acoustic data. 2. The problem has several parts - distinguishing individuals, adjusting for individuals that are missed, and adjusting for the area sampled. Spatially explicit capture-recapture (SECR) is a statistical methodology that addresses jointly the second and third parts of the problem. We have extended SECR to use uncalibrated information from acoustic signals on the distance to each source. 3. We applied this extension of SECR to data from an acoustic survey of ovenbird Seiurus aurocapilla density in an eastern US deciduous forest with multiple four-microphone arrays. We modelled average power from spectrograms of ovenbird songs measured within a window of 0??7 s duration and frequencies between 4200 and 5200 Hz. 4. The resulting estimates of the density of singing males (0??19 ha -1 SE 0??03 ha-1) were consistent with estimates of the adult male population density from mist-netting (0??36 ha-1 SE 0??12 ha-1). The fitted model predicts sound attenuation of 0??11 dB m-1 (SE 0??01 dB m-1) in excess of losses from spherical spreading. 5.Synthesis and applications. Our method for estimating animal population density from acoustic signals fills a gap in the census methods available for visually cryptic but vocal taxa, including many species of bird and cetacean. The necessary equipment is simple and readily available; as few as two microphones may provide adequate estimates, given spatial replication. The method requires that individuals detected at the same place are acoustically distinguishable and all individuals vocalize during the recording interval, or that the per capita rate of vocalization is known. We believe these requirements can be met, with suitable field methods, for a significant number of songbird species. ?? 2009 British Ecological Society.

Grid mapping: a novel method of signal quality evaluation on a single lead electrocardiogram.

PubMed

Li, Yanjun; Tang, Xiaoying

2017-12-01

Diagnosis of long-term electrocardiogram (ECG) calls for automatic and accurate methods of ECG signal quality estimation, not only to lighten the burden of the doctors but also to avoid misdiagnoses. In this paper, a novel waveform-based method of phase-space reconstruction for signal quality estimation on a single lead ECG was proposed by projecting the amplitude of the ECG and its first order difference into grid cells. The waveform of a single lead ECG was divided into non-overlapping episodes (T s  = 10, 20, 30 s), and the number of grids in both the width and the height of each map are in the range [20, 100] (N X  = N Y  = 20, 30, 40, … 90, 100). The blank pane ratio (BPR) and the entropy were calculated from the distribution of ECG sampling points which were projected into the grid cells. Signal Quality Indices (SQI) bSQI and eSQI were calculated according to the BPR and the entropy, respectively. The MIT-BIH Noise Stress Test Database was used to test the performance of bSQI and eSQI on ECG signal quality estimation. The signal-to-noise ratio (SNR) during the noisy segments of the ECG records in the database is 24, 18, 12, 6, 0 and - 6 dB, respectively. For the SQI quantitative analysis, the records were divided into three groups: good quality group (24, 18 dB), moderate group (12, 6 dB) and bad quality group (0, - 6 dB). The classification among good quality group, moderate quality group and bad quality group were made by linear support-vector machine with the combination of the BPR, the entropy, the bSQI and the eSQI. The classification accuracy was 82.4% and the Cohen's Kappa coefficient was 0.74 on a scale of N X  = 40 and T s  = 20 s. In conclusion, the novel grid mapping offers an intuitive and simple approach to achieving signal quality estimation on a single lead ECG.

Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform.

PubMed

Wu, Hau-Tieng; Lewis, Gregory F; Davila, Maria I; Daubechies, Ingrid; Porges, Stephen W

2016-10-17

With recent advances in sensor and computer technologies, the ability to monitor peripheral pulse activity is no longer limited to the laboratory and clinic. Now inexpensive sensors, which interface with smartphones or other computer-based devices, are expanding into the consumer market. When appropriate algorithms are applied, these new technologies enable ambulatory monitoring of dynamic physiological responses outside the clinic in a variety of applications including monitoring fatigue, health, workload, fitness, and rehabilitation. Several of these applications rely upon measures derived from peripheral pulse waves measured via contact or non-contact photoplethysmography (PPG). As technologies move from contact to non-contact PPG, there are new challenges. The technology necessary to estimate average heart rate over a few seconds from a noncontact PPG is available. However, a technology to precisely measure instantaneous heat rate (IHR) from non-contact sensors, on a beat-to-beat basis, is more challenging. The objective of this paper is to develop an algorithm with the ability to accurately monitor IHR from peripheral pulse waves, which provides an opportunity to measure the neural regulation of the heart from the beat-to-beat heart rate pattern (i.e., heart rate variability). The adaptive harmonic model is applied to model the contact or non-contact PPG signals, and a new methodology, the Synchrosqueezing Transform (SST), is applied to extract IHR. The body sway rhythm inherited in the non-contact PPG signal is modeled and handled by the notion of wave-shape function. The SST optimizes the extraction of IHR from the PPG signals and the technique functions well even during periods of poor signal to noise. We contrast the contact and non-contact indices of PPG derived heart rate with a criterion electrocardiogram (ECG). ECG and PPG signals were monitored in 21 healthy subjects performing tasks with different physical demands. The root mean square error of IHR estimated by SST is significantly better than commonly applied methods such as autoregressive (AR) method. In the walking situation, while AR method fails, SST still provides a reasonably good result. The SST processed PPG data provided an accurate estimate of the ECG derived IHR and consistently performed better than commonly applied methods such as autoregressive method.

Spatial sparsity based indoor localization in wireless sensor network for assistive healthcare.

PubMed

Pourhomayoun, Mohammad; Jin, Zhanpeng; Fowler, Mark

2012-01-01

Indoor localization is one of the key topics in the area of wireless networks with increasing applications in assistive healthcare, where tracking the position and actions of the patient or elderly are required for medical observation or accident prevention. Most of the common indoor localization methods are based on estimating one or more location-dependent signal parameters like TOA, AOA or RSS. However, some difficulties and challenges caused by the complex scenarios within a closed space significantly limit the applicability of those existing approaches in an indoor assistive environment, such as the well-known multipath effect. In this paper, we develop a new one-stage localization method based on spatial sparsity of the x-y plane. In this method, we directly estimate the location of the emitter without going through the intermediate stage of TOA or signal strength estimation. We evaluate the performance of the proposed method using Monte Carlo simulation. The results show that the proposed method is (i) very accurate even with a small number of sensors and (ii) very effective in addressing the multi-path issues.

Estimation of signal-dependent noise level function in transform domain via a sparse recovery model.

PubMed

Yang, Jingyu; Gan, Ziqiao; Wu, Zhaoyang; Hou, Chunping

2015-05-01

This paper proposes a novel algorithm to estimate the noise level function (NLF) of signal-dependent noise (SDN) from a single image based on the sparse representation of NLFs. Noise level samples are estimated from the high-frequency discrete cosine transform (DCT) coefficients of nonlocal-grouped low-variation image patches. Then, an NLF recovery model based on the sparse representation of NLFs under a trained basis is constructed to recover NLF from the incomplete noise level samples. Confidence levels of the NLF samples are incorporated into the proposed model to promote reliable samples and weaken unreliable ones. We investigate the behavior of the estimation performance with respect to the block size, sampling rate, and confidence weighting. Simulation results on synthetic noisy images show that our method outperforms existing state-of-the-art schemes. The proposed method is evaluated on real noisy images captured by three types of commodity imaging devices, and shows consistently excellent SDN estimation performance. The estimated NLFs are incorporated into two well-known denoising schemes, nonlocal means and BM3D, and show significant improvements in denoising SDN-polluted images.

Covert Channels in SIP for VoIP Signalling

NASA Astrophysics Data System (ADS)

Mazurczyk, Wojciech; Szczypiorski, Krzysztof

In this paper, we evaluate available steganographic techniques for SIP (Session Initiation Protocol) that can be used for creating covert channels during signaling phase of VoIP (Voice over IP) call. Apart from characterizing existing steganographic methods we provide new insights by introducing new techniques. We also estimate amount of data that can be transferred in signalling messages for typical IP telephony call.

Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients

PubMed Central

Hassani, Malihe; Karami, Mohammad Reza

2015-01-01

The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3. PMID:26284176

Method and Apparatus for Reducing Noise from Near Ocean Surface Sources

DTIC Science & Technology

2001-10-01

reducing the acoustic noise from near-surface 4 sources using an array processing technique that utilizes 5 Multiple Signal Classification ( MUSIC ...sources without 13 degrading the signal level and quality of the TOI. The present 14 invention utilizes a unique application of the MUSIC beamforming...specific algorithm that utilizes a 5 MUSIC technique and estimates the direction of arrival (DOA) of 6 the acoustic signal signals and generates output

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

PubMed

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

Direction of Arrival Estimation for MIMO Radar via Unitary Nuclear Norm Minimization

PubMed Central

Wang, Xianpeng; Huang, Mengxing; Wu, Xiaoqin; Bi, Guoan

2017-01-01

In this paper, we consider the direction of arrival (DOA) estimation issue of noncircular (NC) source in multiple-input multiple-output (MIMO) radar and propose a novel unitary nuclear norm minimization (UNNM) algorithm. In the proposed method, the noncircular properties of signals are used to double the virtual array aperture, and the real-valued data are obtained by utilizing unitary transformation. Then a real-valued block sparse model is established based on a novel over-complete dictionary, and a UNNM algorithm is formulated for recovering the block-sparse matrix. In addition, the real-valued NC-MUSIC spectrum is used to design a weight matrix for reweighting the nuclear norm minimization to achieve the enhanced sparsity of solutions. Finally, the DOA is estimated by searching the non-zero blocks of the recovered matrix. Because of using the noncircular properties of signals to extend the virtual array aperture and an additional real structure to suppress the noise, the proposed method provides better performance compared with the conventional sparse recovery based algorithms. Furthermore, the proposed method can handle the case of underdetermined DOA estimation. Simulation results show the effectiveness and advantages of the proposed method. PMID:28441770

Adaptive Fourier decomposition based ECG denoising.

PubMed

Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

2016-10-01

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Speech Enhancement Using Gaussian Scale Mixture Models

PubMed Central

Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.

2011-01-01

This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches to enhance the efficiency: the Laplace method and a variational approximation. The proposed methods were applied to enhance speech corrupted by Gaussian noise and speech-shaped noise (SSN). For both approximations, signals reconstructed from the estimated frequency coefficients provided higher signal-to-noise ratio (SNR) and those reconstructed from the estimated log-spectra produced lower word recognition error rate because the log-spectra fit the inputs to the recognizer better. Our algorithms effectively reduced the SSN, which algorithms based on spectral analysis were not able to suppress. PMID:21359139

Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal

PubMed Central

Islam, Mohammad Tariqul; Tanvir Ahmed, Sk.; Zabir, Ishmam; Shahnaz, Celia

2018-01-01

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach. PMID:29515812

An Online Observer for Minimization of Pulsating Torque in SMPM Motors

PubMed Central

Roșca, Lucian

2016-01-01

A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method. PMID:27089182

Estimators of The Magnitude-Squared Spectrum and Methods for Incorporating SNR Uncertainty

PubMed Central

Lu, Yang; Loizou, Philipos C.

2011-01-01

Statistical estimators of the magnitude-squared spectrum are derived based on the assumption that the magnitude-squared spectrum of the noisy speech signal can be computed as the sum of the (clean) signal and noise magnitude-squared spectra. Maximum a posterior (MAP) and minimum mean square error (MMSE) estimators are derived based on a Gaussian statistical model. The gain function of the MAP estimator was found to be identical to the gain function used in the ideal binary mask (IdBM) that is widely used in computational auditory scene analysis (CASA). As such, it was binary and assumed the value of 1 if the local SNR exceeded 0 dB, and assumed the value of 0 otherwise. By modeling the local instantaneous SNR as an F-distributed random variable, soft masking methods were derived incorporating SNR uncertainty. The soft masking method, in particular, which weighted the noisy magnitude-squared spectrum by the a priori probability that the local SNR exceeds 0 dB was shown to be identical to the Wiener gain function. Results indicated that the proposed estimators yielded significantly better speech quality than the conventional MMSE spectral power estimators, in terms of yielding lower residual noise and lower speech distortion. PMID:21886543

Time reversal for localization of sources of infrasound signals in a windy stratified atmosphere.

PubMed

Lonzaga, Joel B

2016-06-01

Time reversal is used for localizing sources of recorded infrasound signals propagating in a windy, stratified atmosphere. Due to the convective effect of the background flow, the back-azimuths of the recorded signals can be substantially different from the source back-azimuth, posing a significant difficulty in source localization. The back-propagated signals are characterized by negative group velocities from which the source back-azimuth and source-to-receiver (STR) distance can be estimated using the apparent back-azimuths and trace velocities of the signals. The method is applied to several distinct infrasound arrivals recorded by two arrays in the Netherlands. The infrasound signals were generated by the Buncefield oil depot explosion in the U.K. in December 2005. Analyses show that the method can be used to substantially enhance estimates of the source back-azimuth and the STR distance. In one of the arrays, for instance, the deviations between the measured back-azimuths of the signals and the known source back-azimuth are quite large (-1° to -7°), whereas the deviations between the predicted and known source back-azimuths are small with an absolute mean value of <1°. Furthermore, the predicted STR distance is off only by <5% of the known STR distance.

A posteriori error estimates in voice source recovery

NASA Astrophysics Data System (ADS)

Leonov, A. S.; Sorokin, V. N.

2017-12-01

The inverse problem of voice source pulse recovery from a segment of a speech signal is under consideration. A special mathematical model is used for the solution that relates these quantities. A variational method of solving inverse problem of voice source recovery for a new parametric class of sources, that is for piecewise-linear sources (PWL-sources), is proposed. Also, a technique for a posteriori numerical error estimation for obtained solutions is presented. A computer study of the adequacy of adopted speech production model with PWL-sources is performed in solving the inverse problems for various types of voice signals, as well as corresponding study of a posteriori error estimates. Numerical experiments for speech signals show satisfactory properties of proposed a posteriori error estimates, which represent the upper bounds of possible errors in solving the inverse problem. The estimate of the most probable error in determining the source-pulse shapes is about 7-8% for the investigated speech material. It is noted that a posteriori error estimates can be used as a criterion of the quality for obtained voice source pulses in application to speaker recognition.

Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal.

PubMed

Panigrahy, D; Sahu, P K

2017-03-01

This paper proposes a five-stage based methodology to extract the fetal electrocardiogram (FECG) from the single channel abdominal ECG using differential evolution (DE) algorithm, extended Kalman smoother (EKS) and adaptive neuro fuzzy inference system (ANFIS) framework. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is necessary. The pre-processing stage is used to remove the noise from the abdominal ECG signal. The EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG components are required to develop the state and measurement equation of the EKS framework. These optimized maternal ECG parameters are selected by the differential evolution algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship the ANFIS is used. Inputs to the ANFIS framework are the output of EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting the output of ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. The proposed methodology shows a sensitivity of 94.21%, accuracy of 90.66%, and positive predictive value of 96.05% from the non-invasive fetal ECG database. The proposed methodology also shows a sensitivity of 91.47%, accuracy of 84.89%, and positive predictive value of 92.18% from the set A of PCDB.

Effects of Multipath and Oversampling on Navigation Using Orthogonal Frequency Division Multiplexed Signals of Opportunity

DTIC Science & Technology

2008-03-01

for military use. The L2 carrier frequency operates at 1227.6 MHz and transmits only the precise code . Each satellite transmits a unique pseudo ...random noise (PRN) code by which it is identified. GPS receivers require a LOS to four satellite signals to accurately estimate a position in three...receiver frequency errors, noise addition, and multipath ef- fects. He also developed four methods for estimating the cross- correlation peak within a sampled

Estimation of two-dimensional motion velocity using ultrasonic signals beamformed in Cartesian coordinate for measurement of cardiac dynamics

NASA Astrophysics Data System (ADS)

Kaburaki, Kaori; Mozumi, Michiya; Hasegawa, Hideyuki

2018-07-01

Methods for the estimation of two-dimensional (2D) velocity and displacement of physiological tissues are necessary for quantitative diagnosis. In echocardiography with a phased array probe, the accuracy in the estimation of the lateral motion is lower than that of the axial motion. To improve the accuracy in the estimation of the lateral motion, in the present study, the coordinate system for ultrasonic beamforming was changed from the conventional polar coordinate to the Cartesian coordinate. In a basic experiment, the motion velocity of a phantom, which was moved at a constant speed, was estimated by the conventional and proposed methods. The proposed method reduced the bias error and standard deviation in the estimated motion velocities. In an in vivo measurement, intracardiac blood flow was analyzed by the proposed method.

Improving slowness estimate stability and visualization using limited sensor pair correlation on seismic arrays

NASA Astrophysics Data System (ADS)

Gibbons, Steven J.; Näsholm, S. P.; Ruigrok, E.; Kværna, T.

2018-04-01

Seismic arrays enhance signal detection and parameter estimation by exploiting the time-delays between arriving signals on sensors at nearby locations. Parameter estimates can suffer due to both signal incoherence, with diminished waveform similarity between sensors, and aberration, with time-delays between coherent waveforms poorly represented by the wave-front model. Sensor-to-sensor correlation approaches to parameter estimation have an advantage over direct beamforming approaches in that individual sensor-pairs can be omitted without necessarily omitting entirely the data from each of the sensors involved. Specifically, we can omit correlations between sensors for which signal coherence in an optimal frequency band is anticipated to be poor or for which anomalous time-delays are anticipated. In practice, this usually means omitting correlations between more distant sensors. We present examples from International Monitoring System seismic arrays with poor parameter estimates resulting when classical f-k analysis is performed over the full array aperture. We demonstrate improved estimates and slowness grid displays using correlation beamforming restricted to correlations between sufficiently closely spaced sensors. This limited sensor-pair correlation (LSPC) approach has lower slowness resolution than would ideally be obtained by considering all sensor-pairs. However, this ideal estimate may be unattainable due to incoherence and/or aberration and the LSPC estimate can often exploit all channels, with the associated noise-suppression, while mitigating the complications arising from correlations between very distant sensors. The greatest need for the method is for short-period signals on large aperture arrays although we also demonstrate significant improvement for secondary regional phases on a small aperture array. LSPC can also provide a robust and flexible approach to parameter estimation on three-component seismic arrays.

Dual-process theory and signal-detection theory of recognition memory.

PubMed

Wixted, John T

2007-01-01

Two influential models of recognition memory, the unequal-variance signal-detection model and a dual-process threshold/detection model, accurately describe the receiver operating characteristic, but only the latter model can provide estimates of recollection and familiarity. Such estimates often accord with those provided by the remember-know procedure, and both methods are now widely used in the neuroscience literature to identify the brain correlates of recollection and familiarity. However, in recent years, a substantial literature has accumulated directly contrasting the signal-detection model against the threshold/detection model, and that literature is almost unanimous in its endorsement of signal-detection theory. A dual-process version of signal-detection theory implies that individual recognition decisions are not process pure, and it suggests new ways to investigate the brain correlates of recognition memory. ((c) 2007 APA, all rights reserved).

On the Use of a Signal Quality Index Applying at Tracking Stage Level to Assist the RAIM System of a GNSS Receiver.

PubMed

Berardo, Mattia; Lo Presti, Letizia

2016-07-02

In this work, a novel signal processing method is proposed to assist the Receiver Autonomous Integrity Monitoring (RAIM) module used in a receiver of Global Navigation Satellite Systems (GNSS) to improve the integrity of the estimated position. The proposed technique represents an evolution of the Multipath Distance Detector (MPDD), thanks to the introduction of a Signal Quality Index (SQI), which is both a metric able to evaluate the goodness of the signal, and a parameter used to improve the performance of the RAIM modules. Simulation results show the effectiveness of the proposed method.

Spatially constrained incoherent motion method improves diffusion-weighted MRI signal decay analysis in the liver and spleen

PubMed Central

Taimouri, Vahid; Afacan, Onur; Perez-Rossello, Jeannette M.; Callahan, Michael J.; Mulkern, Robert V.; Warfield, Simon K.; Freiman, Moti

2015-01-01

Purpose: To evaluate the effect of the spatially constrained incoherent motion (SCIM) method on improving the precision and robustness of fast and slow diffusion parameter estimates from diffusion-weighted MRI in liver and spleen in comparison to the independent voxel-wise intravoxel incoherent motion (IVIM) model. Methods: We collected diffusion-weighted MRI (DW-MRI) data of 29 subjects (5 healthy subjects and 24 patients with Crohn’s disease in the ileum). We evaluated parameters estimates’ robustness against different combinations of b-values (i.e., 4 b-values and 7 b-values) by comparing the variance of the estimates obtained with the SCIM and the independent voxel-wise IVIM model. We also evaluated the improvement in the precision of parameter estimates by comparing the coefficient of variation (CV) of the SCIM parameter estimates to that of the IVIM. Results: The SCIM method was more robust compared to IVIM (up to 70% in liver and spleen) for different combinations of b-values. Also, the CV values of the parameter estimations using the SCIM method were significantly lower compared to repeated acquisition and signal averaging estimated using IVIM, especially for the fast diffusion parameter in liver (CVIV IM = 46.61 ± 11.22, CVSCIM = 16.85 ± 2.160, p < 0.001) and spleen (CVIV IM = 95.15 ± 19.82, CVSCIM = 52.55 ± 1.91, p < 0.001). Conclusions: The SCIM method characterizes fast and slow diffusion more precisely compared to the independent voxel-wise IVIM model fitting in the liver and spleen. PMID:25832079

Source counting in MEG neuroimaging

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Dell, John; Magee, Ralphy; Roberts, Timothy P. L.

2009-02-01

Magnetoencephalography (MEG) is a multi-channel, functional imaging technique. It measures the magnetic field produced by the primary electric currents inside the brain via a sensor array composed of a large number of superconducting quantum interference devices. The measurements are then used to estimate the locations, strengths, and orientations of these electric currents. This magnetic source imaging technique encompasses a great variety of signal processing and modeling techniques which include Inverse problem, MUltiple SIgnal Classification (MUSIC), Beamforming (BF), and Independent Component Analysis (ICA) method. A key problem with Inverse problem, MUSIC and ICA methods is that the number of sources must be detected a priori. Although BF method scans the source space on a point-to-point basis, the selection of peaks as sources, however, is finally made by subjective thresholding. In practice expert data analysts often select results based on physiological plausibility. This paper presents an eigenstructure approach for the source number detection in MEG neuroimaging. By sorting eigenvalues of the estimated covariance matrix of the acquired MEG data, the measured data space is partitioned into the signal and noise subspaces. The partition is implemented by utilizing information theoretic criteria. The order of the signal subspace gives an estimate of the number of sources. The approach does not refer to any model or hypothesis, hence, is an entirely data-led operation. It possesses clear physical interpretation and efficient computation procedure. The theoretical derivation of this method and the results obtained by using the real MEG data are included to demonstrates their agreement and the promise of the proposed approach.

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network.

PubMed

Qi, Jun; Liu, Guo-Ping

2017-11-06

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μ s. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal.

An EMG-CT method using multiple surface electrodes in the forearm.

PubMed

Nakajima, Yasuhiro; Keeratihattayakorn, Saran; Yoshinari, Satoshi; Tadano, Shigeru

2014-12-01

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values from the model with the measured values. The individual muscle activities in the deep region were estimated and used to produce an EMG tomographic image. For validation of the method, isometric contractions of finger muscles were examined for three subjects, applying a flexion load (4.9, 7.4 and 9.8 N) to the proximal interphalangeal joint of the middle finger. EMG signals in the forearm were recorded during the tasks using multiple surface electrodes, which were bound around the subject's forearm. The EMG-CT method illustrates the distribution of muscle activities within the forearm. The change in amplitude and area of activated muscles can be observed. The normalized muscle activities of all three subjects appear to increase monotonically with increases in the load. Kinesiologically, this method was able to estimate individual muscle activation values and could provide a novel tool for studying hand function and development of an examination for evaluating rehabilitation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Channel Estimation for Filter Bank Multicarrier Systems in Low SNR Environments

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

Driggs, Jonathan; Sibbett, Taylor; Moradiy, Hussein

Channel estimation techniques are crucial for reliable communications. This paper is concerned with channel estimation in a filter bank multicarrier spread spectrum (FBMCSS) system. We explore two channel estimator options: (i) a method that makes use of a periodic preamble and mimics the channel estimation techniques that are widely used in OFDM-based systems; and (ii) a method that stays within the traditional realm of filter bank signal processing. For the case where the channel noise is white, both methods are analyzed in detail and their performance is compared against their respective Cramer-Rao Lower Bounds (CRLB). Advantages and disadvantages of themore » two methods under different channel conditions are given to provide insight to the reader as to when one will outperform the other.« less

Separation of electrocardiographic from electromyographic signals using dynamic filtration.

PubMed

Christov, Ivaylo; Raikova, Rositsa; Angelova, Silvija

2018-07-01

Trunk muscle electromyographic (EMG) signals are often contaminated by the electrical activity of the heart. During low or moderate muscle force, these electrocardiographic (ECG) signals disturb the estimation of muscle activity. Butterworth high-pass filters with cut-off frequency of up to 60 Hz are often used to suppress the ECG signal. Such filters disturb the EMG signal in both frequency and time domain. A new method based on the dynamic application of Savitzky-Golay filter is proposed. EMG signals of three left trunk muscles and pure ECG signal were recorded during different motor tasks. The efficiency of the method was tested and verified both with the experimental EMG signals and with modeled signals obtained by summing the pure ECG signal with EMG signals at different levels of signal-to-noise ratio. The results were compared with those obtained by application of high-pass, 4th order Butterworth filter with cut-off frequency of 30 Hz. The suggested method is separating the EMG signal from the ECG signal without EMG signal distortion across its entire frequency range regardless of amplitudes. Butterworth filter suppresses the signals in the 0-30 Hz range thus preventing the low-frequency analysis of the EMG signal. An additional disadvantage is that it passes high-frequency ECG signal components which is apparent at equal and higher amplitudes of the ECG signal as compared to the EMG signal. The new method was also successfully verified with abnormal ECG signals. Copyright © 2018. Published by Elsevier Ltd.

Detection and evaluation of ventricular repolarization alternans: an approach to combined ECG, thoracic impedance, and beat-to-beat heart rate variability analysis.

PubMed

Kriščiukaitis, Algimantas; Šimoliūnienė, Renata; Macas, Andrius; Petrolis, Robertas; Drėgūnas, Kęstutis; Bakšytė, Giedrė; Pieteris, Linas; Bertašienė, Zita; Žaliūnas, Remigijus

2014-01-01

Beat-to-beat alteration in ventricles repolarization reflected by alternans of amplitude and/or shape of ECG S-T,T segment (TWA) is known as phenomena related with risk of severe arrhythmias leading to sudden cardiac death. Technical difficulties have caused limited its usage in clinical diagnostics. Possibilities to register and analyze multimodal signals reflecting heart activity inspired search for new technical solutions. First objective of this study was to test whether thoracic impedance signal and beat-to-beat heart rate reflect repolarization alternans detected as TWA. The second objective was revelation of multimodal signal features more comprehensively representing the phenomena and increasing its prognostic usefulness. ECG, and thoracic impedance signal recordings made during 24h follow-up of the patients hospitalized in acute phase of myocardial infarction were used for investigation. Signal morphology variations reflecting estimates were obtained by the principal component analysis-based method. Clinical outcomes of patients (survival and/or rehospitalization in 6 and 12 months) were compared to repolarization alternans and heart rate variability estimates. Repolarization alternans detected as TWA was also reflected in estimates of thoracic impedance signal shape and variation in beat-to-beat heart rate. All these parameters showed correlation with clinical outcomes of patients. The strongest significant correlation showed magnitude of alternans in estimates of thoracic impedance signal shape. The features of ECG, thoracic impedance signal and beat-to-beat variability of heart rate, give comprehensive estimates of repolarization alternans, which correlate, with clinical outcomes of the patients and we recommend using them to improve diagnostic reliability. Copyright © 2014 Lithuanian University of Health Sciences. Production and hosting by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Beamforming using subspace estimation from a diagonally averaged sample covariance.

PubMed

Quijano, Jorge E; Zurk, Lisa M

2017-08-01

The potential benefit of a large-aperture sonar array for high resolution target localization is often challenged by the lack of sufficient data required for adaptive beamforming. This paper introduces a Toeplitz-constrained estimator of the clairvoyant signal covariance matrix corresponding to multiple far-field targets embedded in background isotropic noise. The estimator is obtained by averaging along subdiagonals of the sample covariance matrix, followed by covariance extrapolation using the method of maximum entropy. The sample covariance is computed from limited data snapshots, a situation commonly encountered with large-aperture arrays in environments characterized by short periods of local stationarity. Eigenvectors computed from the Toeplitz-constrained covariance are used to construct signal-subspace projector matrices, which are shown to reduce background noise and improve detection of closely spaced targets when applied to subspace beamforming. Monte Carlo simulations corresponding to increasing array aperture suggest convergence of the proposed projector to the clairvoyant signal projector, thereby outperforming the classic projector obtained from the sample eigenvectors. Beamforming performance of the proposed method is analyzed using simulated data, as well as experimental data from the Shallow Water Array Performance experiment.

Application of independent component analysis for speech-music separation using an efficient score function estimation

NASA Astrophysics Data System (ADS)

Pishravian, Arash; Aghabozorgi Sahaf, Masoud Reza

2012-12-01

In this paper speech-music separation using Blind Source Separation is discussed. The separating algorithm is based on the mutual information minimization where the natural gradient algorithm is used for minimization. In order to do that, score function estimation from observation signals (combination of speech and music) samples is needed. The accuracy and the speed of the mentioned estimation will affect on the quality of the separated signals and the processing time of the algorithm. The score function estimation in the presented algorithm is based on Gaussian mixture based kernel density estimation method. The experimental results of the presented algorithm on the speech-music separation and comparing to the separating algorithm which is based on the Minimum Mean Square Error estimator, indicate that it can cause better performance and less processing time

An artificial EMG generation model based on signal-dependent noise and related application to motion classification

PubMed Central

Hayashi, Hideaki; Nakamura, Go; Chin, Takaaki; Tsuji, Toshio

2017-01-01

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification. PMID:28640883

A new method to estimate global mass transport and its implication for sea level rise

NASA Astrophysics Data System (ADS)

Yi, S.; Heki, K.

2017-12-01

Estimates of changes in global land mass by using GRACE observations can be achieved by two methods, a mascon method and a forward modeling method. However, results from these two methods show inconsistent secular trend. Sea level budget can be adopted to validate the consistency among observations of sea level rise by altimetry, steric change by the Argo project, and mass change by GRACE. Mascon products from JPL, GSFC and CSR are compared here, we find that all these three products cannot achieve a reconciled sea level budget, while this problem can be solved by a new forward modeling method. We further investigate the origin of this difference, and speculate that it is caused by the signal leakage from the ocean mass. Generally, it is well recognized that land signals leak into oceans, but it also happens the other way around. We stress the importance of correction of leakage from the ocean in the estimation of global land masses. Based on a reconciled sea level budget, we confirmed that global sea level rise has been accelerating significantly over 2005-2015, as a result of the ongoing global temperature increase.

Model-based ultrasound temperature visualization during and following HIFU exposure.

PubMed

Ye, Guoliang; Smith, Penny Probert; Noble, J Alison

2010-02-01

This paper describes the application of signal processing techniques to improve the robustness of ultrasound feedback for displaying changes in temperature distribution in treatment using high-intensity focused ultrasound (HIFU), especially at the low signal-to-noise ratios that might be expected in in vivo abdominal treatment. Temperature estimation is based on the local displacements in ultrasound images taken during HIFU treatment, and a method to improve robustness to outliers is introduced. The main contribution of the paper is in the application of a Kalman filter, a statistical signal processing technique, which uses a simple analytical temperature model of heat dispersion to improve the temperature estimation from the ultrasound measurements during and after HIFU exposure. To reduce the sensitivity of the method to previous assumptions on the material homogeneity and signal-to-noise ratio, an adaptive form is introduced. The method is illustrated using data from HIFU exposure of ex vivo bovine liver. A particular advantage of the stability it introduces is that the temperature can be visualized not only in the intervals between HIFU exposure but also, for some configurations, during the exposure itself. 2010 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Estimating Transmitted-Signal Phase Variations for Uplink Array Antennas

NASA Technical Reports Server (NTRS)

Paal, Leslie; Mukai, Ryan; Vilntrotter, Victor; Cornish, Timothy; Lee, Dennis

2009-01-01

A method of estimating phase drifts of microwave signals distributed to, and transmitted by, antennas in an array involves the use of the signals themselves as phase references. The method was conceived as part of the solution of the problem of maintaining precise phase calibration required for proper operation of an array of Deep Space Network (DSN) antennas on Earth used for communicating with distant spacecraft at frequencies between 7 and 8 GHz. The method could also be applied to purely terrestrial phased-array radar and other radio antenna array systems. In the DSN application, the electrical lengths (effective signal-propagation path lengths) of the various branches of the system for distributing the transmitted signals to the antennas are not precisely known, and they vary with time. The variations are attributable mostly to thermal expansion and contraction of fiber-optic and electrical signal cables and to a variety of causes associated with aging of signal-handling components. The variations are large enough to introduce large phase drifts at the signal frequency. It is necessary to measure and correct for these phase drifts in order to maintain phase calibration of the antennas. A prior method of measuring phase drifts involves the use of reference-frequency signals separate from the transmitted signals. A major impediment to accurate measurement of phase drifts over time by the prior method is the fact that although DSN reference-frequency sources separate from the transmitting signal sources are stable and accurate enough for most DSN purposes, they are not stable enough for use in maintaining phase calibrations, as required, to within a few degrees over times as long as days or possibly even weeks. By eliminating reliance on the reference-frequency subsystem, the present method overcomes this impediment. In a DSN array to which the present method applies (see figure), the microwave signals to be transmitted are generated by exciters in a signal-processing center, then distributed to the antennas via optical fibers. At each antenna, the signals are used to drive a microwave power-amplifier train, the output of which is coupled to the antenna for transmission. A small fraction of the power-amplifier-train output is sent back to the signal-processing center along another optical fiber that is part of the same fiber-optic cable used to distribute the transmitted signal to the antenna. In the signal-processing center, the signal thus returned from each antenna is detected and its phase is compared with the phase of the signal sampled directly from the corresponding exciter. It is known, from other measurements, that the signal-propagation path length from the power-amplifier-train output port to the phase center of each antenna is sufficiently stable and, hence, that sampling the signal at the power-amplifier-train output port suffices for the purpose of characterizing the phase drift of the transmitted signal at the phase center of the antenna

Fallback options for airgap sensor fault of an electromagnetic suspension system

NASA Astrophysics Data System (ADS)

Michail, Konstantinos; Zolotas, Argyrios C.; Goodall, Roger M.

2013-06-01

The paper presents a method to recover the performance of an electromagnetic suspension under faulty airgap sensor. The proposed control scheme is a combination of classical control loops, a Kalman Estimator and analytical redundancy (for the airgap signal). In this way redundant airgap sensors are not essential for reliable operation of this system. When the airgap sensor fails the required signal is recovered using a combination of a Kalman estimator and analytical redundancy. The performance of the suspension is optimised using genetic algorithms and some preliminary robustness issues to load and operating airgap variations are discussed. Simulations on a realistic model of such type of suspension illustrate the efficacy of the proposed sensor tolerant control method.

Noise elimination algorithm for modal analysis

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

Bao, X. X., E-mail: baoxingxian@upc.edu.cn; Li, C. L.; Xiong, C. B.

2015-07-27

Modal analysis is an ongoing interdisciplinary physical issue. Modal parameters estimation is applied to determine the dynamic characteristics of structures under vibration excitation. Modal analysis is more challenging for the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of structured low rank approximation combined with the complex exponential method to estimate the modal parameters. Physical experiments using a steel cantilever beam with ten accelerometers mounted, excited by an impulse load, demonstrate that this method can significantly eliminate noise from measured signals and accurately identify the modal frequencies and damping ratios. This study provides amore » fundamental mechanism of noise elimination using structured low rank approximation in physical fields.« less

Precession missile feature extraction using sparse component analysis of radar measurements

NASA Astrophysics Data System (ADS)

Liu, Lihua; Du, Xiaoyong; Ghogho, Mounir; Hu, Weidong; McLernon, Des

2012-12-01

According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.

Heartbeat detection in multimodal physiological signals using signal quality assessment based on sample entropy.

PubMed

Singh, Omkar; Sunkaria, Ramesh Kumar

2017-12-01

This paper presents a novel technique to identify heartbeats in multimodal data using electrocardiogram (ECG) and arterial blood pressure (ABP) signals. Multiple physiological signals such as ECG, ABP, and Respiration are often recorded in parallel from the activity of heart. These signals generally possess related information as they are generated by the same physical system. The ECG and ABP correspond to the same phenomenon of contraction and relaxation activity of heart. Multiple signals acquired from various sensors are generally processed independently, thus discarding the information from other measurements. In the estimation of heart rate and heart rate variability, the R peaks are generally identified from ECG signal. Efficient detection of R-peaks in electrocardiogram (ECG) is a key component in the estimation of clinically relevant parameters from ECG. However, when the signal is severely affected by undesired artifacts, this becomes a challenging task. Sometimes in clinical environment, other physiological signals reflecting the cardiac activity such as ABP signal are also acquired simultaneously. Under the availability of such multimodal signals, the accuracy of R peak detection methods can be improved using sensor-fusion techniques. In the proposed method, the sample entropy (SampEn) is used as a metric for assessing the noise content in the physiological signal and the R peaks in ECG and the systolic peaks in ABP signals are fused together to enhance the efficiency of heartbeat detection. The proposed method was evaluated on the 100 records from the computing in cardiology challenge 2014 training data set. The performance parameters are: sensitivity (Se) and positive predictivity (PPV). The unimodal R peaks detector achieved: Se gross = 99.40%, PPV gross = 99.29%, Se average = 99.37%, PPV average = 99.29%. Similarly unimodal BP delineator achieved Se gross = 99.93%, PPV gross = 99.99%, Se average = 99.93%, PPV average = 99.99% whereas, the proposed multimodal beat detector achieved: Se gross = 99.65%, PPV gross = 99.91%, Se average = 99.68%, PPV average = 99.91%.

Parameter estimation method that directly compares gravitational wave observations to numerical relativity

NASA Astrophysics Data System (ADS)

Lange, J.; O'Shaughnessy, R.; Boyle, M.; Calderón Bustillo, J.; Campanelli, M.; Chu, T.; Clark, J. A.; Demos, N.; Fong, H.; Healy, J.; Hemberger, D. A.; Hinder, I.; Jani, K.; Khamesra, B.; Kidder, L. E.; Kumar, P.; Laguna, P.; Lousto, C. O.; Lovelace, G.; Ossokine, S.; Pfeiffer, H.; Scheel, M. A.; Shoemaker, D. M.; Szilagyi, B.; Teukolsky, S.; Zlochower, Y.

2017-11-01

We present and assess a Bayesian method to interpret gravitational wave signals from binary black holes. Our method directly compares gravitational wave data to numerical relativity (NR) simulations. In this study, we present a detailed investigation of the systematic and statistical parameter estimation errors of this method. This procedure bypasses approximations used in semianalytical models for compact binary coalescence. In this work, we use the full posterior parameter distribution for only generic nonprecessing binaries, drawing inferences away from the set of NR simulations used, via interpolation of a single scalar quantity (the marginalized log likelihood, ln L ) evaluated by comparing data to nonprecessing binary black hole simulations. We also compare the data to generic simulations, and discuss the effectiveness of this procedure for generic sources. We specifically assess the impact of higher order modes, repeating our interpretation with both l ≤2 as well as l ≤3 harmonic modes. Using the l ≤3 higher modes, we gain more information from the signal and can better constrain the parameters of the gravitational wave signal. We assess and quantify several sources of systematic error that our procedure could introduce, including simulation resolution and duration; most are negligible. We show through examples that our method can recover the parameters for equal mass, zero spin, GW150914-like, and unequal mass, precessing spin sources. Our study of this new parameter estimation method demonstrates that we can quantify and understand the systematic and statistical error. This method allows us to use higher order modes from numerical relativity simulations to better constrain the black hole binary parameters.

Acoustic signal propagation and measurement in natural stream channels for application to surrogate bed load measurements: Halfmoon Creek, Colorado

USDA-ARS?s Scientific Manuscript database

Monitoring sediment-generated noise using submerged hydrophones is a surrogate method for measuring bed load transport in streams with the potential for improving estimates of bed load transport through widespread, inexpensive monitoring. Understanding acoustic signal propagation in natural stream e...

Eliminating the influence of source spectrum of white light scanning interferometry through time-delay estimation algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yunfei; Cai, Hongzhi; Zhong, Liyun; Qiu, Xiang; Tian, Jindong; Lu, Xiaoxu

2017-05-01

In white light scanning interferometry (WLSI), the accuracy of profile measurement achieved with the conventional zero optical path difference (ZOPD) position locating method is closely related with the shape of interference signal envelope (ISE), which is mainly decided by the spectral distribution of illumination source. For a broadband light with Gaussian spectral distribution, the corresponding shape of ISE reveals a symmetric distribution, so the accurate ZOPD position can be achieved easily. However, if the spectral distribution of source is irregular, the shape of ISE will become asymmetric or complex multi-peak distribution, WLSI cannot work well through using ZOPD position locating method. Aiming at this problem, we propose time-delay estimation (TDE) based WLSI method, in which the surface profile information is achieved by using the relative displacement of interference signal between different pixels instead of the conventional ZOPD position locating method. Due to all spectral information of interference signal (envelope and phase) are utilized, in addition to revealing the advantage of high accuracy, the proposed method can achieve profile measurement with high accuracy in the case that the shape of ISE is irregular while ZOPD position locating method cannot work. That is to say, the proposed method can effectively eliminate the influence of source spectrum.

Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution

USGS Publications Warehouse

Huang, C.; Townshend, J.R.G.; Liang, S.; Kalluri, S.N.V.; DeFries, R.S.

2002-01-01

Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size. ?? 2002 Elsevier Science Inc. All rights reserved.

Surface EMG decomposition based on K-means clustering and convolution kernel compensation.

PubMed

Ning, Yong; Zhu, Xiangjun; Zhu, Shanan; Zhang, Yingchun

2015-03-01

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multichannel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multistep iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering-Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of -10 dB. More than 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A "two-source" test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multichannel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

Statistical issues in signal extraction from microarrays

NASA Astrophysics Data System (ADS)

Bergemann, Tracy; Quiaoit, Filemon; Delrow, Jeffrey J.; Zhao, Lue Ping

2001-06-01

Microarray technologies are increasingly used in biomedical research to study genome-wide expression profiles in the post genomic era. Their popularity is largely due to their high throughput and economical affordability. For example, microarrays have been applied to studies of cell cycle, regulatory circuitry, cancer cell lines, tumor tissues, and drug discoveries. One obstacle facing the continued success of applying microarray technologies, however, is the random variaton present on microarrays: within signal spots, between spots and among chips. In addition, signals extracted by available software packages seem to vary significantly. Despite a variety of software packages, it appears that there are two major approaches to signal extraction. One approach is to focus on the identification of signal regions and hence estimation of signal levels above background levels. The other approach is to use the distribution of intensity values as a way of identifying relevant signals. Building upon both approaches, the objective of our work is to develop a method that is statistically rigorous and also efficient and robust. Statistical issues to be considered here include: (1) how to refine grid alignment so that the overall variation is minimized, (2) how to estimate the signal levels relative to the local background levels as well as the variance of this estimate, and (3) how to integrate red and green channel signals so that the ratio of interest is stable, simultaneously relaxing distributional assumptions.

Reconstruction of ECG signals in presence of corruption.

PubMed

Ganeshapillai, Gartheeban; Liu, Jessica F; Guttag, John

2011-01-01

We present an approach to identifying and reconstructing corrupted regions in a multi-parameter physiological signal. The method, which uses information in correlated signals, is specifically designed to preserve clinically significant aspects of the signals. We use template matching to jointly segment the multi-parameter signal, morphological dissimilarity to estimate the quality of the signal segment, similarity search using features on a database of templates to find the closest match, and time-warping to reconstruct the corrupted segment with the matching template. In experiments carried out on the MIT-BIH Arrhythmia Database, a two-parameter database with many clinically significant arrhythmias, our method improved the classification accuracy of the beat type by more than 7 times on a signal corrupted with white Gaussian noise, and increased the similarity to the original signal, as measured by the normalized residual distance, by more than 2.5 times.

A concept for adaptive performance optimization on commercial transport aircraft

NASA Technical Reports Server (NTRS)

Jackson, Michael R.; Enns, Dale F.

1995-01-01

An adaptive control method is presented for the minimization of drag during flight for transport aircraft. The minimization of drag is achieved by taking advantage of the redundant control capability available in the pitch axis, with the horizontal tail used as the primary surface and symmetric deflection of the ailerons and cruise flaps used as additional controls. The additional control surfaces are excited with sinusoidal signals, while the altitude and velocity loops are closed with guidance and control laws. A model of the throttle response as a function of the additional control surfaces is formulated and the parameters in the model are estimated from the sensor measurements using a least squares estimation method. The estimated model is used to determine the minimum drag positions of the control surfaces. The method is presented for the optimization of one and two additional control surfaces. The adaptive control method is extended to optimize rate of climb with the throttle fixed. Simulations that include realistic disturbances are presented, as well as the results of a Monte Carlo simulation analysis that shows the effects of changing the disturbance environment and the excitation signal parameters.

Algorithm based on the short-term Rényi entropy and IF estimation for noisy EEG signals analysis.

PubMed

Lerga, Jonatan; Saulig, Nicoletta; Mozetič, Vladimir

2017-01-01

Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses. Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterization of the planetary boundary layer height and structure by Raman lidar: comparison of different approaches

NASA Astrophysics Data System (ADS)

Summa, D.; Di Girolamo, P.; Stelitano, D.; Cacciani, M.

2013-12-01

The planetary boundary layer (PBL) includes the portion of the atmosphere which is directly influenced by the presence of the earth's surface. Aerosol particles trapped within the PBL can be used as tracers to study the boundary-layer vertical structure and time variability. As a result of this, elastic backscatter signals collected by lidar systems can be used to determine the height and the internal structure of the PBL. The present analysis considers three different methods to estimate the PBL height. The first method is based on the determination of the first-order derivative of the logarithm of the range-corrected elastic lidar signals. Estimates of the PBL height for specific case studies obtained through this approach are compared with simultaneous estimates from the potential temperature profiles measured by radiosondes launched simultaneously to lidar operation. Additional estimates of the boundary layer height are based on the determination of the first-order derivative of the range-corrected rotational Raman lidar signals. This latter approach results to be successfully applicable also in the afternoon-evening decaying phase of the PBL, when the effectiveness of the approach based on the elastic lidar signals may be compromised or altered by the presence of the residual layer. Results from these different approaches are compared and discussed in the paper, with a specific focus on selected case studies collected by the University of Basilicata Raman lidar system BASIL during the Convective and Orographically-induced Precipitation Study (COPS).

Characterization of the planetary boundary layer height and structure by Raman lidar: comparison of different approaches

NASA Astrophysics Data System (ADS)

Summa, D.; Di Girolamo, P.; Stelitano, D.; Cacciani, M.

2013-06-01

The Planetary Boundary Layer (PBL) includes the portion of the atmosphere which is directly influenced by the presence of the Earth's surface. Aerosol particles trapped within the PBL can be used as tracers to study the boundary-layer vertical structure and time variability. As a result of this, elastic backscatter signals collected by lidar systems can be used to determine the height and the internal structure of the PBL. The present analysis considers three different methods to estimate the PBL height. A first method is based on the determination of the first order derivative of the logarithm of the range-corrected elastic lidar signals. Estimates of the PBL height for specific case studies obtained from this approach are compared with simultaneous estimates from the potential temperature profiles measured by radiosondes launched simultaneously to lidar operation. Additional estimates of the boundary layer height are based on the determination of the first order derivative of the range-corrected rotational Raman lidar signals. This latter approach results to be successfully applicable also in the afternoon-evening decaying phase of the PBL, when the effectiveness of the approach based on the elastic lidar signals may be compromised or altered by the presence of the residual layer. Results from these different approaches are compared and discussed in the paper, with a specific focus on selected case studies collected by the University of Basilicata Raman lidar system BASIL during the Convective and Orographically-induced Precipitation Study (COPS).

Estimation of chaotic coupled map lattices using symbolic vector dynamics

NASA Astrophysics Data System (ADS)

Wang, Kai; Pei, Wenjiang; Cheung, Yiu-ming; Shen, Yi; He, Zhenya

2010-01-01

In [K. Wang, W.J. Pei, Z.Y. He, Y.M. Cheung, Phys. Lett. A 367 (2007) 316], an original symbolic vector dynamics based method has been proposed for initial condition estimation in additive white Gaussian noisy environment. The estimation precision of this estimation method is determined by symbolic errors of the symbolic vector sequence gotten by symbolizing the received signal. This Letter further develops the symbolic vector dynamical estimation method. We correct symbolic errors with backward vector and the estimated values by using different symbols, and thus the estimation precision can be improved. Both theoretical and experimental results show that this algorithm enables us to recover initial condition of coupled map lattice exactly in both noisy and noise free cases. Therefore, we provide novel analytical techniques for understanding turbulences in coupled map lattice.

Instantaneous and Frequency-Warped Signal Processing Techniques for Auditory Source Separation.

NASA Astrophysics Data System (ADS)

Wang, Avery Li-Chun

This thesis summarizes several contributions to the areas of signal processing and auditory source separation. The philosophy of Frequency-Warped Signal Processing is introduced as a means for separating the AM and FM contributions to the bandwidth of a complex-valued, frequency-varying sinusoid p (n), transforming it into a signal with slowly-varying parameters. This transformation facilitates the removal of p (n) from an additive mixture while minimizing the amount of damage done to other signal components. The average winding rate of a complex-valued phasor is explored as an estimate of the instantaneous frequency. Theorems are provided showing the robustness of this measure. To implement frequency tracking, a Frequency-Locked Loop algorithm is introduced which uses the complex winding error to update its frequency estimate. The input signal is dynamically demodulated and filtered to extract the envelope. This envelope may then be remodulated to reconstruct the target partial, which may be subtracted from the original signal mixture to yield a new, quickly-adapting form of notch filtering. Enhancements to the basic tracker are made which, under certain conditions, attain the Cramer -Rao bound for the instantaneous frequency estimate. To improve tracking, the novel idea of Harmonic -Locked Loop tracking, using N harmonically constrained trackers, is introduced for tracking signals, such as voices and certain musical instruments. The estimated fundamental frequency is computed from a maximum-likelihood weighting of the N tracking estimates, making it highly robust. The result is that harmonic signals, such as voices, can be isolated from complex mixtures in the presence of other spectrally overlapping signals. Additionally, since phase information is preserved, the resynthesized harmonic signals may be removed from the original mixtures with relatively little damage to the residual signal. Finally, a new methodology is given for designing linear-phase FIR filters which require a small fraction of the computational power of conventional FIR implementations. This design strategy is based on truncated and stabilized IIR filters. These signal-processing methods have been applied to the problem of auditory source separation, resulting in voice separation from complex music that is significantly better than previous results at far lower computational cost.

76 FR 28215 - Notice of Intent To Grant Exclusive Patent License; Hadal, Inc.

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-16

...: Attitude Estimation Using Ground Imagery//Navy Case No. 98,801: Apparatus and Method For Grazing Angle Independent Signal Detection//Navy Case No. 98,946: Apparatus and Method For Compensating Images For...

Study on the description method of upper limb's muscle force levels during simulated in-orbit operations

NASA Astrophysics Data System (ADS)

Zhao, Yan; Li, DongXu; Liu, ZhiZhen; Liu, Liang

2013-03-01

The dexterous upper limb serves as the most important tool for astronauts to implement in-orbit experiments and operations. This study developed a simulated weightlessness experiment and invented new measuring equipment to quantitatively evaluate the muscle ability of the upper limb. Isometric maximum voluntary contractions (MVCs) and surface electromyography (sEMG) signals of right-handed pushing at the three positions were measured for eleven subjects. In order to enhance the comprehensiveness and accuracy of muscle force assessment, the study focused on signal processing techniques. We applied a combination method, which consists of time-, frequency-, and bi-frequency-domain analyses. Time- and frequency-domain analyses estimated the root mean square (RMS) and median frequency (MDF) of sEMG signals, respectively. Higher order spectra (HOS) of bi-frequency domain evaluated the maximum bispectrum amplitude ( B max), Gaussianity level (Sg) and linearity level (S l ) of sEMG signals. Results showed that B max, S l , and RMS values all increased as force increased. MDF and Sg values both declined as force increased. The research demonstrated that the combination method is superior to the conventional time- and frequency-domain analyses. The method not only described sEMG signal amplitude and power spectrum, but also deeper characterized phase coupling information and non-Gaussianity and non-linearity levels of sEMG, compared to two conventional analyses. The finding from the study can aid ergonomist to estimate astronaut muscle performance, so as to optimize in-orbit operation efficacy and minimize musculoskeletal injuries.

BACOM2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor

NASA Astrophysics Data System (ADS)

Fu, Yi; Yu, Guoqiang; Levine, Douglas A.; Wang, Niya; Shih, Ie-Ming; Zhang, Zhen; Clarke, Robert; Wang, Yue

2015-09-01

Most published copy number datasets on solid tumors were obtained from specimens comprised of mixed cell populations, for which the varying tumor-stroma proportions are unknown or unreported. The inability to correct for signal mixing represents a major limitation on the use of these datasets for subsequent analyses, such as discerning deletion types or detecting driver aberrations. We describe the BACOM2.0 method with enhanced accuracy and functionality to normalize copy number signals, detect deletion types, estimate tumor purity, quantify true copy numbers, and calculate average-ploidy value. While BACOM has been validated and used with promising results, subsequent BACOM analysis of the TCGA ovarian cancer dataset found that the estimated average tumor purity was lower than expected. In this report, we first show that this lowered estimate of tumor purity is the combined result of imprecise signal normalization and parameter estimation. Then, we describe effective allele-specific absolute normalization and quantification methods that can enhance BACOM applications in many biological contexts while in the presence of various confounders. Finally, we discuss the advantages of BACOM in relation to alternative approaches. Here we detail this revised computational approach, BACOM2.0, and validate its performance in real and simulated datasets.

Time-resolved flow reconstruction with indirect measurements using regression models and Kalman-filtered POD ROM

NASA Astrophysics Data System (ADS)

Leroux, Romain; Chatellier, Ludovic; David, Laurent

2018-01-01

This article is devoted to the estimation of time-resolved particle image velocimetry (TR-PIV) flow fields using a time-resolved point measurements of a voltage signal obtained by hot-film anemometry. A multiple linear regression model is first defined to map the TR-PIV flow fields onto the voltage signal. Due to the high temporal resolution of the signal acquired by the hot-film sensor, the estimates of the TR-PIV flow fields are obtained with a multiple linear regression method called orthonormalized partial least squares regression (OPLSR). Subsequently, this model is incorporated as the observation equation in an ensemble Kalman filter (EnKF) applied on a proper orthogonal decomposition reduced-order model to stabilize it while reducing the effects of the hot-film sensor noise. This method is assessed for the reconstruction of the flow around a NACA0012 airfoil at a Reynolds number of 1000 and an angle of attack of {20}°. Comparisons with multi-time delay-modified linear stochastic estimation show that both the OPLSR and EnKF combined with OPLSR are more accurate as they produce a much lower relative estimation error, and provide a faithful reconstruction of the time evolution of the velocity flow fields.

Using Doppler Shifts of GPS Signals To Measure Angular Speed

NASA Technical Reports Server (NTRS)

Campbell, Charles E., Jr.

2006-01-01

A method has been proposed for extracting information on the rate of rotation of an aircraft, spacecraft, or other body from differential Doppler shifts of Global Positioning System (GPS) signals received by antennas mounted on the body. In principle, the method should be capable of yielding low-noise estimates of rates of rotation. The method could eliminate the need for gyroscopes to measure rates of rotation. The method is based on the fact that for a given signal of frequency ft transmitted by a given GPS satellite, the differential Doppler shift is attributable to the difference between those components of the instantaneous translational velocities of the antennas that lie along the line of sight from the antennas to the GPS satellite.

Novel, continuous monitoring of fine‐scale movement using fixed‐position radiotelemetry arrays and random forest location fingerprinting

USGS Publications Warehouse

Harbicht, Andrew B.; Castro-Santos, Theodore R.; Ardren, William R.; Gorsky, Dimitry; Fraser, Dylan

2017-01-01

Radio‐tag signals from fixed‐position antennas are most often used to indicate presence or absence of individuals, or to estimate individual activity levels from signal strength variation within an antenna's detection zone. The potential of such systems to provide more precise information on tag location and movement has not been explored in great detail in an ecological setting.By reversing the roles that transmitters and receivers play in localization methods common to the telecommunications industry, we present a new telemetric tool for accurately estimating the location of tagged individuals from received signal strength values. The methods used to characterize the study area in terms of received signal strength are described, as is the random forest model used for localization. The resulting method is then validated using test data before being applied to true data collected from tagged individuals in the study site.Application of the localization method to test data withheld from the learning dataset indicated a low average error over the entire study area (<1 m), whereas application of the localization method to real data produced highly probable results consistent with field observations.This telemetric approach provided detailed movement data for tagged fish along a single axis (a migratory path) and is particularly useful for monitoring passage along migratory routes. The new methods applied in this study can also be expanded to include multiple axes (x, y, z) and multiple environments (aquatic and terrestrial) for remotely monitoring wildlife movement.

LCMV beamforming for a novel wireless local positioning system: a stationarity analysis

NASA Astrophysics Data System (ADS)

Tong, Hui; Zekavat, Seyed A.

2005-05-01

In this paper, we discuss the implementation of Linear Constrained Minimum Variance (LCMV) beamforming (BF) for a novel Wireless Local Position System (WLPS). WLPS main components are: (a) a dynamic base station (DBS), and (b) a transponder (TRX), both mounted on mobiles. WLPS might be considered as a node in a Mobile Adhoc NETwork (MANET). Each TRX is assigned an identification (ID) code. DBS transmits periodic short bursts of energy which contains an ID request (IDR) signal. The TRX transmits back its ID code (a signal with a limited duration) to the DBS as soon as it detects the IDR signal. Hence, the DBS receives non-continuous signals transmitted by TRX. In this work, we assume asynchronous Direct-Sequence Code Division Multiple Access (DS-CDMA) transmission from the TRX with antenna array/LCMV BF mounted at the DBS, and we discuss the implementation of the observed signal covariance matrix for LCMV BF. In LCMV BF, the observed covariance matrix should be estimated. Usually sample covariance matrix (SCM) is used to estimate this covariance matrix assuming a stationary model for the observed data which is the case in many communication systems. However, due to the non-stationary behavior of the received signal in WLPS systems, SCM does not lead to a high WLPS performance compared to even a conventional beamformer. A modified covariance matrix estimation method which utilizes the cyclostationarity property of WLPS system is introduced as a solution to this problem. It is shown that this method leads to a significant improvement in the WLPS performance.

An Efficient Estimator for Moving Target Localization Using Multi-Station Dual-Frequency Radars.

PubMed

Huang, Jiyan; Zhang, Ying; Luo, Shan

2017-12-15

Localization of a moving target in a dual-frequency radars system has now gained considerable attention. The noncoherent localization approach based on a least squares (LS) estimator has been addressed in the literature. Compared with the LS method, a novel localization method based on a two-step weighted least squares estimator is proposed to increase positioning accuracy for a multi-station dual-frequency radars system in this paper. The effects of signal noise ratio and the number of samples on the performance of range estimation are also analyzed in the paper. Furthermore, both the theoretical variance and Cramer-Rao lower bound (CRLB) are derived. The simulation results verified the proposed method.

An Efficient Estimator for Moving Target Localization Using Multi-Station Dual-Frequency Radars

PubMed Central

Zhang, Ying; Luo, Shan

2017-01-01

Localization of a moving target in a dual-frequency radars system has now gained considerable attention. The noncoherent localization approach based on a least squares (LS) estimator has been addressed in the literature. Compared with the LS method, a novel localization method based on a two-step weighted least squares estimator is proposed to increase positioning accuracy for a multi-station dual-frequency radars system in this paper. The effects of signal noise ratio and the number of samples on the performance of range estimation are also analyzed in the paper. Furthermore, both the theoretical variance and Cramer–Rao lower bound (CRLB) are derived. The simulation results verified the proposed method. PMID:29244727

Estimation and Mitigation of Channel Non-Reciprocity in Massive MIMO

NASA Astrophysics Data System (ADS)

Raeesi, Orod; Gokceoglu, Ahmet; Valkama, Mikko

2018-05-01

Time-division duplex (TDD) based massive MIMO systems rely on the reciprocity of the wireless propagation channels when calculating the downlink precoders based on uplink pilots. However, the effective uplink and downlink channels incorporating the analog radio front-ends of the base station (BS) and user equipments (UEs) exhibit non-reciprocity due to non-identical behavior of the individual transmit and receive chains. When downlink precoder is not aware of such channel non-reciprocity (NRC), system performance can be significantly degraded due to NRC induced interference terms. In this work, we consider a general TDD-based massive MIMO system where frequency-response mismatches at both the BS and UEs, as well as the mutual coupling mismatch at the BS large-array system all coexist and induce channel NRC. Based on the NRC-impaired signal models, we first propose a novel iterative estimation method for acquiring both the BS and UE side NRC matrices and then also propose a novel NRC-aware downlink precoder design which utilizes the obtained estimates. Furthermore, an efficient pilot signaling scheme between the BS and UEs is introduced in order to facilitate executing the proposed estimation method and the NRC-aware precoding technique in practical systems. Comprehensive numerical results indicate substantially improved spectral efficiency performance when the proposed NRC estimation and NRC-aware precoding methods are adopted, compared to the existing state-of-the-art methods.

Estimation of sea level variations with GPS/GLONASS-reflectometry technique

NASA Astrophysics Data System (ADS)

Padokhin, A. M.; Kurbatov, G. A.; Andreeva, E. S.; Nesterov, I. A.; Nazarenko, M. O.; Berbeneva, N. A.; Karlysheva, A. V.

2017-11-01

In the present paper we study GNSS - reflectometry methods for estimation of sea level variations using a single GNSSreceiver, which are based on the multipath propagation effects caused by the reflection of navigational signals from the sea surface. Such multipath propagation results in the appearance of the interference pattern in the Signal-to-Noise Ratio (SNR) of GNSS signals at small satellite elevation angles, which parameters are determined by the wavelength of the navigational signal and height of the antenna phase center above the reflecting sea surface. In current work we used GPS and GLONASS signals and measurements at two working frequencies of both systems to study sea level variations which almost doubles the amount of observations compared to GPS-only tide gauge. For UNAVCO sc02 station and collocated Friday Harbor NOAA tide gauge we show good agreement between GNSS-reflectometry and traditional mareograph sea level data.

Filter design for cancellation of baseline-fluctuation in needle EMG recordings.

PubMed

Rodríguez-Carreño, I; Malanda-Trigueros, A; Gila-Useros, L; Navallas-Irujo, J; Rodríguez-Falces, J

2006-01-01

Appropriate cancellation of the baseline fluctuation (BLF) is an important issue when recording EMG signals as it may degrade signal quality and distort qualitative and quantitative analysis. We present a novel filter-design approach for automatic cancellation of the BLF based on several signal processing techniques used sequentially. The methodology is to estimate the spectral content of the BLF, and then to use this estimation to design a high-pass FIR filter that cancel the BLF present in the signal. Two merit figures are devised for measuring the degree of BLF present in an EMG record. These figures are used to compare our method with the conventional approach, which naively considers the baseline course to be of constant (without any fluctuation) potential shift. Applications of the technique on real and simulated EMG signals show the superior performance of our approach in terms of both visual inspection and the merit figures.

Method and apparatus for autonomous, in-receiver prediction of GNSS ephemerides

NASA Technical Reports Server (NTRS)

Bar-Sever, Yoaz E. (Inventor); Bertiger, William I. (Inventor)

2012-01-01

Methods and apparatus for autonomous in-receiver prediction of orbit and clock states of Global Navigation Satellite Systems (GNSS) are described. Only the GNSS broadcast message is used, without need for periodic externally-communicated information. Earth orientation information is extracted from the GNSS broadcast ephemeris. With the accurate estimation of the Earth orientation parameters it is possible to propagate the best-fit GNSS orbits forward in time in an inertial reference frame. Using the estimated Earth orientation parameters, the predicted orbits are then transformed into Earth-Centered-Earth-Fixed (ECEF) coordinates to be used to assist the GNSS receiver in the acquisition of the signals. GNSS satellite clock states are also extracted from the broadcast ephemeris and a parameterized model of clock behavior is fit to that data. The estimated modeled clocks are then propagated forward in time to enable, together with the predicted orbits, quicker GNSS signal acquisition.

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

An efficient quantum algorithm for spectral estimation

NASA Astrophysics Data System (ADS)

Steffens, Adrian; Rebentrost, Patrick; Marvian, Iman; Eisert, Jens; Lloyd, Seth

2017-03-01

We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method, which determines the frequencies and damping factors of signals consisting of finite sums of exponentially damped sinusoids. Our algorithm provides a quantum speedup in a natural regime where the sampling rate is much higher than the number of sinusoid components. Along the way, we develop techniques that are expected to be useful for other quantum algorithms as well—consecutive phase estimations to efficiently make products of asymmetric low rank matrices classically accessible and an alternative method to efficiently exponentiate non-Hermitian matrices. Our algorithm features an efficient quantum-classical division of labor: the time-critical steps are implemented in quantum superposition, while an interjacent step, requiring much fewer parameters, can operate classically. We show that frequencies and damping factors can be obtained in time logarithmic in the number of sampling points, exponentially faster than known classical algorithms.

MNE software for processing MEG and EEG data

PubMed Central

Gramfort, A.; Luessi, M.; Larson, E.; Engemann, D.; Strohmeier, D.; Brodbeck, C.; Parkkonen, L.; Hämäläinen, M.

2013-01-01

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. PMID:24161808

New estimates of extensive-air-shower energies on the basis of signals in scintillation detectors

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

Anyutin, N. V.; Dedenko, L. G., E-mail: ddn@dec1.sinp.msu.ru; Roganova, T. M.

New formulas for estimating the energy of inclined extensive air showers (EASs) on the basis of signals in detectors by means of an original method and detailed tables of signals induced in scintillation detectors by photons, electrons, positrons, and muons and calculated with the aid of the GEANT4 code package were proposed in terms of the QGSJETII-04, EPOS LHC, and GHEISHA models. The parameters appearing in the proposed formulas were calculated by employing the CORSIKA code package. It is shown that, for showers of zenith angles in the range of 20◦–45◦, the standard constant-intensity-cut method, which is used to interpretmore » data from the Yakutsk EAS array, overestimates the shower energy by a factor of 1.2 to 1.5. It is proposed to employ the calculated VEM (Vertical Equivalent Muon) signal units of 10.8 and 11.4 MeV for, respectively, ground-based and underground scintillation detectors and to take into account the dependence of signals on the azimuthal angle of the detector position and fluctuations in the development of showers.« less

Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches

NASA Astrophysics Data System (ADS)

Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

2015-07-01

A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.

Demodulation of messages received with low signal to noise ratio

NASA Astrophysics Data System (ADS)

Marguinaud, A.; Quignon, T.; Romann, B.

The implementation of this all-digital demodulator is derived from maximum likelihood considerations applied to an analytical representation of the received signal. Traditional adapted filters and phase lock loops are replaced by minimum variance estimators and hypothesis tests. These statistical tests become very simple when working on phase signal. These methods, combined with rigorous control data representation allow significant computation savings as compared to conventional realizations. Nominal operation has been verified down to energetic signal over noise of -3 dB upon a QPSK demodulator.

Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

NASA Astrophysics Data System (ADS)

Mishra, C.; Samantaray, A. K.; Chakraborty, G.

2016-05-01

Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources. In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data1

Identification of the contribution of the ankle and hip joints to multi-segmental balance control

PubMed Central

2013-01-01

Background Human stance involves multiple segments, including the legs and trunk, and requires coordinated actions of both. A novel method was developed that reliably estimates the contribution of the left and right leg (i.e., the ankle and hip joints) to the balance control of individual subjects. Methods The method was evaluated using simulations of a double-inverted pendulum model and the applicability was demonstrated with an experiment with seven healthy and one Parkinsonian participant. Model simulations indicated that two perturbations are required to reliably estimate the dynamics of a double-inverted pendulum balance control system. In the experiment, two multisine perturbation signals were applied simultaneously. The balance control system dynamic behaviour of the participants was estimated by Frequency Response Functions (FRFs), which relate ankle and hip joint angles to joint torques, using a multivariate closed-loop system identification technique. Results In the model simulations, the FRFs were reliably estimated, also in the presence of realistic levels of noise. In the experiment, the participants responded consistently to the perturbations, indicated by low noise-to-signal ratios of the ankle angle (0.24), hip angle (0.28), ankle torque (0.07), and hip torque (0.33). The developed method could detect that the Parkinson patient controlled his balance asymmetrically, that is, the right ankle and hip joints produced more corrective torque. Conclusion The method allows for a reliable estimate of the multisegmental feedback mechanism that stabilizes stance, of individual participants and of separate legs. PMID:23433148

On the use of the noncentral chi-square density function for the distribution of helicopter spectral estimates

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.

Mutual information estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

For the automated, objective and joint analysis of time series, similarity measures are crucial. Used in the analysis of climate records, they allow for a complimentary, unbiased view onto sparse datasets. The irregular sampling of many of these time series, however, makes it necessary to either perform signal reconstruction (e.g. interpolation) or to develop and use adapted measures. Standard linear interpolation comes with an inevitable loss of information and bias effects. We have recently developed a Gaussian kernel-based correlation algorithm with which the interpolation error can be substantially lowered, but this would not work should the functional relationship in a bivariate setting be non-linear. We therefore propose an algorithm to estimate lagged auto and cross mutual information from irregularly sampled time series. We have extended the standard and adaptive binning histogram estimators and use Gaussian distributed weights in the estimation of the (joint) probabilities. To test our method we have simulated linear and nonlinear auto-regressive processes with Gamma-distributed inter-sampling intervals. We have then performed a sensitivity analysis for the estimation of actual coupling length, the lag of coupling and the decorrelation time in the synthetic time series and contrast our results to the performance of a signal reconstruction scheme. Finally we applied our estimator to speleothem records. We compare the estimated memory (or decorrelation time) to that from a least-squares estimator based on fitting an auto-regressive process of order 1. The calculated (cross) mutual information results are compared for the different estimators (standard or adaptive binning) and contrasted with results from signal reconstruction. We find that the kernel-based estimator has a significantly lower root mean square error and less systematic sampling bias than the interpolation-based method. It is possible that these encouraging results could be further improved by using non-histogram mutual information estimators, like k-Nearest Neighbor or Kernel-Density estimators, but for short (<1000 points) and irregularly sampled datasets the proposed algorithm is already a great improvement.

The a priori SDR Estimation Techniques with Reduced Speech Distortion for Acoustic Echo and Noise Suppression

NASA Astrophysics Data System (ADS)

Thoonsaengngam, Rattapol; Tangsangiumvisai, Nisachon

This paper proposes an enhanced method for estimating the a priori Signal-to-Disturbance Ratio (SDR) to be employed in the Acoustic Echo and Noise Suppression (AENS) system for full-duplex hands-free communications. The proposed a priori SDR estimation technique is modified based upon the Two-Step Noise Reduction (TSNR) algorithm to suppress the background noise while preserving speech spectral components. In addition, a practical approach to determine accurately the Echo Spectrum Variance (ESV) is presented based upon the linear relationship assumption between the power spectrum of far-end speech and acoustic echo signals. The ESV estimation technique is then employed to alleviate the acoustic echo problem. The performance of the AENS system that employs these two proposed estimation techniques is evaluated through the Echo Attenuation (EA), Noise Attenuation (NA), and two speech distortion measures. Simulation results based upon real speech signals guarantee that our improved AENS system is able to mitigate efficiently the problem of acoustic echo and background noise, while preserving the speech quality and speech intelligibility.

Real-Time Algebraic Derivative Estimations Using a Novel Low-Cost Architecture Based on Reconfigurable Logic

PubMed Central

Morales, Rafael; Rincón, Fernando; Gazzano, Julio Dondo; López, Juan Carlos

2014-01-01

Time derivative estimation of signals plays a very important role in several fields, such as signal processing and control engineering, just to name a few of them. For that purpose, a non-asymptotic algebraic procedure for the approximate estimation of the system states is used in this work. The method is based on results from differential algebra and furnishes some general formulae for the time derivatives of a measurable signal in which two algebraic derivative estimators run simultaneously, but in an overlapping fashion. The algebraic derivative algorithm presented in this paper is computed online and in real-time, offering high robustness properties with regard to corrupting noises, versatility and ease of implementation. Besides, in this work, we introduce a novel architecture to accelerate this algebraic derivative estimator using reconfigurable logic. The core of the algorithm is implemented in an FPGA, improving the speed of the system and achieving real-time performance. Finally, this work proposes a low-cost platform for the integration of hardware in the loop in MATLAB. PMID:24859033

Analysis of radiometric signal in sedimentating suspension flow in open channel

NASA Astrophysics Data System (ADS)

Zych, Marcin; Hanus, Robert; Petryka, Leszek; Świsulski, Dariusz; Doktor, Marek; Mastej, Wojciech

2015-05-01

The article discusses issues related to the estimation of the sedimentating solid particles average flow velocity in an open channel using radiometric methods. Due to the composition of the compound, which formed water and diatomite, received data have a very weak signal to noise ratio. In the process analysis the known determining of the solid phase transportation time delay the classical cross-correlation function is the most reliable method. The use of advanced frequency analysis based on mutual spectral density function and wavelet transform of recorded signals allows a reduction of the noise contribution.

Examination of the reliability of the crash modification factors using empirical Bayes method with resampling technique.

PubMed

Wang, Jung-Han; Abdel-Aty, Mohamed; Wang, Ling

2017-07-01

There have been plenty of studies intended to use different methods, for example, empirical Bayes before-after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before-after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle+Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Electromagnetic signal penetration in a planetary soil simulant: Estimated attenuation rates using GPR and TDR in volcanic deposits on Mount Etna

NASA Astrophysics Data System (ADS)

Lauro, S. E.; Mattei, E.; Cosciotti, B.; Di Paolo, F.; Arcone, S. A.; Viccaro, M.; Pettinelli, E.

2017-07-01

Ground-penetrating radar (GPR) is a well-established geophysical terrestrial exploration method and has recently become one of the most promising for planetary subsurface exploration. Several future landing vehicles like EXOMARS, 2020 NASA ROVER, and Chang'e-4, to mention a few, will host GPR. A GPR survey has been conducted on volcanic deposits on Mount Etna (Italy), considered a good analogue for Martian and Lunar volcanic terrains, to test a novel methodology for subsoil dielectric properties estimation. The stratigraphy of the volcanic deposits was investigated using 500 MHz and 1 GHz antennas in two different configurations: transverse electric and transverse magnetic. Sloping discontinuities have been used to estimate the loss tangents of the upper layer of such deposits by applying the amplitude-decay and frequency shift methods and approximating the GPR transmitted signal by Gaussian and Ricker wavelets. The loss tangent values, estimated using these two methodologies, were compared and validated with those retrieved from time domain reflectometry measurements acquired along the radar profiles. The results show that the proposed analysis, together with typical GPR methods for the estimation of the real part of permittivity, can be successfully used to characterize the electrical properties of planetary subsurface and to define some constraints on its lithology of the subsurface.

An Electromyographic-driven Musculoskeletal Torque Model using Neuro-Fuzzy System Identification: A Case Study

PubMed Central

Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza

2014-01-01

The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications. PMID:25426427

Estimation of laser beam pointing parameters in the presence of atmospheric turbulence.

PubMed

Borah, Deva K; Voelz, David G

2007-08-10

The problem of estimating mechanical boresight and jitter performance of a laser pointing system in the presence of atmospheric turbulence is considered. A novel estimator based on maximizing an average probability density function (pdf) of the received signal is presented. The proposed estimator uses a Gaussian far-field mean irradiance profile, and the irradiance pdf is assumed to be lognormal. The estimates are obtained using a sequence of return signal values from the intended target. Alternatively, one can think of the estimates being made by a cooperative target using the received signal samples directly. The estimator does not require sample-to-sample atmospheric turbulence parameter information. The approach is evaluated using wave optics simulation for both weak and strong turbulence conditions. Our results show that very good boresight and jitter estimation performance can be obtained under the weak turbulence regime. We also propose a novel technique to include the effect of very low received intensity values that cannot be measured well by the receiving device. The proposed technique provides significant improvement over a conventional approach where such samples are simply ignored. Since our method is derived from the lognormal irradiance pdf, the performance under strong turbulence is degraded. However, the ideas can be extended with appropriate pdf models to obtain more accurate results under strong turbulence conditions.

Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

NASA Astrophysics Data System (ADS)

Yi, Cancan; Lv, Yong; Xiao, Han; Huang, Tao; You, Guanghui

2018-04-01

Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time-frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time-frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time-frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling bearing system and a gear system. The results show that the proposed multisensor matching synchronous squeezing wavelet transform (MMSWT) is superior to existing methods.

Optimal reference polarization states for the calibration of general Stokes polarimeters in the presence of noise

NASA Astrophysics Data System (ADS)

Mu, Tingkui; Bao, Donghao; Zhang, Chunmin; Chen, Zeyu; Song, Jionghui

2018-07-01

During the calibration of the system matrix of a Stokes polarimeter using reference polarization states (RPSs) and pseudo-inversion estimation method, the measurement intensities are usually noised by the signal-independent additive Gaussian noise or signal-dependent Poisson shot noise, the precision of the estimated system matrix is degraded. In this paper, we present a paradigm for selecting RPSs to improve the precision of the estimated system matrix in the presence of both types of noise. The analytical solution of the precision of the system matrix estimated with the RPSs are derived. Experimental measurements from a general Stokes polarimeter show that accurate system matrix is estimated with the optimal RPSs, which are generated using two rotating quarter-wave plates. The advantage of using optimal RPSs is a reduction in measurement time with high calibration precision.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

NASA Astrophysics Data System (ADS)

Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

2016-12-01

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

Assessment of Receiver Signal Strength Sensing for Location Estimation Based on Fisher Information

PubMed Central

Nielsen, John; Nielsen, Christopher

2016-01-01

Currently there is almost ubiquitous availability of wireless signaling for data communications within commercial building complexes resulting in receiver signal strength (RSS) observables that are typically sufficient for generating viable location estimates of mobile wireless devices. However, while RSS observables are generally plentiful, achieving an accurate estimation of location is difficult due to several factors affecting the electromagnetic coupling between the mobile antenna and the building access points that are not modeled and hence contribute to the overall estimation uncertainty. Such uncertainty is typically mitigated with a moderate redundancy of RSS sensor observations in combination with other constraints imposed on the mobile trajectory. In this paper, the Fisher Information (FI) of a set of RSS sensor observations in the context of variables related to the mobile location is developed. This provides a practical method of determining the potential location accuracy for the given set of wireless signals available. Furthermore, the information value of individual RSS measurements can be quantified and the RSS observables weighted accordingly in estimation combining algorithms. The practical utility of using FI in this context was demonstrated experimentally with an extensive set of RSS measurements recorded in an office complex. The resulting deviation of the mobile location estimation based on application of weighted likelihood processing to the experimental RSS data was shown to agree closely with the Cramer Rao bound determined from the FI analysis. PMID:27669262

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Chromatic dispersion estimation based on heterodyne detection for coherent optical communication systems

NASA Astrophysics Data System (ADS)

Li, Yong; Yang, Aiying; Guo, Peng; Qiao, Yaojun; Lu, Yueming

2018-01-01

We propose an accurate and nondata-aided chromatic dispersion (CD) estimation method involving the use of the cross-correlation function of two heterodyne detection signals for coherent optical communication systems. Simulations are implemented to verify the feasibility of the proposed method for 28-GBaud coherent systems with different modulation formats. The results show that the proposed method has high accuracy for measuring CD and has good robustness against laser phase noise, amplified spontaneous emission noise, and nonlinear impairments.

Using the ratio of the magnetic field to the analytic signal of the magnetic gradient tensor in determining the position of simple shaped magnetic anomalies

NASA Astrophysics Data System (ADS)

Karimi, Kurosh; Shirzaditabar, Farzad

2017-08-01

The analytic signal of magnitude of the magnetic field’s components and its first derivatives have been employed for locating magnetic structures, which can be considered as point-dipoles or line of dipoles. Although similar methods have been used for locating such magnetic anomalies, they cannot estimate the positions of anomalies in noisy states with an acceptable accuracy. The methods are also inexact in determining the depth of deep anomalies. In noisy cases and in places other than poles, the maximum points of the magnitude of the magnetic vector components and Az are not located exactly above 3D bodies. Consequently, the horizontal location estimates of bodies are accompanied by errors. Here, the previous methods are altered and generalized to locate deeper models in the presence of noise even at lower magnetic latitudes. In addition, a statistical technique is presented for working in noisy areas and a new method, which is resistant to noise by using a ‘depths mean’ method, is made. Reduction to the pole transformation is also used to find the most possible actual horizontal body location. Deep models are also well estimated. The method is tested on real magnetic data over an urban gas pipeline in the vicinity of Kermanshah province, Iran. The estimated location of the pipeline is accurate in accordance with the result of the half-width method.

Estimation of continuous multi-DOF finger joint kinematics from surface EMG using a multi-output Gaussian Process.

PubMed

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro

2014-01-01

Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs. We show that estimation of finger joints from muscle activation inputs can be improved by using a regression model that considers inherent coupling or correlation within the hand and finger joints. We also provide a comparison of estimation performance between different regression methods, such as Artificial Neural Networks (ANN) which is used by many of the related studies. We show that using a multi-output GP gives improved estimation compared to multi-output ANN and even dedicated or independent regression models.

Characterization of highly multiplexed monolithic PET / gamma camera detector modules.

PubMed

Pierce, L A; Pedemonte, S; DeWitt, D; MacDonald, L; Hunter, W C J; Van Leemput, K; Miyaoka, R

2018-03-29

PET detectors use signal multiplexing to reduce the total number of electronics channels needed to cover a given area. Using measured thin-beam calibration data, we tested a principal component based multiplexing scheme for scintillation detectors. The highly-multiplexed detector signal is no longer amenable to standard calibration methodologies. In this study we report results of a prototype multiplexing circuit, and present a new method for calibrating the detector module with multiplexed data. A [Formula: see text] mm 3 LYSO scintillation crystal was affixed to a position-sensitive photomultiplier tube with [Formula: see text] position-outputs and one channel that is the sum of the other 64. The 65-channel signal was multiplexed in a resistive circuit, with 65:5 or 65:7 multiplexing. A 0.9 mm beam of 511 keV photons was scanned across the face of the crystal in a 1.52 mm grid pattern in order to characterize the detector response. New methods are developed to reject scattered events and perform depth-estimation to characterize the detector response of the calibration data. Photon interaction position estimation of the testing data was performed using a Gaussian Maximum Likelihood estimator and the resolution and scatter-rejection capabilities of the detector were analyzed. We found that using a 7-channel multiplexing scheme (65:7 compression ratio) with 1.67 mm depth bins had the best performance with a beam-contour of 1.2 mm FWHM (from the 0.9 mm beam) near the center of the crystal and 1.9 mm FWHM near the edge of the crystal. The positioned events followed the expected Beer-Lambert depth distribution. The proposed calibration and positioning method exhibited a scattered photon rejection rate that was a 55% improvement over the summed signal energy-windowing method.

A Robust High-Accuracy Ultrasound Indoor Positioning System Based on a Wireless Sensor Network

PubMed Central

Qi, Jun; Liu, Guo-Ping

2017-01-01

This paper describes the development and implementation of a robust high-accuracy ultrasonic indoor positioning system (UIPS). The UIPS consists of several wireless ultrasonic beacons in the indoor environment. Each of them has a fixed and known position coordinate and can collect all the transmissions from the target node or emit ultrasonic signals. Every wireless sensor network (WSN) node has two communication modules: one is WiFi, that transmits the data to the server, and the other is the radio frequency (RF) module, which is only used for time synchronization between different nodes, with accuracy up to 1 μs. The distance between the beacon and the target node is calculated by measuring the time-of-flight (TOF) for the ultrasonic signal, and then the position of the target is computed by some distances and the coordinate of the beacons. TOF estimation is the most important technique in the UIPS. A new time domain method to extract the envelope of the ultrasonic signals is presented in order to estimate the TOF. This method, with the envelope detection filter, estimates the value with the sampled values on both sides based on the least squares method (LSM). The simulation results show that the method can achieve envelope detection with a good filtering effect by means of the LSM. The highest precision and variance can reach 0.61 mm and 0.23 mm, respectively, in pseudo-range measurements with UIPS. A maximum location error of 10.2 mm is achieved in the positioning experiments for a moving robot, when UIPS works on the line-of-sight (LOS) signal. PMID:29113126

Estimation of Delta Wave by Mutual Information of Heartbeat During Sleep

NASA Astrophysics Data System (ADS)

Kurihara, Yosuke; Watanabe, Kajiro; Kobayashi, Kazuyuki; Tanaka, Hiroshi

The quality of sleep is evaluated based on the sleep stages judged by R-K method or the manual of American Academy of Sleep Medicine. The brainwaves, eye movements, and chin EMG of sleeping subjects are used for the judgment. These methods above, however, require some electrodes to be attached to the head and the face to obtain the brainwaves, eye movements, and chin EMG, thus making the measurements troublesome to be held on a daily basis. If non-invasive measurements of brainwaves, eye movements, and chin EMG are feasible, or their equivalent data can be estimated through other bio-signals, the monitoring of the quality of daily sleeps, which influences the health condition, will be easy. In this paper, we discuss the appearance rate of delta wave occurrences, which is deeply related with the depth of sleep, can be estimated based on the average amount of mutual information calculated by pulse wave signals and body movements measured non-invasively by the pneumatic method. As a result, the root mean square error between the appearance rate of delta wave occurrences measured with a polysomnography and the estimated delta pulse was 14.93%.

Thermal noise calculation method for precise estimation of the signal-to-noise ratio of ultra-low-field MRI with an atomic magnetometer.

PubMed

Yamashita, Tatsuya; Oida, Takenori; Hamada, Shoji; Kobayashi, Tetsuo

2012-02-01

In recent years, there has been considerable interest in developing an ultra-low-field magnetic resonance imaging (ULF-MRI) system using an optically pumped atomic magnetometer (OPAM). However, a precise estimation of the signal-to-noise ratio (SNR) of ULF-MRI has not been carried out. Conventionally, to calculate the SNR of an MR image, thermal noise, also called Nyquist noise, has been estimated by considering a resistor that is electrically equivalent to a biological-conductive sample and is connected in series to a pickup coil. However, this method has major limitations in that the receiver has to be a coil and that it cannot be applied directly to a system using OPAM. In this paper, we propose a method to estimate the thermal noise of an MRI system using OPAM. We calculate the thermal noise from the variance of the magnetic sensor output produced by current-dipole moments that simulate thermally fluctuating current sources in a biological sample. We assume that the random magnitude of the current dipole in each volume element of the biological sample is described by the Maxwell-Boltzmann distribution. The sensor output produced by each current-dipole moment is calculated either by an analytical formula or a numerical method based on the boundary element method. We validate the proposed method by comparing our results with those obtained by conventional methods that consider resistors connected in series to a pickup coil using single-layered sphere, multi-layered sphere, and realistic head models. Finally, we apply the proposed method to the ULF-MRI model using OPAM as the receiver with multi-layered sphere and realistic head models and estimate their SNR. Copyright © 2011 Elsevier Inc. All rights reserved.

A Practical Cone-beam CT Scatter Correction Method with Optimized Monte Carlo Simulations for Image-Guided Radiation Therapy

PubMed Central

Xu, Yuan; Bai, Ti; Yan, Hao; Ouyang, Luo; Pompos, Arnold; Wang, Jing; Zhou, Linghong; Jiang, Steve B.; Jia, Xun

2015-01-01

Cone-beam CT (CBCT) has become the standard image guidance tool for patient setup in image-guided radiation therapy. However, due to its large illumination field, scattered photons severely degrade its image quality. While kernel-based scatter correction methods have been used routinely in the clinic, it is still desirable to develop Monte Carlo (MC) simulation-based methods due to their accuracy. However, the high computational burden of the MC method has prevented routine clinical application. This paper reports our recent development of a practical method of MC-based scatter estimation and removal for CBCT. In contrast with conventional MC approaches that estimate scatter signals using a scatter-contaminated CBCT image, our method used a planning CT image for MC simulation, which has the advantages of accurate image intensity and absence of image truncation. In our method, the planning CT was first rigidly registered with the CBCT. Scatter signals were then estimated via MC simulation. After scatter signals were removed from the raw CBCT projections, a corrected CBCT image was reconstructed. The entire workflow was implemented on a GPU platform for high computational efficiency. Strategies such as projection denoising, CT image downsampling, and interpolation along the angular direction were employed to further enhance the calculation speed. We studied the impact of key parameters in the workflow on the resulting accuracy and efficiency, based on which the optimal parameter values were determined. Our method was evaluated in numerical simulation, phantom, and real patient cases. In the simulation cases, our method reduced mean HU errors from 44 HU to 3 HU and from 78 HU to 9 HU in the full-fan and the half-fan cases, respectively. In both the phantom and the patient cases, image artifacts caused by scatter, such as ring artifacts around the bowtie area, were reduced. With all the techniques employed, we achieved computation time of less than 30 sec including the time for both the scatter estimation and CBCT reconstruction steps. The efficacy of our method and its high computational efficiency make our method attractive for clinical use. PMID:25860299

Joint deconvolution and classification with applications to passive acoustic underwater multipath.

PubMed

Anderson, Hyrum S; Gupta, Maya R

2008-11-01

This paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time-invariant (LTI) filtering such as multipath, given labeled uncorrupted training signals. A maximum a posteriori approach to the deconvolution and classification is considered, which produces estimates of the desired signal, the unknown channel, and the class label. For cases in which only a class label is needed, the classification accuracy can be improved by not committing to an estimate of the channel or signal. A variant of the quadratic discriminant analysis (QDA) classifier is proposed that probabilistically accounts for the unknown LTI filtering, and which avoids deconvolution. The proposed QDA classifier can work either directly on the signal or on features whose transformation by LTI filtering can be analyzed; as an example a classifier for subband-power features is derived. Results on simulated data and real Bowhead whale vocalizations show that jointly considering deconvolution with classification can dramatically improve classification performance over traditional methods over a range of signal-to-noise ratios.

A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data.

PubMed

Adams, Dean C

2014-09-01

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (K(mult)) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of K(mult) remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K(mult) and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of K(mult) were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that K(mult) provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN

PubMed Central

Wu, Changcheng; Zeng, Hong; Song, Aiguo; Xu, Baoguo

2017-01-01

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the human's hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA) is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the human's hand can be correctly estimated by using sEMG and GRNN method. PMID:28713231

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN.

PubMed

Wu, Changcheng; Zeng, Hong; Song, Aiguo; Xu, Baoguo

2017-01-01

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the human's hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA) is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the human's hand can be correctly estimated by using sEMG and GRNN method.

Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

PubMed Central

Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

2016-01-01

Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. PMID:27322267

Emg Amplitude Estimators Based on Probability Distribution for Muscle-Computer Interface

NASA Astrophysics Data System (ADS)

Phinyomark, Angkoon; Quaine, Franck; Laurillau, Yann; Thongpanja, Sirinee; Limsakul, Chusak; Phukpattaranont, Pornchai

To develop an advanced muscle-computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.

Novel approaches to estimating the turbulent kinetic energy dissipation rate from low- and moderate-resolution velocity fluctuation time series

NASA Astrophysics Data System (ADS)

Wacławczyk, Marta; Ma, Yong-Feng; Kopeć, Jacek M.; Malinowski, Szymon P.

2017-11-01

In this paper we propose two approaches to estimating the turbulent kinetic energy (TKE) dissipation rate, based on the zero-crossing method by Sreenivasan et al. (1983). The original formulation requires a fine resolution of the measured signal, down to the smallest dissipative scales. However, due to finite sampling frequency, as well as measurement errors, velocity time series obtained from airborne experiments are characterized by the presence of effective spectral cutoffs. In contrast to the original formulation the new approaches are suitable for use with signals originating from airborne experiments. The suitability of the new approaches is tested using measurement data obtained during the Physics of Stratocumulus Top (POST) airborne research campaign as well as synthetic turbulence data. They appear useful and complementary to existing methods. We show the number-of-crossings-based approaches respond differently to errors due to finite sampling and finite averaging than the classical power spectral method. Hence, their application for the case of short signals and small sampling frequencies is particularly interesting, as it can increase the robustness of turbulent kinetic energy dissipation rate retrieval.

Bypassing the Limits of Ll Regularization: Convex Sparse Signal Processing Using Non-Convex Regularization

NASA Astrophysics Data System (ADS)

Parekh, Ankit

Sparsity has become the basis of some important signal processing methods over the last ten years. Many signal processing problems (e.g., denoising, deconvolution, non-linear component analysis) can be expressed as inverse problems. Sparsity is invoked through the formulation of an inverse problem with suitably designed regularization terms. The regularization terms alone encode sparsity into the problem formulation. Often, the ℓ1 norm is used to induce sparsity, so much so that ℓ1 regularization is considered to be `modern least-squares'. The use of ℓ1 norm, as a sparsity-inducing regularizer, leads to a convex optimization problem, which has several benefits: the absence of extraneous local minima, well developed theory of globally convergent algorithms, even for large-scale problems. Convex regularization via the ℓ1 norm, however, tends to under-estimate the non-zero values of sparse signals. In order to estimate the non-zero values more accurately, non-convex regularization is often favored over convex regularization. However, non-convex regularization generally leads to non-convex optimization, which suffers from numerous issues: convergence may be guaranteed to only a stationary point, problem specific parameters may be difficult to set, and the solution is sensitive to the initialization of the algorithm. The first part of this thesis is aimed toward combining the benefits of non-convex regularization and convex optimization to estimate sparse signals more effectively. To this end, we propose to use parameterized non-convex regularizers with designated non-convexity and provide a range for the non-convex parameter so as to ensure that the objective function is strictly convex. By ensuring convexity of the objective function (sum of data-fidelity and non-convex regularizer), we can make use of a wide variety of convex optimization algorithms to obtain the unique global minimum reliably. The second part of this thesis proposes a non-linear signal decomposition technique for an important biomedical signal processing problem: the detection of sleep spindles and K-complexes in human sleep electroencephalography (EEG). We propose a non-linear model for the EEG consisting of three components: (1) a transient (sparse piecewise constant) component, (2) a low-frequency component, and (3) an oscillatory component. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, we propose a fast non-linear optimization algorithm to estimate the three components in the proposed signal model. The low-frequency and oscillatory components are then used to estimate the K-complexes and sleep spindles respectively. The proposed detection method is shown to outperform several state-of-the-art automated sleep spindles detection methods.

Estimation of hyper-parameters for a hierarchical model of combined cortical and extra-brain current sources in the MEG inverse problem.

PubMed

Morishige, Ken-ichi; Yoshioka, Taku; Kawawaki, Dai; Hiroe, Nobuo; Sato, Masa-aki; Kawato, Mitsuo

2014-11-01

One of the major obstacles in estimating cortical currents from MEG signals is the disturbance caused by magnetic artifacts derived from extra-cortical current sources such as heartbeats and eye movements. To remove the effect of such extra-brain sources, we improved the hybrid hierarchical variational Bayesian method (hyVBED) proposed by Fujiwara et al. (NeuroImage, 2009). hyVBED simultaneously estimates cortical and extra-brain source currents by placing dipoles on cortical surfaces as well as extra-brain sources. This method requires EOG data for an EOG forward model that describes the relationship between eye dipoles and electric potentials. In contrast, our improved approach requires no EOG and less a priori knowledge about the current variance of extra-brain sources. We propose a new method, "extra-dipole," that optimally selects hyper-parameter values regarding current variances of the cortical surface and extra-brain source dipoles. With the selected parameter values, the cortical and extra-brain dipole currents were accurately estimated from the simulated MEG data. The performance of this method was demonstrated to be better than conventional approaches, such as principal component analysis and independent component analysis, which use only statistical properties of MEG signals. Furthermore, we applied our proposed method to measured MEG data during covert pursuit of a smoothly moving target and confirmed its effectiveness. Copyright © 2014 Elsevier Inc. All rights reserved.

Direction of Arrival Estimation with a Novel Single-Port Smart Antenna

NASA Astrophysics Data System (ADS)

Sun, Chen; Karmakar, Nemai C.

2004-12-01

A novel direction of arrival (DOA) estimation technique that uses the conventional multiple-signal classification (MUSIC) algorithm with periodic signals is applied to a single-port smart antenna. Results show that the proposed method gives a high-resolution (1 degree) DOA estimation in an uncorrelated signal environment. The novelty lies in that the MUSIC algorithm is applied to a simplified antenna configuration. Only 1 analogue-to-digital converter (ADC) is used in this antenna, which features low power consumption, low cost, and ease of fabrication. Modifications to the conventional MUSIC algorithm do not bring much additional complexity. The proposed technique is also free from the negative influence by the mutual coupling among antenna elements. Therefore, it offers an economical way to extensively implement smart antennas into the existing wireless mobile communications systems, especially at the power consumption limited mobile terminals such as laptops in wireless networks.

Compressive Video Recovery Using Block Match Multi-Frame Motion Estimation Based on Single Pixel Cameras

PubMed Central

Bi, Sheng; Zeng, Xiao; Tang, Xin; Qin, Shujia; Lai, King Wai Chiu

2016-01-01

Compressive sensing (CS) theory has opened up new paths for the development of signal processing applications. Based on this theory, a novel single pixel camera architecture has been introduced to overcome the current limitations and challenges of traditional focal plane arrays. However, video quality based on this method is limited by existing acquisition and recovery methods, and the method also suffers from being time-consuming. In this paper, a multi-frame motion estimation algorithm is proposed in CS video to enhance the video quality. The proposed algorithm uses multiple frames to implement motion estimation. Experimental results show that using multi-frame motion estimation can improve the quality of recovered videos. To further reduce the motion estimation time, a block match algorithm is used to process motion estimation. Experiments demonstrate that using the block match algorithm can reduce motion estimation time by 30%. PMID:26950127

Robust detection of heartbeats using association models from blood pressure and EEG signals.

PubMed

Jeon, Taegyun; Yu, Jongmin; Pedrycz, Witold; Jeon, Moongu; Lee, Boreom; Lee, Byeongcheol

2016-01-15

The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats. The proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets. To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.

Robust and intelligent bearing estimation

DOEpatents

Claassen, John P.

2000-01-01

A method of bearing estimation comprising quadrature digital filtering of event observations, constructing a plurality of observation matrices each centered on a time-frequency interval, determining for each observation matrix a parameter such as degree of polarization, linearity of particle motion, degree of dyadicy, or signal-to-noise ratio, choosing observation matrices most likely to produce a set of best available bearing estimates, and estimating a bearing for each observation matrix of the chosen set.

Improved estimation of parametric images of cerebral glucose metabolic rate from dynamic FDG-PET using volume-wise principle component analysis

NASA Astrophysics Data System (ADS)

Dai, Xiaoqian; Tian, Jie; Chen, Zhe

2010-03-01

Parametric images can represent both spatial distribution and quantification of the biological and physiological parameters of tracer kinetics. The linear least square (LLS) method is a well-estimated linear regression method for generating parametric images by fitting compartment models with good computational efficiency. However, bias exists in LLS-based parameter estimates, owing to the noise present in tissue time activity curves (TTACs) that propagates as correlated error in the LLS linearized equations. To address this problem, a volume-wise principal component analysis (PCA) based method is proposed. In this method, firstly dynamic PET data are properly pre-transformed to standardize noise variance as PCA is a data driven technique and can not itself separate signals from noise. Secondly, the volume-wise PCA is applied on PET data. The signals can be mostly represented by the first few principle components (PC) and the noise is left in the subsequent PCs. Then the noise-reduced data are obtained using the first few PCs by applying 'inverse PCA'. It should also be transformed back according to the pre-transformation method used in the first step to maintain the scale of the original data set. Finally, the obtained new data set is used to generate parametric images using the linear least squares (LLS) estimation method. Compared with other noise-removal method, the proposed method can achieve high statistical reliability in the generated parametric images. The effectiveness of the method is demonstrated both with computer simulation and with clinical dynamic FDG PET study.

Gravity Compensation Method for Combined Accelerometer and Gyro Sensors Used in Cardiac Motion Measurements.

PubMed

Krogh, Magnus Reinsfelt; Nghiem, Giang M; Halvorsen, Per Steinar; Elle, Ole Jakob; Grymyr, Ole-Johannes; Hoff, Lars; Remme, Espen W

2017-05-01

A miniaturized accelerometer fixed to the heart can be used for monitoring of cardiac function. However, an accelerometer cannot differentiate between acceleration caused by motion and acceleration due to gravity. The accuracy of motion measurements is therefore dependent on how well the gravity component can be estimated and filtered from the measured signal. In this study we propose a new method for estimating the gravity, based on strapdown inertial navigation, using a combined accelerometer and gyro. The gyro was used to estimate the orientation of the gravity field and thereby remove it. We compared this method with two previously proposed gravity filtering methods in three experimental models using: (1) in silico computer simulated heart motion; (2) robot mimicked heart motion; and (3) in vivo measured motion on the heart in an animal model. The new method correlated excellently with the reference (r 2  > 0.93) and had a deviation from reference peak systolic displacement (6.3 ± 3.9 mm) below 0.2 ± 0.5 mm for the robot experiment model. The new method performed significantly better than the two previously proposed methods (p < 0.001). The results show that the proposed method using gyro can measure cardiac motion with high accuracy and performs better than existing methods for filtering the gravity component from the accelerometer signal.

Nonparametric identification of nonlinear dynamic systems using a synchronisation-based method

NASA Astrophysics Data System (ADS)

Kenderi, Gábor; Fidlin, Alexander

2014-12-01

The present study proposes an identification method for highly nonlinear mechanical systems that does not require a priori knowledge of the underlying nonlinearities to reconstruct arbitrary restoring force surfaces between degrees of freedom. This approach is based on the master-slave synchronisation between a dynamic model of the system as the slave and the real system as the master using measurements of the latter. As the model synchronises to the measurements, it becomes an observer of the real system. The optimal observer algorithm in a least-squares sense is given by the Kalman filter. Using the well-known state augmentation technique, the Kalman filter can be turned into a dual state and parameter estimator to identify parameters of a priori characterised nonlinearities. The paper proposes an extension of this technique towards nonparametric identification. A general system model is introduced by describing the restoring forces as bilateral spring-dampers with time-variant coefficients, which are estimated as augmented states. The estimation procedure is followed by an a posteriori statistical analysis to reconstruct noise-free restoring force characteristics using the estimated states and their estimated variances. Observability is provided using only one measured mechanical quantity per degree of freedom, which makes this approach less demanding in the number of necessary measurement signals compared with truly nonparametric solutions, which typically require displacement, velocity and acceleration signals. Additionally, due to the statistical rigour of the procedure, it successfully addresses signals corrupted by significant measurement noise. In the present paper, the method is described in detail, which is followed by numerical examples of one degree of freedom (1DoF) and 2DoF mechanical systems with strong nonlinearities of vibro-impact type to demonstrate the effectiveness of the proposed technique.

Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

NASA Astrophysics Data System (ADS)

Popkov, Artem

2016-01-01

The article contains information about acoustic emission signals analysing using autocorrelation function. Operation factors were analysed, such as shape of signal, the origins time and carrier frequency. The purpose of work is estimating the validity of correlations methods analysing signals. Acoustic emission signal consist of different types of waves, which propagate on different trajectories in object of control. Acoustic emission signal is amplitude-, phase- and frequency-modeling signal. It was described by carrier frequency at a given point of time. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for analysing signal. Usage autocorrelation function like indicator the origin time of acoustic emission signal raises validity localization of emitters.

Soil hydraulic material properties and layered architecture from time-lapse GPR

NASA Astrophysics Data System (ADS)

Jaumann, Stefan; Roth, Kurt

2018-04-01

Quantitative knowledge of the subsurface material distribution and its effective soil hydraulic material properties is essential to predict soil water movement. Ground-penetrating radar (GPR) is a noninvasive and nondestructive geophysical measurement method that is suitable to monitor hydraulic processes. Previous studies showed that the GPR signal from a fluctuating groundwater table is sensitive to the soil water characteristic and the hydraulic conductivity function. In this work, we show that the GPR signal originating from both the subsurface architecture and the fluctuating groundwater table is suitable to estimate the position of layers within the subsurface architecture together with the associated effective soil hydraulic material properties with inversion methods. To that end, we parameterize the subsurface architecture, solve the Richards equation, convert the resulting water content to relative permittivity with the complex refractive index model (CRIM), and solve Maxwell's equations numerically. In order to analyze the GPR signal, we implemented a new heuristic algorithm that detects relevant signals in the radargram (events) and extracts the corresponding signal travel time and amplitude. This algorithm is applied to simulated as well as measured radargrams and the detected events are associated automatically. Using events instead of the full wave regularizes the inversion focussing on the relevant measurement signal. For optimization, we use a global-local approach with preconditioning. Starting from an ensemble of initial parameter sets drawn with a Latin hypercube algorithm, we sequentially couple a simulated annealing algorithm with a Levenberg-Marquardt algorithm. The method is applied to synthetic as well as measured data from the ASSESS test site. We show that the method yields reasonable estimates for the position of the layers as well as for the soil hydraulic material properties by comparing the results to references derived from ground truth data as well as from time domain reflectometry (TDR).

Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-06-14

It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error.

Heart rate calculation from ensemble brain wave using wavelet and Teager-Kaiser energy operator.

PubMed

Srinivasan, Jayaraman; Adithya, V

2015-01-01

Electroencephalogram (EEG) signal artifacts are caused by various factors, such as, Electro-oculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG), movement artifact and line interference. The relatively high electrical energy cardiac activity causes EEG artifacts. In EEG signal processing the general approach is to remove the ECG signal. In this paper, we introduce an automated method to extract the ECG signal from EEG using wavelet and Teager-Kaiser energy operator for R-peak enhancement and detection. From the detected R-peaks the heart rate (HR) is calculated for clinical diagnosis. To check the efficiency of our method, we compare the HR calculated from ECG signal recorded in synchronous with EEG. The proposed method yields a mean error of 1.4% for the heart rate and 1.7% for mean R-R interval. The result illustrates that, proposed method can be used for ECG extraction from single channel EEG and used in clinical diagnosis like estimation for stress analysis, fatigue, and sleep stages classification studies as a multi-model system. In addition, this method eliminates the dependence of additional synchronous ECG in extraction of ECG from EEG signal process.

Data Driven Estimation of Transpiration from Net Water Fluxes: the TEA Algorithm

NASA Astrophysics Data System (ADS)

Nelson, J. A.; Carvalhais, N.; Cuntz, M.; Delpierre, N.; Knauer, J.; Migliavacca, M.; Ogee, J.; Reichstein, M.; Jung, M.

2017-12-01

The eddy covariance method, while powerful, can only provide a net accounting of ecosystem fluxes. Particularly with water cycle components, efforts to partitioning total evapotranspiration (ET) into the biotic component (transpiration, T) and the abiotic component (here evaporation, E) have seen limited success, with no one method emerging as a standard.Here we demonstrate a novel method that uses ecosystem WUE to predict transpiration in two steps: (1) a filtration step that to isolate the signal of ET for periods where E is minimized and ET is likely dominated by the signal of T; and (2) a step which predicts the WUE using meteorological variables, as well as information derived from the carbon and energy fluxes. To assess the the underlying assumptions, we tested the proposed method on three ecological models, allowing validation where the underlying carbon:water relationships, as well as the transpiration estimates, are know.The partitioning method shows high correlation (R²>0.8) between Tmodel/ET and TTEA/ET across timescales from half-hourly to annually, as well as capturing spatial variability across sites. Apart from predictive performance, we explore the sensitivities of the method to the underlying assumptions, such as the effects of residual evaporation in the training dataset. Furthermore, we show initial transpiration estimates from the algorithm at global scale, via the FLUXNET dataset.

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation

PubMed Central

Zimmer, Christoph

2016-01-01

Background Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models. Methods The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique. Results The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models. PMID:27583802

Vector Observation-Aided/Attitude-Rate Estimation Using Global Positioning System Signals

NASA Technical Reports Server (NTRS)

Oshman, Yaakov; Markley, F. Landis

1997-01-01

A sequential filtering algorithm is presented for attitude and attitude-rate estimation from Global Positioning System (GPS) differential carrier phase measurements. A third-order, minimal-parameter method for solving the attitude matrix kinematic equation is used to parameterize the filter's state, which renders the resulting estimator computationally efficient. Borrowing from tracking theory concepts, the angular acceleration is modeled as an exponentially autocorrelated stochastic process, thus avoiding the use of the uncertain spacecraft dynamic model. The new formulation facilitates the use of aiding vector observations in a unified filtering algorithm, which can enhance the method's robustness and accuracy. Numerical examples are used to demonstrate the performance of the method.

A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data.

PubMed

Kuntzelman, Karl; Jack Rhodes, L; Harrington, Lillian N; Miskovic, Vladimir

2018-06-01

There is a broad family of statistical methods for capturing time series regularity, with increasingly widespread adoption by the neuroscientific community. A common feature of these methods is that they permit investigators to quantify the entropy of brain signals - an index of unpredictability/complexity. Despite the proliferation of algorithms for computing entropy from neural time series data there is scant evidence concerning their relative stability and efficiency. Here we evaluated several different algorithmic implementations (sample, fuzzy, dispersion and permutation) of multiscale entropy in terms of their stability across sessions, internal consistency and computational speed, accuracy and precision using a combination of electroencephalogram (EEG) and synthetic 1/ƒ noise signals. Overall, we report fair to excellent internal consistency and longitudinal stability over a one-week period for the majority of entropy estimates, with several caveats. Computational timing estimates suggest distinct advantages for dispersion and permutation entropy over other entropy estimates. Considered alongside the psychometric evidence, we suggest several ways in which researchers can maximize computational resources (without sacrificing reliability), especially when working with high-density M/EEG data or multivoxel BOLD time series signals. Copyright © 2018 Elsevier Inc. All rights reserved.

Acoustical Direction Finding with Time-Modulated Arrays

PubMed Central

Clark, Ben; Flint, James A.

2016-01-01

Time-Modulated Linear Arrays (TMLAs) offer useful efficiency savings over conventional phased arrays when applied in parameter estimation applications. The present paper considers the application of TMLAs to acoustic systems and proposes an algorithm for efficiently deriving the arrival angle of a signal. The proposed technique is applied in the frequency domain, where the signal and harmonic content is captured. Using a weighted average method on harmonic amplitudes and their respective main beam angles, it is possible to determine an estimate for the signal’s direction of arrival. The method is demonstrated and evaluated using results from both numerical and practical implementations and performance data is provided. The use of Micro-Electromechanical Systems (MEMS) sensors allows time-modulation techniques to be applied at ultrasonic frequencies. Theoretical predictions for an array of five isotropic elements with half-wavelength spacing and 1000 data samples suggest an accuracy of ±1∘ within an angular range of approximately ±50∘. In experiments of a 40 kHz five-element microphone array, a Direction of Arrival (DoA) estimation within ±2.5∘ of the target signal is readily achieved inside a ±45∘ range using a single switched input stage and a simple hardware setup. PMID:27973432

Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MIMO Radar.

PubMed

Wang, Xianpeng; Wang, Wei; Li, Xin; Liu, Qi; Liu, Jing

2016-04-14

In this paper, a novel sparsity-aware direction of arrival (DOA) estimation scheme for a noncircular source is proposed in multiple-input multiple-output (MIMO) radar. In the proposed method, the reduced-dimensional transformation technique is adopted to eliminate the redundant elements. Then, exploiting the noncircularity of signals, a joint sparsity-aware scheme based on the reweighted l1 norm penalty is formulated for DOA estimation, in which the diagonal elements of the weight matrix are the coefficients of the noncircular MUSIC-like (NC MUSIC-like) spectrum. Compared to the existing l1 norm penalty-based methods, the proposed scheme provides higher angular resolution and better DOA estimation performance. Results from numerical experiments are used to show the effectiveness of our proposed method.

Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM.

PubMed

Amiralizadeh, Siamak; Nguyen, An T; Rusch, Leslie A

2013-08-26

We investigate the performance of digital filter back-propagation (DFBP) using coarse parameter estimation for mitigating SOA nonlinearity in coherent communication systems. We introduce a simple, low overhead method for parameter estimation for DFBP based on error vector magnitude (EVM) as a figure of merit. The bit error rate (BER) penalty achieved with this method has negligible penalty as compared to DFBP with fine parameter estimation. We examine different bias currents for two commercial SOAs used as booster amplifiers in our experiments to find optimum operating points and experimentally validate our method. The coarse parameter DFBP efficiently compensates SOA-induced nonlinearity for both SOA types in 80 km propagation of 16-QAM signal at 22 Gbaud.

Carrier frequency offset estimation for an acoustic-electric channel using 16 QAM modulation

NASA Astrophysics Data System (ADS)

Cunningham, Michael T.; Anderson, Leonard A.; Wilt, Kyle R.; Chakraborty, Soumya; Saulnier, Gary J.; Scarton, Henry A.

2016-05-01

Acoustic-electric channels can be used to send data through metallic barriers, enabling communications where electromagnetic signals are ineffective. This paper considers an acoustic-electric channel that is formed by mounting piezoelectric transducers on metallic barriers that are separated by a thin water layer. The transducers are coupled to the barriers using epoxy and the barriers are positioned to axially-align the PZTs, maximizing energy transfer efficiency. The electrical signals are converted by the transmitting transducers into acoustic waves, which propagate through the elastic walls and water medium to the receiving transducers. The reverberation of the acoustic signals in these channels can produce multipath distortion with a significant delay spread that introduces inter-symbol interference (ISI) into the received signal. While the multipath effects can be severe, the channel does not change rapidly which makes equalization easier. Here we implement a 16-QAM system on this channel, including a method for obtaining accurate carrier frequency offset (CFO) estimates in the presence of the quasi-static multipath propagation. A raised-power approach is considered but found to suffer from excessive data noise resulting from the ISI. An alternative approach that utilizes a pilot tone burst at the start of a data packet is used for CFO estimation and found to be effective. The autocorrelation method is used to estimate the frequency of the received burst. A real-time prototype of the 16 QAM system that uses a Texas Instruments MSP430 microcontroller-based transmitter and a personal computer-based receiver is presented along with performance results.

Analysis of Maneuvering Targets with Complex Motions by Two-Dimensional Product Modified Lv's Distribution for Quadratic Frequency Modulation Signals.

PubMed

Jing, Fulong; Jiao, Shuhong; Hou, Changbo; Si, Weijian; Wang, Yu

2017-06-21

For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF) and modified scaled Fourier transform (mSFT), an effective parameter estimation algorithm is proposed-referred to as the Two-Dimensional product modified Lv's distribution (2D-PMLVD)-for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT) and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified.

Estimation of the sensitive volume for gravitational-wave source populations using weighted Monte Carlo integration

NASA Astrophysics Data System (ADS)

Tiwari, Vaibhav

2018-07-01

The population analysis and estimation of merger rates of compact binaries is one of the important topics in gravitational wave astronomy. The primary ingredient in these analyses is the population-averaged sensitive volume. Typically, sensitive volume, of a given search to a given simulated source population, is estimated by drawing signals from the population model and adding them to the detector data as injections. Subsequently injections, which are simulated gravitational waveforms, are searched for by the search pipelines and their signal-to-noise ratio (SNR) is determined. Sensitive volume is estimated, by using Monte-Carlo (MC) integration, from the total number of injections added to the data, the number of injections that cross a chosen threshold on SNR and the astrophysical volume in which the injections are placed. So far, only fixed population models have been used in the estimation of binary black holes (BBH) merger rates. However, as the scope of population analysis broaden in terms of the methodologies and source properties considered, due to an increase in the number of observed gravitational wave (GW) signals, the procedure will need to be repeated multiple times at a large computational cost. In this letter we address the problem by performing a weighted MC integration. We show how a single set of generic injections can be weighted to estimate the sensitive volume for multiple population models; thereby greatly reducing the computational cost. The weights in this MC integral are the ratios of the output probabilities, determined by the population model and standard cosmology, and the injection probability, determined by the distribution function of the generic injections. Unlike analytical/semi-analytical methods, which usually estimate sensitive volume using single detector sensitivity, the method is accurate within statistical errors, comes at no added cost and requires minimal computational resources.

The evaluation of distributed damage in concrete based on sinusoidal modeling of the ultrasonic response.

PubMed

Sepehrinezhad, Alireza; Toufigh, Vahab

2018-05-25

Ultrasonic wave attenuation is an effective descriptor of distributed damage in inhomogeneous materials. Methods developed to measure wave attenuation have the potential to provide an in-site evaluation of existing concrete structures insofar as they are accurate and time-efficient. In this study, material classification and distributed damage evaluation were investigated based on the sinusoidal modeling of the response from the through-transmission ultrasonic tests on polymer concrete specimens. The response signal was modeled as single or the sum of damping sinusoids. Due to the inhomogeneous nature of concrete materials, model parameters may vary from one specimen to another. Therefore, these parameters are not known in advance and should be estimated while the response signal is being received. The modeling procedure used in this study involves a data-adaptive algorithm to estimate the parameters online. Data-adaptive algorithms are used due to a lack of knowledge of the model parameters. The damping factor was estimated as a descriptor of the distributed damage. The results were compared in two different cases as follows: (1) constant excitation frequency with varying concrete mixtures and (2) constant mixture with varying excitation frequencies. The specimens were also loaded up to their ultimate compressive strength to investigate the effect of distributed damage in the response signal. The results of the estimation indicated that the damping was highly sensitive to the change in material inhomogeneity, even in comparable mixtures. In addition to the proposed method, three methods were employed to compare the results based on their accuracy in the classification of materials and the evaluation of the distributed damage. It is shown that the estimated damping factor is not only sensitive to damage in the final stages of loading, but it is also applicable in evaluating micro damages in the earlier stages providing a reliable descriptor of damage. In addition, the modified amplitude ratio method is introduced as an improvement of the classical method. The proposed methods were validated to be effective descriptors of distributed damage. The presented models were also in good agreement with the experimental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Cost estimation: An expert-opinion approach. [cost analysis of research projects using the Delphi method (forecasting)

NASA Technical Reports Server (NTRS)

Buffalano, C.; Fogleman, S.; Gielecki, M.

1976-01-01

A methodology is outlined which can be used to estimate the costs of research and development projects. The approach uses the Delphi technique a method developed by the Rand Corporation for systematically eliciting and evaluating group judgments in an objective manner. The use of the Delphi allows for the integration of expert opinion into the cost-estimating process in a consistent and rigorous fashion. This approach can also signal potential cost-problem areas. This result can be a useful tool in planning additional cost analysis or in estimating contingency funds. A Monte Carlo approach is also examined.

Method for detection and correction of errors in speech pitch period estimates

NASA Technical Reports Server (NTRS)

Bhaskar, Udaya (Inventor)

1989-01-01

A method of detecting and correcting received values of a pitch period estimate of a speech signal for use in a speech coder or the like. An average is calculated of the nonzero values of received pitch period estimate since the previous reset. If a current pitch period estimate is within a range of 0.75 to 1.25 times the average, it is assumed correct, while if not, a correction process is carried out. If correction is required successively for more than a preset number of times, which will most likely occur when the speaker changes, the average is discarded and a new average calculated.

Deterministic-random separation in nonstationary regime

NASA Astrophysics Data System (ADS)

Abboud, D.; Antoni, J.; Sieg-Zieba, S.; Eltabach, M.

2016-02-01

In rotating machinery vibration analysis, the synchronous average is perhaps the most widely used technique for extracting periodic components. Periodic components are typically related to gear vibrations, misalignments, unbalances, blade rotations, reciprocating forces, etc. Their separation from other random components is essential in vibration-based diagnosis in order to discriminate useful information from masking noise. However, synchronous averaging theoretically requires the machine to operate under stationary regime (i.e. the related vibration signals are cyclostationary) and is otherwise jeopardized by the presence of amplitude and phase modulations. A first object of this paper is to investigate the nature of the nonstationarity induced by the response of a linear time-invariant system subjected to speed varying excitation. For this purpose, the concept of a cyclo-non-stationary signal is introduced, which extends the class of cyclostationary signals to speed-varying regimes. Next, a "generalized synchronous average'' is designed to extract the deterministic part of a cyclo-non-stationary vibration signal-i.e. the analog of the periodic part of a cyclostationary signal. Two estimators of the GSA have been proposed. The first one returns the synchronous average of the signal at predefined discrete operating speeds. A brief statistical study of it is performed, aiming to provide the user with confidence intervals that reflect the "quality" of the estimator according to the SNR and the estimated speed. The second estimator returns a smoothed version of the former by enforcing continuity over the speed axis. It helps to reconstruct the deterministic component by tracking a specific trajectory dictated by the speed profile (assumed to be known a priori).The proposed method is validated first on synthetic signals and then on actual industrial signals. The usefulness of the approach is demonstrated on envelope-based diagnosis of bearings in variable-speed operation.

A satellite-based radar wind sensor

NASA Technical Reports Server (NTRS)

Xin, Weizhuang

1991-01-01

The objective is to investigate the application of Doppler radar systems for global wind measurement. A model of the satellite-based radar wind sounder (RAWS) is discussed, and many critical problems in the designing process, such as the antenna scan pattern, tracking the Doppler shift caused by satellite motion, and backscattering of radar signals from different types of clouds, are discussed along with their computer simulations. In addition, algorithms for measuring mean frequency of radar echoes, such as the Fast Fourier Transform (FFT) estimator, the covariance estimator, and the estimators based on autoregressive models, are discussed. Monte Carlo computer simulations were used to compare the performance of these algorithms. Anti-alias methods are discussed for the FFT and the autoregressive methods. Several algorithms for reducing radar ambiguity were studied, such as random phase coding methods and staggered pulse repitition frequncy (PRF) methods. Computer simulations showed that these methods are not applicable to the RAWS because of the broad spectral widths of the radar echoes from clouds. A waveform modulation method using the concept of spread spectrum and correlation detection was developed to solve the radar ambiguity. Radar ambiguity functions were used to analyze the effective signal-to-noise ratios for the waveform modulation method. The results showed that, with suitable bandwidth product and modulation of the waveform, this method can achieve the desired maximum range and maximum frequency of the radar system.

Reliability of stellar inclination estimated from asteroseismology: analytical criteria, mock simulations and Kepler data analysis

NASA Astrophysics Data System (ADS)

Kamiaka, Shoya; Benomar, Othman; Suto, Yasushi

2018-05-01

Advances in asteroseismology of solar-like stars, now provide a unique method to estimate the stellar inclination i⋆. This enables to evaluate the spin-orbit angle of transiting planetary systems, in a complementary fashion to the Rossiter-McLaughlineffect, a well-established method to estimate the projected spin-orbit angle λ. Although the asteroseismic method has been broadly applied to the Kepler data, its reliability has yet to be assessed intensively. In this work, we evaluate the accuracy of i⋆ from asteroseismology of solar-like stars using 3000 simulated power spectra. We find that the low signal-to-noise ratio of the power spectra induces a systematic under-estimate (over-estimate) bias for stars with high (low) inclinations. We derive analytical criteria for the reliable asteroseismic estimate, which indicates that reliable measurements are possible in the range of 20° ≲ i⋆ ≲ 80° only for stars with high signal-to-noise ratio. We also analyse and measure the stellar inclination of 94 Kepler main-sequence solar-like stars, among which 33 are planetary hosts. According to our reliability criteria, a third of them (9 with planets, 22 without) have accurate stellar inclination. Comparison of our asteroseismic estimate of vsin i⋆ against spectroscopic measurements indicates that the latter suffers from a large uncertainty possibly due to the modeling of macro-turbulence, especially for stars with projected rotation speed vsin i⋆ ≲ 5km/s. This reinforces earlier claims, and the stellar inclination estimated from the combination of measurements from spectroscopy and photometric variation for slowly rotating stars needs to be interpreted with caution.

Computer algorithm for analyzing and processing borehole strainmeter data

USGS Publications Warehouse

Langbein, John O.

2010-01-01

The newly installed Plate Boundary Observatory (PBO) strainmeters record signals from tectonic activity, Earth tides, and atmospheric pressure. Important information about tectonic processes may occur at amplitudes at and below tidal strains and pressure loading. If incorrect assumptions are made regarding the background noise in the strain data, then the estimates of tectonic signal amplitudes may be incorrect. Furthermore, the use of simplifying assumptions that data are uncorrelated can lead to incorrect results and pressure loading and tides may not be completely removed from the raw data. Instead, any algorithm used to process strainmeter data must incorporate the strong temporal correlations that are inherent with these data. The technique described here uses least squares but employs data covariance that describes the temporal correlation of strainmeter data. There are several advantages to this method since many parameters are estimated simultaneously. These parameters include: (1) functional terms that describe the underlying error model, (2) the tidal terms, (3) the pressure loading term(s), (4) amplitudes of offsets, either those from earthquakes or from the instrument, (5) rate and changes in rate, and (6) the amplitudes and time constants of either logarithmic or exponential curves that can characterize postseismic deformation or diffusion of fluids near the strainmeter. With the proper error model, realistic estimates of the standard errors of the various parameters are obtained; this is especially critical in determining the statistical significance of a suspected, tectonic strain signal. The program also provides a method of tracking the various adjustments required to process strainmeter data. In addition, the program provides several plots to assist with identifying either tectonic signals or other signals that may need to be removed before any geophysical signal can be identified.

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.

PubMed

Beda, Alessandro; Simpson, David M; Faes, Luca

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications in biomedical signal processing and beyond, providing a powerful new tool for assessing the confidence limits of indexes estimated from individual recordings.