Improvement of Accuracy for Background Noise Estimation Method Based on TPE-AE

NASA Astrophysics Data System (ADS)

Itai, Akitoshi; Yasukawa, Hiroshi

This paper proposes a method of a background noise estimation based on the tensor product expansion with a median and a Monte carlo simulation. We have shown that a tensor product expansion with absolute error method is effective to estimate a background noise, however, a background noise might not be estimated by using conventional method properly. In this paper, it is shown that the estimate accuracy can be improved by using proposed methods.

Robust Diagnosis Method Based on Parameter Estimation for an Interturn Short-Circuit Fault in Multipole PMSM under High-Speed Operation.

PubMed

Lee, Jewon; Moon, Seokbae; Jeong, Hyeyun; Kim, Sang Woo

2015-11-20

This paper proposes a diagnosis method for a multipole permanent magnet synchronous motor (PMSM) under an interturn short circuit fault. Previous works in this area have suffered from the uncertainties of the PMSM parameters, which can lead to misdiagnosis. The proposed method estimates the q-axis inductance (Lq) of the faulty PMSM to solve this problem. The proposed method also estimates the faulty phase and the value of G, which serves as an index of the severity of the fault. The q-axis current is used to estimate the faulty phase, the values of G and Lq. For this reason, two open-loop observers and an optimization method based on a particle-swarm are implemented. The q-axis current of a healthy PMSM is estimated by the open-loop observer with the parameters of a healthy PMSM. The Lq estimation significantly compensates for the estimation errors in high-speed operation. The experimental results demonstrate that the proposed method can estimate the faulty phase, G, and Lq besides exhibiting robustness against parameter uncertainties.

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Distributed processing of a GPS receiver network for a regional ionosphere map

NASA Astrophysics Data System (ADS)

Choi, Kwang Ho; Hoo Lim, Joon; Yoo, Won Jae; Lee, Hyung Keun

2018-01-01

This paper proposes a distributed processing method applicable to GPS receivers in a network to generate a regional ionosphere map accurately and reliably. For accuracy, the proposed method is operated by multiple local Kalman filters and Kriging estimators. Each local Kalman filter is applied to a dual-frequency receiver to estimate the receiver’s differential code bias and vertical ionospheric delays (VIDs) at different ionospheric pierce points. The Kriging estimator selects and combines several VID estimates provided by the local Kalman filters to generate the VID estimate at each ionospheric grid point. For reliability, the proposed method uses receiver fault detectors and satellite fault detectors. Each receiver fault detector compares the VID estimates of the same local area provided by different local Kalman filters. Each satellite fault detector compares the VID estimate of each local area with that projected from the other local areas. Compared with the traditional centralized processing method, the proposed method is advantageous in that it considerably reduces the computational burden of each single Kalman filter and enables flexible fault detection, isolation, and reconfiguration capability. To evaluate the performance of the proposed method, several experiments with field collected measurements were performed.

Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems.

PubMed

Chen, Tengpeng; Foo, Yi Shyh Eddy; Ling, K V; Chen, Xuebing

2017-10-11

In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load.

Efficient Regressions via Optimally Combining Quantile Information*

PubMed Central

Zhao, Zhibiao; Xiao, Zhijie

2014-01-01

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods. PMID:25484481

A Channelization-Based DOA Estimation Method for Wideband Signals

PubMed Central

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

2016-01-01

In this paper, we propose a novel direction of arrival (DOA) estimation method for wideband signals with sensor arrays. The proposed method splits the wideband array output into multiple frequency sub-channels and estimates the signal parameters using a digital channelization receiver. Based on the output sub-channels, a channelization-based incoherent signal subspace method (Channelization-ISM) and a channelization-based test of orthogonality of projected subspaces method (Channelization-TOPS) are proposed. Channelization-ISM applies narrowband signal subspace methods on each sub-channel independently. Then the arithmetic mean or geometric mean of the estimated DOAs from each sub-channel gives the final result. Channelization-TOPS measures the orthogonality between the signal and the noise subspaces of the output sub-channels to estimate DOAs. The proposed channelization-based method isolates signals in different bandwidths reasonably and improves the output SNR. It outperforms the conventional ISM and TOPS methods on estimation accuracy and dynamic range, especially in real environments. Besides, the parallel processing architecture makes it easy to implement on hardware. A wideband digital array radar (DAR) using direct wideband radio frequency (RF) digitization is presented. Experiments carried out in a microwave anechoic chamber with the wideband DAR are presented to demonstrate the performance. The results verify the effectiveness of the proposed method. PMID:27384566

Comparison of method using phase-sensitive motion estimator with speckle tracking method and application to measurement of arterial wall motion

NASA Astrophysics Data System (ADS)

Miyajo, Akira; Hasegawa, Hideyuki

2018-07-01

At present, the speckle tracking method is widely used as a two- or three-dimensional (2D or 3D) motion estimator for the measurement of cardiovascular dynamics. However, this method requires high-level interpolation of a function, which evaluates the similarity between ultrasonic echo signals in two frames, to estimate a subsample small displacement in high-frame-rate ultrasound, which results in a high computational cost. To overcome this problem, a 2D motion estimator using the 2D Fourier transform, which does not require any interpolation process, was proposed by our group. In this study, we compared the accuracies of the speckle tracking method and our method using a 2D motion estimator, and applied the proposed method to the measurement of motion of a human carotid arterial wall. The bias error and standard deviation in the lateral velocity estimates obtained by the proposed method were 0.048 and 0.282 mm/s, respectively, which were significantly better than those (‑0.366 and 1.169 mm/s) obtained by the speckle tracking method. The calculation time of the proposed phase-sensitive method was 97% shorter than the speckle tracking method. Furthermore, the in vivo experimental results showed that a characteristic change in velocity around the carotid bifurcation could be detected by the proposed method.

Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls

PubMed Central

Chae, Jeongsook; Jin, Yong; Sung, Yunsick

2018-01-01

Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%. PMID:29324641

A Pitch Extraction Method with High Frequency Resolution for Singing Evaluation

NASA Astrophysics Data System (ADS)

Takeuchi, Hideyo; Hoguro, Masahiro; Umezaki, Taizo

This paper proposes a pitch estimation method suitable for singing evaluation incorporable in KARAOKE machines. Professional singers and musicians have sharp hearing for music and singing voice. They recognize that singer's voice pitch is “a little off key” or “be in tune”. In the same way, the pitch estimation method that has high frequency resolution is necessary in order to evaluate singing. This paper proposes a pitch estimation method with high frequency resolution utilizing harmonic characteristic of autocorrelation function. The proposed method can estimate a fundamental frequency in the range 50 ∼ 1700[Hz] with resolution less than 3.6 cents in light processing.

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.

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.

Tracking of electrochemical impedance of batteries

NASA Astrophysics Data System (ADS)

Piret, H.; Granjon, P.; Guillet, N.; Cattin, V.

2016-04-01

This paper presents an evolutionary battery impedance estimation method, which can be easily embedded in vehicles or nomad devices. The proposed method not only allows an accurate frequency impedance estimation, but also a tracking of its temporal evolution contrary to classical electrochemical impedance spectroscopy methods. Taking into account constraints of cost and complexity, we propose to use the existing electronics of current control to perform a frequency evolutionary estimation of the electrochemical impedance. The developed method uses a simple wideband input signal, and relies on a recursive local average of Fourier transforms. The averaging is controlled by a single parameter, managing a trade-off between tracking and estimation performance. This normalized parameter allows to correctly adapt the behavior of the proposed estimator to the variations of the impedance. The advantage of the proposed method is twofold: the method is easy to embed into a simple electronic circuit, and the battery impedance estimator is evolutionary. The ability of the method to monitor the impedance over time is demonstrated on a simulator, and on a real Lithium ion battery, on which a repeatability study is carried out. The experiments reveal good tracking results, and estimation performance as accurate as the usual laboratory approaches.

Robust gaze-steering of an active vision system against errors in the estimated parameters

NASA Astrophysics Data System (ADS)

Han, Youngmo

2015-01-01

Gaze-steering is often used to broaden the viewing range of an active vision system. Gaze-steering procedures are usually based on estimated parameters such as image position, image velocity, depth and camera calibration parameters. However, there may be uncertainties in these estimated parameters because of measurement noise and estimation errors. In this case, robust gaze-steering cannot be guaranteed. To compensate for such problems, this paper proposes a gaze-steering method based on a linear matrix inequality (LMI). In this method, we first propose a proportional derivative (PD) control scheme on the unit sphere that does not use depth parameters. This proposed PD control scheme can avoid uncertainties in the estimated depth and camera calibration parameters, as well as inconveniences in their estimation process, including the use of auxiliary feature points and highly non-linear computation. Furthermore, the control gain of the proposed PD control scheme on the unit sphere is designed using LMI such that the designed control is robust in the presence of uncertainties in the other estimated parameters, such as image position and velocity. Simulation results demonstrate that the proposed method provides a better compensation for uncertainties in the estimated parameters than the contemporary linear method and steers the gaze of the camera more steadily over time than the contemporary non-linear method.

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar

PubMed Central

Wang, Xianpeng; Wang, Wei; Li, Xin; Liu, Jing

2015-01-01

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri–Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method. PMID:26569241

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar.

PubMed

Wang, Xianpeng; Wang, Wei; Li, Xin; Liu, Jing

2015-11-10

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri-Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method.

A Novel Residual Frequency Estimation Method for GNSS Receivers.

PubMed

Nguyen, Tu Thi-Thanh; La, Vinh The; Ta, Tung Hai

2018-01-04

In Global Navigation Satellite System (GNSS) receivers, residual frequency estimation methods are traditionally applied in the synchronization block to reduce the transient time from acquisition to tracking, or they are used within the frequency estimator to improve its accuracy in open-loop architectures. There are several disadvantages in the current estimation methods, including sensitivity to noise and wide search space size. This paper proposes a new residual frequency estimation method depending on differential processing. Although the complexity of the proposed method is higher than the one of traditional methods, it can lead to more accurate estimates, without increasing the size of the search space.

Novel blood pressure and pulse pressure estimation based on pulse transit time and stroke volume approximation.

PubMed

Lee, Joonnyong; Sohn, JangJay; Park, Jonghyun; Yang, SeungMan; Lee, Saram; Kim, Hee Chan

2018-06-18

Non-invasive continuous blood pressure monitors are of great interest to the medical community due to their value in hypertension management. Recently, studies have shown the potential of pulse pressure as a therapeutic target for hypertension, but not enough attention has been given to non-invasive continuous monitoring of pulse pressure. Although accurate pulse pressure estimation can be of direct value to hypertension management and indirectly to the estimation of systolic blood pressure, as it is the sum of pulse pressure and diastolic blood pressure, only a few inadequate methods of pulse pressure estimation have been proposed. We present a novel, non-invasive blood pressure and pulse pressure estimation method based on pulse transit time and pre-ejection period. Pre-ejection period and pulse transit time were measured non-invasively using electrocardiogram, seismocardiogram, and photoplethysmogram measured from the torso. The proposed method used the 2-element Windkessel model to model pulse pressure with the ratio of stroke volume, approximated by pre-ejection period, and arterial compliance, estimated by pulse transit time. Diastolic blood pressure was estimated using pulse transit time, and systolic blood pressure was estimated as the sum of the two estimates. The estimation method was verified in 11 subjects in two separate conditions with induced cardiovascular response and the results were compared against a reference measurement and values obtained from a previously proposed method. The proposed method yielded high agreement with the reference (pulse pressure correlation with reference R ≥ 0.927, diastolic blood pressure correlation with reference R ≥ 0.854, systolic blood pressure correlation with reference R ≥ 0.914) and high estimation accuracy in pulse pressure (mean root-mean-squared error ≤ 3.46 mmHg) and blood pressure (mean root-mean-squared error ≤ 6.31 mmHg for diastolic blood pressure and ≤ 8.41 mmHg for systolic blood pressure) over a wide range of hemodynamic changes. The proposed pulse pressure estimation method provides accurate estimates in situations with and without significant changes in stroke volume. The proposed method improves upon the currently available systolic blood pressure estimation methods by providing accurate pulse pressure estimates.

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

PubMed

Qin, Guoyou; Zhang, Jiajia; Zhu, Zhongyi; Fung, Wing

2016-12-20

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Estimation of Comfort/Disconfort Based on EEG in Massage by Use of Clustering according to Correration and Incremental Learning type NN

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Authors proposed the estimation method combining k-means algorithm and NN for evaluating massage. However, this estimation method has a problem that discrimination ratio is decreased to new user. There are two causes of this problem. One is that generalization of NN is bad. Another one is that clustering result by k-means algorithm has not high correlation coefficient in a class. Then, this research proposes k-means algorithm according to correlation coefficient and incremental learning for NN. The proposed k-means algorithm is method included evaluation function based on correlation coefficient. Incremental learning is method that NN is learned by new data and initialized weight based on the existing data. The effect of proposed methods are verified by estimation result using EEG data when testee is given massage.

Estimation of genetic parameters and their sampling variances of quantitative traits in the type 2 modified augmented design

USDA-ARS?s Scientific Manuscript database

We proposed a method to estimate the error variance among non-replicated genotypes, thus to estimate the genetic parameters by using replicated controls. We derived formulas to estimate sampling variances of the genetic parameters. Computer simulation indicated that the proposed methods of estimatin...

Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

NASA Astrophysics Data System (ADS)

Wei, Zhongbao; Tseng, King Jet; Wai, Nyunt; Lim, Tuti Mariana; Skyllas-Kazacos, Maria

2016-11-01

Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.

Improved remote gaze estimation using corneal reflection-adaptive geometric transforms

NASA Astrophysics Data System (ADS)

Ma, Chunfei; Baek, Seung-Jin; Choi, Kang-A.; Ko, Sung-Jea

2014-05-01

Recently, the remote gaze estimation (RGE) technique has been widely applied to consumer devices as a more natural interface. In general, the conventional RGE method estimates a user's point of gaze using a geometric transform, which represents the relationship between several infrared (IR) light sources and their corresponding corneal reflections (CRs) in the eye image. Among various methods, the homography normalization (HN) method achieves state-of-the-art performance. However, the geometric transform of the HN method requiring four CRs is infeasible for the case when fewer than four CRs are available. To solve this problem, this paper proposes a new RGE method based on three alternative geometric transforms, which are adaptive to the number of CRs. Unlike the HN method, the proposed method not only can operate with two or three CRs, but can also provide superior accuracy. To further enhance the performance, an effective error correction method is also proposed. By combining the introduced transforms with the error-correction method, the proposed method not only provides high accuracy and robustness for gaze estimation, but also allows for a more flexible system setup with a different number of IR light sources. Experimental results demonstrate the effectiveness of the proposed method.

An alternative empirical likelihood method in missing response problems and causal inference.

PubMed

Ren, Kaili; Drummond, Christopher A; Brewster, Pamela S; Haller, Steven T; Tian, Jiang; Cooper, Christopher J; Zhang, Biao

2016-11-30

Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data.

PubMed

Takeda, Yusuke; Hiroe, Nobuo; Yamashita, Okito; Sato, Masa-Aki

2016-06-01

Repetitive spatiotemporal patterns in spontaneous brain activities have been widely examined in non-human studies. These studies have reported that such patterns reflect past experiences embedded in neural circuits. In human magnetoencephalography (MEG) and electroencephalography (EEG) studies, however, spatiotemporal patterns in resting-state brain activities have not been extensively examined. This is because estimating spatiotemporal patterns from resting-state MEG/EEG data is difficult due to their unknown onsets. Here, we propose a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data, including MEG/EEG. Without the information of onsets, the proposed method can estimate several spatiotemporal patterns, even if they are overlapping. We verified the performance of the method by detailed simulation tests. Furthermore, we examined whether the proposed method could estimate the visual evoked magnetic fields (VEFs) without using stimulus onset information. The proposed method successfully detected the stimulus onsets and estimated the VEFs, implying the applicability of this method to real MEG data. The proposed method was applied to resting-state functional magnetic resonance imaging (fMRI) data and MEG data. The results revealed informative spatiotemporal patterns representing consecutive brain activities that dynamically change with time. Using this method, it is possible to reveal discrete events spontaneously occurring in our brains, such as memory retrieval. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Direct Importance Estimation with Gaussian Mixture Models

NASA Astrophysics Data System (ADS)

Yamada, Makoto; Sugiyama, Masashi

The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.

Simple method for quick estimation of aquifer hydrogeological parameters

NASA Astrophysics Data System (ADS)

Ma, C.; Li, Y. Y.

2017-08-01

Development of simple and accurate methods to determine the aquifer hydrogeological parameters was of importance for groundwater resources assessment and management. Aiming at the present issue of estimating aquifer parameters based on some data of the unsteady pumping test, a fitting function of Theis well function was proposed using fitting optimization method and then a unitary linear regression equation was established. The aquifer parameters could be obtained by solving coefficients of the regression equation. The application of the proposed method was illustrated, using two published data sets. By the error statistics and analysis on the pumping drawdown, it showed that the method proposed in this paper yielded quick and accurate estimates of the aquifer parameters. The proposed method could reliably identify the aquifer parameters from long distance observed drawdowns and early drawdowns. It was hoped that the proposed method in this paper would be helpful for practicing hydrogeologists and hydrologists.

Optimal back-extrapolation method for estimating plasma volume in humans using the indocyanine green dilution method.

PubMed

Polidori, David; Rowley, Clarence

2014-07-22

The indocyanine green dilution method is one of the methods available to estimate plasma volume, although some researchers have questioned the accuracy of this method. We developed a new, physiologically based mathematical model of indocyanine green kinetics that more accurately represents indocyanine green kinetics during the first few minutes postinjection than what is assumed when using the traditional mono-exponential back-extrapolation method. The mathematical model is used to develop an optimal back-extrapolation method for estimating plasma volume based on simulated indocyanine green kinetics obtained from the physiological model. Results from a clinical study using the indocyanine green dilution method in 36 subjects with type 2 diabetes indicate that the estimated plasma volumes are considerably lower when using the traditional back-extrapolation method than when using the proposed back-extrapolation method (mean (standard deviation) plasma volume = 26.8 (5.4) mL/kg for the traditional method vs 35.1 (7.0) mL/kg for the proposed method). The results obtained using the proposed method are more consistent with previously reported plasma volume values. Based on the more physiological representation of indocyanine green kinetics and greater consistency with previously reported plasma volume values, the new back-extrapolation method is proposed for use when estimating plasma volume using the indocyanine green dilution method.

Improvement of Vehicle Positioning Using Car-to-Car Communications in Consideration of Communication Delay

NASA Astrophysics Data System (ADS)

Hontani, Hidekata; Higuchi, Yuya

In this article, we propose a vehicle positioning method that can estimate positions of cars even in areas where the GPS is not available. For the estimation, each car measures the relative distance to a car running in front, communicates the measurements with other cars, and uses the received measurements for estimating its position. In order to estimate the position even if the measurements are received with time-delay, we employed the time-delay tolerant Kalman filtering. For sharing the measurements, it is assumed that a car-to-car communication system is used. Then, the measurements sent from farther cars are received with larger time-delay. It follows that the accuracy of the estimates of farther cars become worse. Hence, the proposed method manages only the states of nearby cars to reduce computing effort. The authors simulated the proposed filtering method and found that the proposed method estimates the positions of nearby cars as accurate as the distributed Kalman filtering.

Improving estimates of genetic maps: a meta-analysis-based approach.

PubMed

Stewart, William C L

2007-07-01

Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The method uses the variance of each independent map estimate to combine them efficiently, whether the map estimates use the same set of markers or not. As compared with a joint analysis of the pooled genotype data, the proposed method is attractive for three reasons: (1) it has comparable efficiency to the maximum likelihood map estimate when the pooled data are homogeneous; (2) relative to existing map estimation methods, it can have increased efficiency when the pooled data are heterogeneous; and (3) it avoids the practical difficulties of pooling human subjects data. On the basis of simulated data modeled after two real data sets, the proposed method can reduce the sampling variation of linkage maps commonly used in whole-genome linkage scans. Furthermore, when the independent map estimates are also maximum likelihood estimates, the proposed method performs as well as or better than when they are estimated by the program CRIMAP. Since variance estimates of maps may not always be available, I demonstrate the feasibility of three different variance estimators. Overall, the method should prove useful to investigators who need map positions for markers not contained in publicly available maps, and to those who wish to minimize the negative effects of inaccurate maps. Copyright 2007 Wiley-Liss, Inc.

Statistical inference for the additive hazards model under outcome-dependent sampling.

PubMed

Yu, Jichang; Liu, Yanyan; Sandler, Dale P; Zhou, Haibo

2015-09-01

Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer.

Statistical inference for the additive hazards model under outcome-dependent sampling

PubMed Central

Yu, Jichang; Liu, Yanyan; Sandler, Dale P.; Zhou, Haibo

2015-01-01

Cost-effective study design and proper inference procedures for data from such designs are always of particular interests to study investigators. In this article, we propose a biased sampling scheme, an outcome-dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo-score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer. PMID:26379363

Using Kriging with a heterogeneous measurement error to improve the accuracy of extreme precipitation return level estimation

NASA Astrophysics Data System (ADS)

Yin, Shui-qing; Wang, Zhonglei; Zhu, Zhengyuan; Zou, Xu-kai; Wang, Wen-ting

2018-07-01

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made.

Methods for Multiloop Identification of Visual and Neuromuscular Pilot Responses.

PubMed

Olivari, Mario; Nieuwenhuizen, Frank M; Venrooij, Joost; Bülthoff, Heinrich H; Pollini, Lorenzo

2015-12-01

In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails.

Adaptive torque estimation of robot joint with harmonic drive transmission

NASA Astrophysics Data System (ADS)

Shi, Zhiguo; Li, Yuankai; Liu, Guangjun

2017-11-01

Robot joint torque estimation using input and output position measurements is a promising technique, but the result may be affected by the load variation of the joint. In this paper, a torque estimation method with adaptive robustness and optimality adjustment according to load variation is proposed for robot joint with harmonic drive transmission. Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust. The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor and two representative filtering methods. The results have demonstrated the effectiveness of the proposed torque estimation technique.

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.

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

PubMed

Farsani, Zahra Amini; Schmid, Volker J

2017-01-01

In the estimation of physiological kinetic parameters from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data, the determination of the arterial input function (AIF) plays a key role. This paper proposes a Bayesian method to estimate the physiological parameters of DCE-MRI along with the AIF in situations, where no measurement of the AIF is available. In the proposed algorithm, the maximum entropy method (MEM) is combined with the maximum a posterior approach (MAP). To this end, MEM is used to specify a prior probability distribution of the unknown AIF. The ability of this method to estimate the AIF is validated using the Kullback-Leibler divergence. Subsequently, the kinetic parameters can be estimated with MAP. The proposed algorithm is evaluated with a data set from a breast cancer MRI study. The application shows that the AIF can reliably be determined from the DCE-MRI data using MEM. Kinetic parameters can be estimated subsequently. The maximum entropy method is a powerful tool to reconstructing images from many types of data. This method is useful for generating the probability distribution based on given information. The proposed method gives an alternative way to assess the input function from the existing data. The proposed method allows a good fit of the data and therefore a better estimation of the kinetic parameters. In the end, this allows for a more reliable use of DCE-MRI. Schattauer GmbH.

Pointwise nonparametric maximum likelihood estimator of stochastically ordered survivor functions

PubMed Central

Park, Yongseok; Taylor, Jeremy M. G.; Kalbfleisch, John D.

2012-01-01

In this paper, we consider estimation of survivor functions from groups of observations with right-censored data when the groups are subject to a stochastic ordering constraint. Many methods and algorithms have been proposed to estimate distribution functions under such restrictions, but none have completely satisfactory properties when the observations are censored. We propose a pointwise constrained nonparametric maximum likelihood estimator, which is defined at each time t by the estimates of the survivor functions subject to constraints applied at time t only. We also propose an efficient method to obtain the estimator. The estimator of each constrained survivor function is shown to be nonincreasing in t, and its consistency and asymptotic distribution are established. A simulation study suggests better small and large sample properties than for alternative estimators. An example using prostate cancer data illustrates the method. PMID:23843661

Transient Stability Output Margin Estimation Based on Energy Function Method

NASA Astrophysics Data System (ADS)

Miwa, Natsuki; Tanaka, Kazuyuki

In this paper, a new method of estimating critical generation margin (CGM) in power systems is proposed from the viewpoint of transient stability diagnostic. The proposed method has the capability to directly compute the stability limit output for a given contingency based on transient energy function method (TEF). Since CGM can be directly obtained by the limit output using estimated P-θ curves and is easy to understand, it is more useful rather than conventional critical clearing time (CCT) of energy function method. The proposed method can also estimate CGM as its negative value that means unstable in present load profile, then negative CGM can be directly utilized as generator output restriction. The proposed method is verified its accuracy and fast solution ability by applying to simple 3-machine model and IEEJ EAST10-machine standard model. Furthermore the useful application to severity ranking of transient stability for a lot of contingency cases is discussed by using CGM.

Crowd density estimation based on convolutional neural networks with mixed pooling

NASA Astrophysics Data System (ADS)

Zhang, Li; Zheng, Hong; Zhang, Ying; Zhang, Dongming

2017-09-01

Crowd density estimation is an important topic in the fields of machine learning and video surveillance. Existing methods do not provide satisfactory classification accuracy; moreover, they have difficulty in adapting to complex scenes. Therefore, we propose a method based on convolutional neural networks (CNNs). The proposed method improves performance of crowd density estimation in two key ways. First, we propose a feature pooling method named mixed pooling to regularize the CNNs. It replaces deterministic pooling operations with a parameter that, by studying the algorithm, could combine the conventional max pooling with average pooling methods. Second, we present a classification strategy, in which an image is divided into two cells and respectively categorized. The proposed approach was evaluated on three datasets: two ground truth image sequences and the University of California, San Diego, anomaly detection dataset. The results demonstrate that the proposed approach performs more effectively and easily than other methods.

3D-Subspace-Based Auto-Paired Azimuth Angle, Elevation Angle, and Range Estimation for 24G FMCW Radar with an L-Shaped Array

PubMed Central

Nam, HyungSoo; Choi, ByungGil; Oh, Daegun

2018-01-01

In this paper, a three-dimensional (3D)-subspace-based azimuth angle, elevation angle, and range estimation method with auto-pairing is proposed for frequency-modulated continuous waveform (FMCW) radar with an L-shaped array. The proposed method is designed to exploit the 3D shift-invariant structure of the stacked Hankel snapshot matrix for auto-paired azimuth angle, elevation angle, and range estimation. The effectiveness of the proposed method is verified through a variety of experiments conducted in a chamber. For the realization of the proposed method, K-band FMCW radar is implemented with an L-shaped antenna. PMID:29621193

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.

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.

Accurate estimation of human body orientation from RGB-D sensors.

PubMed

Liu, Wu; Zhang, Yongdong; Tang, Sheng; Tang, Jinhui; Hong, Richang; Li, Jintao

2013-10-01

Accurate estimation of human body orientation can significantly enhance the analysis of human behavior, which is a fundamental task in the field of computer vision. However, existing orientation estimation methods cannot handle the various body poses and appearances. In this paper, we propose an innovative RGB-D-based orientation estimation method to address these challenges. By utilizing the RGB-D information, which can be real time acquired by RGB-D sensors, our method is robust to cluttered environment, illumination change and partial occlusions. Specifically, efficient static and motion cue extraction methods are proposed based on the RGB-D superpixels to reduce the noise of depth data. Since it is hard to discriminate all the 360 (°) orientation using static cues or motion cues independently, we propose to utilize a dynamic Bayesian network system (DBNS) to effectively employ the complementary nature of both static and motion cues. In order to verify our proposed method, we build a RGB-D-based human body orientation dataset that covers a wide diversity of poses and appearances. Our intensive experimental evaluations on this dataset demonstrate the effectiveness and efficiency of the proposed method.

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.

Real-Time Parameter Estimation Method Applied to a MIMO Process and its Comparison with an Offline Identification Method

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

Kaplanoglu, Erkan; Safak, Koray K.; Varol, H. Selcuk

2009-01-12

An experiment based method is proposed for parameter estimation of a class of linear multivariable systems. The method was applied to a pressure-level control process. Experimental time domain input/output data was utilized in a gray-box modeling approach. Prior knowledge of the form of the system transfer function matrix elements is assumed to be known. Continuous-time system transfer function matrix parameters were estimated in real-time by the least-squares method. Simulation results of experimentally determined system transfer function matrix compare very well with the experimental results. For comparison and as an alternative to the proposed real-time estimation method, we also implemented anmore » offline identification method using artificial neural networks and obtained fairly good results. The proposed methods can be implemented conveniently on a desktop PC equipped with a data acquisition board for parameter estimation of moderately complex linear multivariable systems.« 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.

Optimal back-extrapolation method for estimating plasma volume in humans using the indocyanine green dilution method

PubMed Central

2014-01-01

Background The indocyanine green dilution method is one of the methods available to estimate plasma volume, although some researchers have questioned the accuracy of this method. Methods We developed a new, physiologically based mathematical model of indocyanine green kinetics that more accurately represents indocyanine green kinetics during the first few minutes postinjection than what is assumed when using the traditional mono-exponential back-extrapolation method. The mathematical model is used to develop an optimal back-extrapolation method for estimating plasma volume based on simulated indocyanine green kinetics obtained from the physiological model. Results Results from a clinical study using the indocyanine green dilution method in 36 subjects with type 2 diabetes indicate that the estimated plasma volumes are considerably lower when using the traditional back-extrapolation method than when using the proposed back-extrapolation method (mean (standard deviation) plasma volume = 26.8 (5.4) mL/kg for the traditional method vs 35.1 (7.0) mL/kg for the proposed method). The results obtained using the proposed method are more consistent with previously reported plasma volume values. Conclusions Based on the more physiological representation of indocyanine green kinetics and greater consistency with previously reported plasma volume values, the new back-extrapolation method is proposed for use when estimating plasma volume using the indocyanine green dilution method. PMID:25052018

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.

Sieve estimation of Cox models with latent structures.

PubMed

Cao, Yongxiu; Huang, Jian; Liu, Yanyan; Zhao, Xingqiu

2016-12-01

This article considers sieve estimation in the Cox model with an unknown regression structure based on right-censored data. We propose a semiparametric pursuit method to simultaneously identify and estimate linear and nonparametric covariate effects based on B-spline expansions through a penalized group selection method with concave penalties. We show that the estimators of the linear effects and the nonparametric component are consistent. Furthermore, we establish the asymptotic normality of the estimator of the linear effects. To compute the proposed estimators, we develop a modified blockwise majorization descent algorithm that is efficient and easy to implement. Simulation studies demonstrate that the proposed method performs well in finite sample situations. We also use the primary biliary cirrhosis data to illustrate its application. © 2016, The International Biometric Society.

A Simple Joint Estimation Method of Residual Frequency Offset and Sampling Frequency Offset for DVB Systems

NASA Astrophysics Data System (ADS)

Kwon, Ki-Won; Cho, Yongsoo

This letter presents a simple joint estimation method for residual frequency offset (RFO) and sampling frequency offset (STO) in OFDM-based digital video broadcasting (DVB) systems. The proposed method selects a continual pilot (CP) subset from an unsymmetrically and non-uniformly distributed CP set to obtain an unbiased estimator. Simulation results show that the proposed method using a properly selected CP subset is unbiased and performs robustly.

[A proposal for a new definition of excess mortality associated with influenza-epidemics and its estimation].

PubMed

Takahashi, M; Tango, T

2001-05-01

As methods for estimating excess mortality associated with influenza-epidemic, the Serfling's cyclical regression model and the Kawai and Fukutomi model with seasonal indices have been proposed. Excess mortality under the old definition (i.e., the number of deaths actually recorded in excess of the number expected on the basis of past seasonal experience) covers the random error for that portion of variation regarded as due to chance. In addition, it disregards the range of random variation of mortality with the season. In this paper, we propose a new definition of excess mortality associated with influenza-epidemics and a new estimation method, considering these questions with the Kawai and Fukutomi method. The new definition of excess mortality and a novel method for its estimation were generated as follows. Factors bringing about variation in mortality in months with influenza-epidemics may be divided into two groups: 1. Influenza itself, 2. others (practically random variation). The range of variation of mortality due to the latter (normal range) can be estimated from the range for months in the absence of influenza-epidemics. Excess mortality is defined as death over the normal range. A new definition of excess mortality associated with influenza-epidemics and an estimation method are proposed. The new method considers variation in mortality in months in the absence of influenza-epidemics. Consequently, it provides reasonable estimates of excess mortality by separating the portion of random variation. Further, it is a characteristic that the proposed estimate can be used as a criterion of statistical significance test.

Reinforcement learning state estimator.

PubMed

Morimoto, Jun; Doya, Kenji

2007-03-01

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

Adaptive noise canceling of electrocardiogram artifacts in single channel electroencephalogram.

PubMed

Cho, Sung Pil; Song, Mi Hye; Park, Young Cheol; Choi, Ho Seon; Lee, Kyoung Joung

2007-01-01

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images.

PubMed

Luo, Gongning; Dong, Suyu; Wang, Kuanquan; Zuo, Wangmeng; Cao, Shaodong; Zhang, Henggui

2017-10-13

Left ventricular (LV) volumes estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address direct LV volumes prediction task. In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN). We study the end-to-end LV volumes prediction method in items of the data preprocessing, networks structure, and multi-views fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new networks structure for end-to-end LV volumes estimation. Third, we explore the representational capacity of different slices, and propose a fusion strategy to improve the prediction accuracy. The evaluation results show that the proposed method outperforms other state-of-the-art LV volumes estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth (EDV: R=0.974, RMSE=9.6ml; ESV: R=0.976, RMSE=7.1ml; EF: R=0.828, RMSE =4.71%). Experimental results prove that the proposed method has high accuracy and efficiency on LV volumes prediction task. The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fields.

A weight modification sequential method for VSC-MTDC power system state estimation

NASA Astrophysics Data System (ADS)

Yang, Xiaonan; Zhang, Hao; Li, Qiang; Guo, Ziming; Zhao, Kun; Li, Xinpeng; Han, Feng

2017-06-01

This paper presents an effective sequential approach based on weight modification for VSC-MTDC power system state estimation, called weight modification sequential method. The proposed approach simplifies the AC/DC system state estimation algorithm through modifying the weight of state quantity to keep the matrix dimension constant. The weight modification sequential method can also make the VSC-MTDC system state estimation calculation results more ccurate and increase the speed of calculation. The effectiveness of the proposed weight modification sequential method is demonstrated and validated in modified IEEE 14 bus system.

Source signature estimation from multimode surface waves via mode-separated virtual real source method

NASA Astrophysics Data System (ADS)

Gao, Lingli; Pan, Yudi

2018-05-01

The correct estimation of the seismic source signature is crucial to exploration geophysics. Based on seismic interferometry, the virtual real source (VRS) method provides a model-independent way for source signature estimation. However, when encountering multimode surface waves, which are commonly seen in the shallow seismic survey, strong spurious events appear in seismic interferometric results. These spurious events introduce errors in the virtual-source recordings and reduce the accuracy of the source signature estimated by the VRS method. In order to estimate a correct source signature from multimode surface waves, we propose a mode-separated VRS method. In this method, multimode surface waves are mode separated before seismic interferometry. Virtual-source recordings are then obtained by applying seismic interferometry to each mode individually. Therefore, artefacts caused by cross-mode correlation are excluded in the virtual-source recordings and the estimated source signatures. A synthetic example showed that a correct source signature can be estimated with the proposed method, while strong spurious oscillation occurs in the estimated source signature if we do not apply mode separation first. We also applied the proposed method to a field example, which verified its validity and effectiveness in estimating seismic source signature from shallow seismic shot gathers containing multimode surface waves.

Design of Passive Power Filter for Hybrid Series Active Power Filter using Estimation, Detection and Classification Method

NASA Astrophysics Data System (ADS)

Swain, Sushree Diptimayee; Ray, Pravat Kumar; Mohanty, K. B.

2016-06-01

This research paper discover the design of a shunt Passive Power Filter (PPF) in Hybrid Series Active Power Filter (HSAPF) that employs a novel analytic methodology which is superior than FFT analysis. This novel approach consists of the estimation, detection and classification of the signals. The proposed method is applied to estimate, detect and classify the power quality (PQ) disturbance such as harmonics. This proposed work deals with three methods: the harmonic detection through wavelet transform method, the harmonic estimation by Kalman Filter algorithm and harmonic classification by decision tree method. From different type of mother wavelets in wavelet transform method, the db8 is selected as suitable mother wavelet because of its potency on transient response and crouched oscillation at frequency domain. In harmonic compensation process, the detected harmonic is compensated through Hybrid Series Active Power Filter (HSAPF) based on Instantaneous Reactive Power Theory (IRPT). The efficacy of the proposed method is verified in MATLAB/SIMULINK domain and as well as with an experimental set up. The obtained results confirm the superiority of the proposed methodology than FFT analysis. This newly proposed PPF is used to make the conventional HSAPF more robust and stable.

New methods of testing nonlinear hypothesis using iterative NLLS estimator

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.

2017-11-01

This research paper discusses the method of testing nonlinear hypothesis using iterative Nonlinear Least Squares (NLLS) estimator. Takeshi Amemiya [1] explained this method. However in the present research paper, a modified Wald test statistic due to Engle, Robert [6] is proposed to test the nonlinear hypothesis using iterative NLLS estimator. An alternative method for testing nonlinear hypothesis using iterative NLLS estimator based on nonlinear hypothesis using iterative NLLS estimator based on nonlinear studentized residuals has been proposed. In this research article an innovative method of testing nonlinear hypothesis using iterative restricted NLLS estimator is derived. Pesaran and Deaton [10] explained the methods of testing nonlinear hypothesis. This paper uses asymptotic properties of nonlinear least squares estimator proposed by Jenrich [8]. The main purpose of this paper is to provide very innovative methods of testing nonlinear hypothesis using iterative NLLS estimator, iterative NLLS estimator based on nonlinear studentized residuals and iterative restricted NLLS estimator. Eakambaram et al. [12] discussed least absolute deviation estimations versus nonlinear regression model with heteroscedastic errors and also they studied the problem of heteroscedasticity with reference to nonlinear regression models with suitable illustration. William Grene [13] examined the interaction effect in nonlinear models disused by Ai and Norton [14] and suggested ways to examine the effects that do not involve statistical testing. Peter [15] provided guidelines for identifying composite hypothesis and addressing the probability of false rejection for multiple hypotheses.

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.

Joint channel/frequency offset estimation and correction for coherent optical FBMC/OQAM system

NASA Astrophysics Data System (ADS)

Wang, Daobin; Yuan, Lihua; Lei, Jingli; wu, Gang; Li, Suoping; Ding, Runqi; Wang, Dongye

2017-12-01

In this paper, we focus on analysis of the preamble-based joint estimation for channel and laser-frequency offset (LFO) in coherent optical filter bank multicarrier systems with offset quadrature amplitude modulation (CO-FBMC/OQAM). In order to reduce the noise impact on the estimation accuracy, we proposed an estimation method based on inter-frame averaging. This method averages the cross-correlation function of real-valued pilots within multiple FBMC frames. The laser-frequency offset is estimated according to the phase of this average. After correcting LFO, the final channel response is also acquired by averaging channel estimation results within multiple frames. The principle of the proposed method is analyzed theoretically, and the preamble structure is thoroughly designed and optimized to suppress the impact of inherent imaginary interference (IMI). The effectiveness of our method is demonstrated numerically using different fiber and LFO values. The obtained results show that the proposed method can improve transmission performance significantly.

Estimating Clothing Thermal Insulation Using an Infrared Camera

PubMed Central

Lee, Jeong-Hoon; Kim, Young-Keun; Kim, Kyung-Soo; Kim, Soohyun

2016-01-01

In this paper, a novel algorithm for estimating clothing insulation is proposed to assess thermal comfort, based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera. The proposed method can accurately measure the clothing insulation of various garments under different clothing fit and sitting postures. The proposed estimation method is investigated to be effective to measure its clothing insulation significantly in different seasonal clothing conditions using a paired t-test in 99% confidence interval. Temperatures simulated with the proposed estimated insulation value show closer to the values of actual temperature than those with individual clothing insulation values. Upper clothing’s temperature is more accurate within 3% error and lower clothing’s temperature is more accurate by 3.7%~6.2% error in indoor working scenarios. The proposed algorithm can reflect the effect of air layer which makes insulation different in the calculation to estimate clothing insulation using the temperature of the face and clothing. In future, the proposed method is expected to be applied to evaluate the customized passenger comfort effectively. PMID:27005625

Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation.

PubMed

Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi

2015-07-01

Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.

Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MIMO Radar

PubMed Central

Wang, Xianpeng; Wang, Wei; Li, Xin; Liu, Qi; Liu, Jing

2016-01-01

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. PMID:27089345

Dynamic characteristics of oxygen consumption.

PubMed

Ye, Lin; Argha, Ahmadreza; Yu, Hairong; Celler, Branko G; Nguyen, Hung T; Su, Steven

2018-04-23

Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation. Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods. Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]). The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO 2 -Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

NASA Astrophysics Data System (ADS)

Yang, Duo; Zhang, Xu; Pan, Rui; Wang, Yujie; Chen, Zonghai

2018-04-01

The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In order to provide an accurate and reliable SOH estimation, a novel Gaussian process regression (GPR) model based on charging curve is proposed in this paper. Different from other researches where SOH is commonly estimated by cycle life, in this work four specific parameters extracted from charging curves are used as inputs of the GPR model instead of cycle numbers. These parameters can reflect the battery aging phenomenon from different angles. The grey relational analysis method is applied to analyze the relational grade between selected features and SOH. On the other hand, some adjustments are made in the proposed GPR model. Covariance function design and the similarity measurement of input variables are modified so as to improve the SOH estimate accuracy and adapt to the case of multidimensional input. Several aging data from NASA data repository are used for demonstrating the estimation effect by the proposed method. Results show that the proposed method has high SOH estimation accuracy. Besides, a battery with dynamic discharging profile is used to verify the robustness and reliability of this method.

Shrinkage regression-based methods for microarray missing value imputation.

PubMed

Wang, Hsiuying; Chiu, Chia-Chun; Wu, Yi-Ching; Wu, Wei-Sheng

2013-01-01

Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets. To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do. Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.

Estimation of group means when adjusting for covariates in generalized linear models.

PubMed

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Unified Least Squares Methods for the Evaluation of Diagnostic Tests With the Gold Standard

PubMed Central

Tang, Liansheng Larry; Yuan, Ao; Collins, John; Che, Xuan; Chan, Leighton

2017-01-01

The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input “data.” It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation. The key difference between the proposed method and the method of Tang and Zhou lies in the response variable. The response variable in the latter is transformed empirical ROC curves at different thresholds. It takes on many values for continuous test results, but few values for ordinal test results. The limited number of values for the response variable makes it impractical for ordinal data. However, the response variable in the proposed method takes on many more distinct values so that the method yields valid estimates for ordinal data. Extensive simulation studies are conducted to investigate and compare the finite sample performance of the proposed method with an existing method, and the method is then used to analyze 2 real cancer diagnostic example as an illustration. PMID:28469385

Bandwidth efficient channel estimation method for airborne hyperspectral data transmission in sparse doubly selective communication channels

NASA Astrophysics Data System (ADS)

Vahidi, Vahid; Saberinia, Ebrahim; Regentova, Emma E.

2017-10-01

A channel estimation (CE) method based on compressed sensing (CS) is proposed to estimate the sparse and doubly selective (DS) channel for hyperspectral image transmission from unmanned aircraft vehicles to ground stations. The proposed method contains three steps: (1) the priori estimate of the channel by orthogonal matching pursuit (OMP), (2) calculation of the linear minimum mean square error (LMMSE) estimate of the received pilots given the estimated channel, and (3) estimate of the complex amplitudes and Doppler shifts of the channel using the enhanced received pilot data applying a second round of a CS algorithm. The proposed method is named DS-LMMSE-OMP, and its performance is evaluated by simulating transmission of AVIRIS hyperspectral data via the communication channel and assessing their fidelity for the automated analysis after demodulation. The performance of the DS-LMMSE-OMP approach is compared with that of two other state-of-the-art CE methods. The simulation results exhibit up to 8-dB figure of merit in the bit error rate and 50% improvement in the hyperspectral image classification accuracy.

Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor

NASA Astrophysics Data System (ADS)

Chen, Shanjun; Duan, Haibin; Deng, Yimin; Li, Cong; Zhao, Guozhi; Xu, Yan

2017-12-01

Autonomous aerial refueling is a significant technology that can significantly extend the endurance of unmanned aerial vehicles. A reliable method that can accurately estimate the position and attitude of the probe relative to the drogue is the key to such a capability. A drogue pose estimation method based on infrared vision sensor is introduced with the general goal of yielding an accurate and reliable drogue state estimate. First, by employing direct least squares ellipse fitting and convex hull in OpenCV, a feature point matching and interference point elimination method is proposed. In addition, considering the conditions that some infrared LEDs are damaged or occluded, a missing point estimation method based on perspective transformation and affine transformation is designed. Finally, an accurate and robust pose estimation algorithm improved by the runner-root algorithm is proposed. The feasibility of the designed visual measurement system is demonstrated by flight test, and the results indicate that our proposed method enables precise and reliable pose estimation of the probe relative to the drogue, even in some poor conditions.

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.

Statistical Methods for Generalized Linear Models with Covariates Subject to Detection Limits.

PubMed

Bernhardt, Paul W; Wang, Huixia J; Zhang, Daowen

2015-05-01

Censored observations are a common occurrence in biomedical data sets. Although a large amount of research has been devoted to estimation and inference for data with censored responses, very little research has focused on proper statistical procedures when predictors are censored. In this paper, we consider statistical methods for dealing with multiple predictors subject to detection limits within the context of generalized linear models. We investigate and adapt several conventional methods and develop a new multiple imputation approach for analyzing data sets with predictors censored due to detection limits. We establish the consistency and asymptotic normality of the proposed multiple imputation estimator and suggest a computationally simple and consistent variance estimator. We also demonstrate that the conditional mean imputation method often leads to inconsistent estimates in generalized linear models, while several other methods are either computationally intensive or lead to parameter estimates that are biased or more variable compared to the proposed multiple imputation estimator. In an extensive simulation study, we assess the bias and variability of different approaches within the context of a logistic regression model and compare variance estimation methods for the proposed multiple imputation estimator. Lastly, we apply several methods to analyze the data set from a recently-conducted GenIMS study.

Covariance Matrix Estimation for Massive MIMO

NASA Astrophysics Data System (ADS)

Upadhya, Karthik; Vorobyov, Sergiy A.

2018-04-01

We propose a novel pilot structure for covariance matrix estimation in massive multiple-input multiple-output (MIMO) systems in which each user transmits two pilot sequences, with the second pilot sequence multiplied by a random phase-shift. The covariance matrix of a particular user is obtained by computing the sample cross-correlation of the channel estimates obtained from the two pilot sequences. This approach relaxes the requirement that all the users transmit their uplink pilots over the same set of symbols. We derive expressions for the achievable rate and the mean-squared error of the covariance matrix estimate when the proposed method is used with staggered pilots. The performance of the proposed method is compared with existing methods through simulations.

An efficient algorithm for measurement of retinal vessel diameter from fundus images based on directional filtering

NASA Astrophysics Data System (ADS)

Wang, Xuchu; Niu, Yanmin

2011-02-01

Automatic measurement of vessels from fundus images is a crucial step for assessing vessel anomalies in ophthalmological community, where the change in retinal vessel diameters is believed to be indicative of the risk level of diabetic retinopathy. In this paper, a new retinal vessel diameter measurement method by combining vessel orientation estimation and filter response is proposed. Its interesting characteristics include: (1) different from the methods that only fit the vessel profiles, the proposed method extracts more stable and accurate vessel diameter by casting this problem as a maximal response problem of a variation of Gabor filter; (2) the proposed method can directly and efficiently estimate the vessel's orientation, which is usually captured by time-consuming multi-orientation fitting techniques in many existing methods. Experimental results shows that the proposed method both retains the computational simplicity and achieves stable and accurate estimation results.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

PubMed Central

Miao, Zhiyong; Shen, Feng; Xu, Dingjie; He, Kunpeng; Tian, Chunmiao

2015-01-01

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor. PMID:25625903

An individual and dynamic Body Segment Inertial Parameter validation method using ground reaction forces.

PubMed

Hansen, Clint; Venture, Gentiane; Rezzoug, Nasser; Gorce, Philippe; Isableu, Brice

2014-05-07

Over the last decades a variety of research has been conducted with the goal to improve the Body Segment Inertial Parameters (BSIP) estimations but to our knowledge a real validation has never been completely successful, because no ground truth is available. The aim of this paper is to propose a validation method for a BSIP identification method (IM) and to confirm the results by comparing them with recalculated contact forces using inverse dynamics to those obtained by a force plate. Furthermore, the results are compared with the recently proposed estimation method by Dumas et al. (2007). Additionally, the results are cross validated with a high velocity overarm throwing movement. Throughout conditions higher correlations, smaller metrics and smaller RMSE can be found for the proposed BSIP estimation (IM) which shows its advantage compared to recently proposed methods as of Dumas et al. (2007). The purpose of the paper is to validate an already proposed method and to show that this method can be of significant advantage compared to conventional methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Innovative methods for calculation of freeway travel time using limited data : final report.

DOT National Transportation Integrated Search

2008-01-01

Description: Travel time estimations created by processing of simulated freeway loop detector data using proposed method have been compared with travel times reported from VISSIM model. An improved methodology was proposed to estimate freeway corrido...

Slip Ratio Estimation and Regenerative Brake Control for Decelerating Electric Vehicles without Detection of Vehicle Velocity and Acceleration

NASA Astrophysics Data System (ADS)

Suzuki, Toru; Fujimoto, Hiroshi

In slip ratio control systems, it is necessary to detect the vehicle velocity in order to obtain the slip ratio. However, it is very difficult to measure this velocity directly. We have proposed slip ratio estimation and control methods that do not require the vehicle velocity with acceleration. In this paper, the slip ratio estimation and control methods are proposed without detecting the vehicle velocity and acceleration when it is decelerating. We carried out simulations and experiments by using an electric vehicle to verify the effectiveness of the proposed method.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

PubMed

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression

PubMed Central

Ding, A. Adam; Wu, Hulin

2015-01-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method. PMID:26401093

Interquantile Shrinkage in Regression Models

PubMed Central

Jiang, Liewen; Wang, Huixia Judy; Bondell, Howard D.

2012-01-01

Conventional analysis using quantile regression typically focuses on fitting the regression model at different quantiles separately. However, in situations where the quantile coefficients share some common feature, joint modeling of multiple quantiles to accommodate the commonality often leads to more efficient estimation. One example of common features is that a predictor may have a constant effect over one region of quantile levels but varying effects in other regions. To automatically perform estimation and detection of the interquantile commonality, we develop two penalization methods. When the quantile slope coefficients indeed do not change across quantile levels, the proposed methods will shrink the slopes towards constant and thus improve the estimation efficiency. We establish the oracle properties of the two proposed penalization methods. Through numerical investigations, we demonstrate that the proposed methods lead to estimations with competitive or higher efficiency than the standard quantile regression estimation in finite samples. Supplemental materials for the article are available online. PMID:24363546

Estimating integrated variance in the presence of microstructure noise using linear regression

NASA Astrophysics Data System (ADS)

Holý, Vladimír

2017-07-01

Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

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.

Q-Method Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.

2012-01-01

A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.

Feature Grouping and Selection Over an Undirected Graph.

PubMed

Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping

2012-01-01

High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.

Quantifying rainfall-derived inflow and infiltration in sanitary sewer systems based on conductivity monitoring

NASA Astrophysics Data System (ADS)

Zhang, Mingkai; Liu, Yanchen; Cheng, Xun; Zhu, David Z.; Shi, Hanchang; Yuan, Zhiguo

2018-03-01

Quantifying rainfall-derived inflow and infiltration (RDII) in a sanitary sewer is difficult when RDII and overflow occur simultaneously. This study proposes a novel conductivity-based method for estimating RDII. The method separately decomposes rainfall-derived inflow (RDI) and rainfall-induced infiltration (RII) on the basis of conductivity data. Fast Fourier transform was adopted to analyze variations in the flow and water quality during dry weather. Nonlinear curve fitting based on the least squares algorithm was used to optimize parameters in the proposed RDII model. The method was successfully applied to real-life case studies, in which inflow and infiltration were successfully estimated for three typical rainfall events with total rainfall volumes of 6.25 mm (light), 28.15 mm (medium), and 178 mm (heavy). Uncertainties of model parameters were estimated using the generalized likelihood uncertainty estimation (GLUE) method and were found to be acceptable. Compared with traditional flow-based methods, the proposed approach exhibits distinct advantages in estimating RDII and overflow, particularly when the two processes happen simultaneously.

Paule‐Mandel estimators for network meta‐analysis with random inconsistency effects

PubMed Central

Veroniki, Areti Angeliki; Law, Martin; Tricco, Andrea C.; Baker, Rose

2017-01-01

Network meta‐analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta‐analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between‐study heterogeneity. Models for network meta‐analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta‐analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta‐analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood‐based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods. PMID:28585257

Alternating steady state free precession for estimation of current-induced magnetic flux density: A feasibility study.

PubMed

Lee, Hyunyeol; Jeong, Woo Chul; Kim, Hyung Joong; Woo, Eung Je; Park, Jaeseok

2016-05-01

To develop a novel, current-controlled alternating steady-state free precession (SSFP)-based conductivity imaging method and corresponding MR signal models to estimate current-induced magnetic flux density (Bz ) and conductivity distribution. In the proposed method, an SSFP pulse sequence, which is in sync with alternating current pulses, produces dual oscillating steady states while yielding nonlinear relation between signal phase and Bz . A ratiometric signal model between the states was analytically derived using the Bloch equation, wherein Bz was estimated by solving a nonlinear inverse problem for conductivity estimation. A theoretical analysis on the signal-to-noise ratio of Bz was given. Numerical and experimental studies were performed using SSFP-FID and SSFP-ECHO with current pulses positioned either before or after signal encoding to investigate the feasibility of the proposed method in conductivity estimation. Given all SSFP variants herein, SSFP-FID with alternating current pulses applied before signal encoding exhibits the highest Bz signal-to-noise ratio and conductivity contrast. Additionally, compared with conventional conductivity imaging, the proposed method benefits from rapid SSFP acquisition without apparent loss of conductivity contrast. We successfully demonstrated the feasibility of the proposed method in estimating current-induced Bz and conductivity distribution. It can be a promising, rapid imaging strategy for quantitative conductivity imaging. © 2015 Wiley Periodicals, Inc.

An improved method for bivariate meta-analysis when within-study correlations are unknown.

PubMed

Hong, Chuan; D Riley, Richard; Chen, Yong

2018-03-01

Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m≥50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses. Copyright © 2017 John Wiley & Sons, Ltd.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle.

PubMed

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Sun, Fenggang; Xiao, Yu

2017-02-26

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle

PubMed Central

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Sun, Fenggang; Xiao, Yu

2017-01-01

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions. PMID:28245634

Sequential fitting-and-separating reflectance components for analytical bidirectional reflectance distribution function estimation.

PubMed

Lee, Yu; Yu, Chanki; Lee, Sang Wook

2018-01-10

We present a sequential fitting-and-separating algorithm for surface reflectance components that separates individual dominant reflectance components and simultaneously estimates the corresponding bidirectional reflectance distribution function (BRDF) parameters from the separated reflectance values. We tackle the estimation of a Lafortune BRDF model, which combines a nonLambertian diffuse reflection and multiple specular reflectance components with a different specular lobe. Our proposed method infers the appropriate number of BRDF lobes and their parameters by separating and estimating each of the reflectance components using an interval analysis-based branch-and-bound method in conjunction with iterative K-ordered scale estimation. The focus of this paper is the estimation of the Lafortune BRDF model. Nevertheless, our proposed method can be applied to other analytical BRDF models such as the Cook-Torrance and Ward models. Experiments were carried out to validate the proposed method using isotropic materials from the Mitsubishi Electric Research Laboratories-Massachusetts Institute of Technology (MERL-MIT) BRDF database, and the results show that our method is superior to a conventional minimization algorithm.

Predicting Costs of Eastern National Forest Wildernesses.

ERIC Educational Resources Information Center

Guldin, Richard W.

1981-01-01

A method for estimating the total direct social costs for proposed wilderness areas is presented. A cost framework is constructed and equations are developed for cost components. To illustrate the study's method, social costs are estimated for a proposed wilderness area in New England. (Author/JN)

A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots

NASA Astrophysics Data System (ADS)

Li, Yuankai; Ding, Liang; Zheng, Zhizhong; Yang, Qizhi; Zhao, Xingang; Liu, Guangjun

2018-05-01

For motion control of wheeled planetary rovers traversing on deformable terrain, real-time terrain parameter estimation is critical in modeling the wheel-terrain interaction and compensating the effect of wheel slipping. A multi-mode real-time estimation method is proposed in this paper to achieve accurate terrain parameter estimation. The proposed method is composed of an inner layer for real-time filtering and an outer layer for online update. In the inner layer, sinkage exponent and internal frictional angle, which have higher sensitivity than that of the other terrain parameters to wheel-terrain interaction forces, are estimated in real time by using an adaptive robust extended Kalman filter (AREKF), whereas the other parameters are fixed with nominal values. The inner layer result can help synthesize the current wheel-terrain contact forces with adequate precision, but has limited prediction capability for time-variable wheel slipping. To improve estimation accuracy of the result from the inner layer, an outer layer based on recursive Gauss-Newton (RGN) algorithm is introduced to refine the result of real-time filtering according to the innovation contained in the history data. With the two-layer structure, the proposed method can work in three fundamental estimation modes: EKF, REKF and RGN, making the method applicable for flat, rough and non-uniform terrains. Simulations have demonstrated the effectiveness of the proposed method under three terrain types, showing the advantages of introducing the two-layer structure.

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.

Estimation of Comfortable/Uncomfortable Feeling Based on EEG by Using NN and k-means Algorithm for Massage Chair

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

A present massage chair realizes the massage motion and force designed by a professional masseur. However, appropriate massage force to the user can not be provided by the massage chair in such a method. On the other hand, the professional masseur can realize an appropriate massage force to more than one patient, because, the masseur considers the physical condition of the patient. Our research proposed the intelligent massage system of applying masseur's procedure for the massage chair using estimated skin elasticity and DB to relate skin elasticity and massage force. However, proposed system has a problem that DB does not adjust to unknown user, because user's feeling by massage can not be estimated. Then, this paper proposed the estimation method of comfortable/uncomfortable feeling based on EEG using the neural network and k-means algorithm. The realizability of the proposed method is verified by the experimental works.

An Adaptive Orientation Estimation Method for Magnetic and Inertial Sensors in the Presence of Magnetic Disturbances

PubMed Central

Fan, Bingfei; Li, Qingguo; Wang, Chao; Liu, Tao

2017-01-01

Magnetic and inertial sensors have been widely used to estimate the orientation of human segments due to their low cost, compact size and light weight. However, the accuracy of the estimated orientation is easily affected by external factors, especially when the sensor is used in an environment with magnetic disturbances. In this paper, we propose an adaptive method to improve the accuracy of orientation estimations in the presence of magnetic disturbances. The method is based on existing gradient descent algorithms, and it is performed prior to sensor fusion algorithms. The proposed method includes stationary state detection and magnetic disturbance severity determination. The stationary state detection makes this method immune to magnetic disturbances in stationary state, while the magnetic disturbance severity determination helps to determine the credibility of magnetometer data under dynamic conditions, so as to mitigate the negative effect of the magnetic disturbances. The proposed method was validated through experiments performed on a customized three-axis instrumented gimbal with known orientations. The error of the proposed method and the original gradient descent algorithms were calculated and compared. Experimental results demonstrate that in stationary state, the proposed method is completely immune to magnetic disturbances, and in dynamic conditions, the error caused by magnetic disturbance is reduced by 51.2% compared with original MIMU gradient descent algorithm. PMID:28534858

Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.

PubMed

Wan, Xiang; Wang, Wenqian; Liu, Jiming; Tong, Tiejun

2014-12-19

In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.'s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations.

Improved atmospheric effect elimination method for the roughness estimation of painted surfaces.

PubMed

Zhang, Ying; Xuan, Jiabin; Zhao, Huijie; Song, Ping; Zhang, Yi; Xu, Wujian

2018-03-01

We propose a method for eliminating the atmospheric effect in polarimetric imaging remote sensing by using polarimetric imagers to simultaneously detect ground targets and skylight, which does not need calibrated targets. In addition, calculation efficiencies are improved by the skylight division method without losing estimation accuracy. Outdoor experiments are performed to obtain the polarimetric bidirectional reflectance distribution functions of painted surfaces and skylight under different weather conditions. Finally, the roughness of the painted surfaces is estimated. We find that the estimation accuracy with the proposed method is 6% on cloudy weather, while it is 30.72% without atmospheric effect elimination.

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals

PubMed Central

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method. PMID:27382610

Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals.

PubMed

Zhao, Ziyue; Liu, Congfeng

2014-01-01

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method.

Bio-Optics Based Sensation Imaging for Breast Tumor Detection Using Tissue Characterization

PubMed Central

Lee, Jong-Ha; Kim, Yoon Nyun; Park, Hee-Jun

2015-01-01

The tissue inclusion parameter estimation method is proposed to measure the stiffness as well as geometric parameters. The estimation is performed based on the tactile data obtained at the surface of the tissue using an optical tactile sensation imaging system (TSIS). A forward algorithm is designed to comprehensively predict the tactile data based on the mechanical properties of tissue inclusion using finite element modeling (FEM). This forward information is used to develop an inversion algorithm that will be used to extract the size, depth, and Young's modulus of a tissue inclusion from the tactile data. We utilize the artificial neural network (ANN) for the inversion algorithm. The proposed estimation method was validated by a realistic tissue phantom with stiff inclusions. The experimental results showed that the proposed estimation method can measure the size, depth, and Young's modulus of a tissue inclusion with 0.58%, 3.82%, and 2.51% relative errors, respectively. The obtained results prove that the proposed method has potential to become a useful screening and diagnostic method for breast cancer. PMID:25785306

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.

Statistical Bayesian method for reliability evaluation based on ADT data

NASA Astrophysics Data System (ADS)

Lu, Dawei; Wang, Lizhi; Sun, Yusheng; Wang, Xiaohong

2018-05-01

Accelerated degradation testing (ADT) is frequently conducted in the laboratory to predict the products’ reliability under normal operating conditions. Two kinds of methods, degradation path models and stochastic process models, are utilized to analyze degradation data and the latter one is the most popular method. However, some limitations like imprecise solution process and estimation result of degradation ratio still exist, which may affect the accuracy of the acceleration model and the extrapolation value. Moreover, the conducted solution of this problem, Bayesian method, lose key information when unifying the degradation data. In this paper, a new data processing and parameter inference method based on Bayesian method is proposed to handle degradation data and solve the problems above. First, Wiener process and acceleration model is chosen; Second, the initial values of degradation model and parameters of prior and posterior distribution under each level is calculated with updating and iteration of estimation values; Third, the lifetime and reliability values are estimated on the basis of the estimation parameters; Finally, a case study is provided to demonstrate the validity of the proposed method. The results illustrate that the proposed method is quite effective and accuracy in estimating the lifetime and reliability of a product.

Concerning an application of the method of least squares with a variable weight matrix

NASA Technical Reports Server (NTRS)

Sukhanov, A. A.

1979-01-01

An estimate of a state vector for a physical system when the weight matrix in the method of least squares is a function of this vector is considered. An iterative procedure is proposed for calculating the desired estimate. Conditions for the existence and uniqueness of the limit of this procedure are obtained, and a domain is found which contains the limit estimate. A second method for calculating the desired estimate which reduces to the solution of a system of algebraic equations is proposed. The question of applying Newton's method of tangents to solving the given system of algebraic equations is considered and conditions for the convergence of the modified Newton's method are obtained. Certain properties of the estimate obtained are presented together with an example.

Integral-equation based methods for parameter estimation in output pulses of radiation detectors: Application in nuclear medicine and spectroscopy

NASA Astrophysics Data System (ADS)

Mohammadian-Behbahani, Mohammad-Reza; Saramad, Shahyar

2018-04-01

Model based analysis methods are relatively new approaches for processing the output data of radiation detectors in nuclear medicine imaging and spectroscopy. A class of such methods requires fast algorithms for fitting pulse models to experimental data. In order to apply integral-equation based methods for processing the preamplifier output pulses, this article proposes a fast and simple method for estimating the parameters of the well-known bi-exponential pulse model by solving an integral equation. The proposed method needs samples from only three points of the recorded pulse as well as its first and second order integrals. After optimizing the sampling points, the estimation results were calculated and compared with two traditional integration-based methods. Different noise levels (signal-to-noise ratios from 10 to 3000) were simulated for testing the functionality of the proposed method, then it was applied to a set of experimental pulses. Finally, the effect of quantization noise was assessed by studying different sampling rates. Promising results by the proposed method endorse it for future real-time applications.

Estimating the number of people in crowded scenes

NASA Astrophysics Data System (ADS)

Kim, Minjin; Kim, Wonjun; Kim, Changick

2011-01-01

This paper presents a method to estimate the number of people in crowded scenes without using explicit object segmentation or tracking. The proposed method consists of three steps as follows: (1) extracting space-time interest points using eigenvalues of the local spatio-temporal gradient matrix, (2) generating crowd regions based on space-time interest points, and (3) estimating the crowd density based on the multiple regression. In experimental results, the efficiency and robustness of our proposed method are demonstrated by using PETS 2009 dataset.

Causal inference with measurement error in outcomes: Bias analysis and estimation methods.

PubMed

Shu, Di; Yi, Grace Y

2017-01-01

Inverse probability weighting estimation has been popularly used to consistently estimate the average treatment effect. Its validity, however, is challenged by the presence of error-prone variables. In this paper, we explore the inverse probability weighting estimation with mismeasured outcome variables. We study the impact of measurement error for both continuous and discrete outcome variables and reveal interesting consequences of the naive analysis which ignores measurement error. When a continuous outcome variable is mismeasured under an additive measurement error model, the naive analysis may still yield a consistent estimator; when the outcome is binary, we derive the asymptotic bias in a closed-form. Furthermore, we develop consistent estimation procedures for practical scenarios where either validation data or replicates are available. With validation data, we propose an efficient method for estimation of average treatment effect; the efficiency gain is substantial relative to usual methods of using validation data. To provide protection against model misspecification, we further propose a doubly robust estimator which is consistent even when either the treatment model or the outcome model is misspecified. Simulation studies are reported to assess the performance of the proposed methods. An application to a smoking cessation dataset is presented.

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 (

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.

Noise estimation for hyperspectral imagery using spectral unmixing and synthesis

NASA Astrophysics Data System (ADS)

Demirkesen, C.; Leloglu, Ugur M.

2014-10-01

Most hyperspectral image (HSI) processing algorithms assume a signal to noise ratio model in their formulation which makes them dependent on accurate noise estimation. Many techniques have been proposed to estimate the noise. A very comprehensive comparative study on the subject is done by Gao et al. [1]. In a nut-shell, most techniques are based on the idea of calculating standard deviation from assumed-to-be homogenous regions in the image. Some of these algorithms work on a regular grid parameterized with a window size w, while others make use of image segmentation in order to obtain homogenous regions. This study focuses not only to the statistics of the noise but to the estimation of the noise itself. A noise estimation technique motivated from a recent HSI de-noising approach [2] is proposed in this study. The denoising algorithm is based on estimation of the end-members and their fractional abundances using non-negative least squares method. The end-members are extracted using the well-known simplex volume optimization technique called NFINDR after manual selection of number of end-members and the image is reconstructed using the estimated endmembers and abundances. Actually, image de-noising and noise estimation are two sides of the same coin: Once we denoise an image, we can estimate the noise by calculating the difference of the de-noised image and the original noisy image. In this study, the noise is estimated as described above. To assess the accuracy of this method, the methodology in [1] is followed, i.e., synthetic images are created by mixing end-member spectra and noise. Since best performing method for noise estimation was spectral and spatial de-correlation (SSDC) originally proposed in [3], the proposed method is compared to SSDC. The results of the experiments conducted with synthetic HSIs suggest that the proposed noise estimation strategy outperforms the existing techniques in terms of mean and standard deviation of absolute error of the estimated noise. Finally, it is shown that the proposed technique demonstrated a robust behavior to the change of its single parameter, namely the number of end-members.

Projection-based estimation and nonuniformity correction of sensitivity profiles in phased-array surface coils.

PubMed

Yun, Sungdae; Kyriakos, Walid E; Chung, Jun-Young; Han, Yeji; Yoo, Seung-Schik; Park, Hyunwook

2007-03-01

To develop a novel approach for calculating the accurate sensitivity profiles of phased-array coils, resulting in correction of nonuniform intensity in parallel MRI. The proposed intensity-correction method estimates the accurate sensitivity profile of each channel of the phased-array coil. The sensitivity profile is estimated by fitting a nonlinear curve to every projection view through the imaged object. The nonlinear curve-fitting efficiently obtains the low-frequency sensitivity profile by eliminating the high-frequency image contents. Filtered back-projection (FBP) is then used to compute the estimates of the sensitivity profile of each channel. The method was applied to both phantom and brain images acquired from the phased-array coil. Intensity-corrected images from the proposed method had more uniform intensity than those obtained by the commonly used sum-of-squares (SOS) approach. With the use of the proposed correction method, the intensity variation was reduced to 6.1% from 13.1% of the SOS. When the proposed approach was applied to the computation of the sensitivity maps during sensitivity encoding (SENSE) reconstruction, it outperformed the SOS approach in terms of the reconstructed image uniformity. The proposed method is more effective at correcting the intensity nonuniformity of phased-array surface-coil images than the conventional SOS method. In addition, the method was shown to be resilient to noise and was successfully applied for image reconstruction in parallel imaging.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data.

PubMed

Liu, Kai; Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.

Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data

PubMed Central

Cui, Meng-Ying; Cao, Peng; Wang, Jiang-Bo

2016-01-01

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods. PMID:27362654

Optimal Combinations of Diagnostic Tests Based on AUC.

PubMed

Huang, Xin; Qin, Gengsheng; Fang, Yixin

2011-06-01

When several diagnostic tests are available, one can combine them to achieve better diagnostic accuracy. This article considers the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a nonparametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests. The proposed methods are examined through simulation studies and applications to three real examples. © 2010, The International Biometric Society.

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data.

PubMed

Onojima, Takayuki; Goto, Takahiro; Mizuhara, Hiroaki; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

PubMed

Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

2017-12-01

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm.

PubMed

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.

On Short-Time Estimation of Vocal Tract Length from Formant Frequencies

PubMed Central

Lammert, Adam C.; Narayanan, Shrikanth S.

2015-01-01

Vocal tract length is highly variable across speakers and determines many aspects of the acoustic speech signal, making it an essential parameter to consider for explaining behavioral variability. A method for accurate estimation of vocal tract length from formant frequencies would afford normalization of interspeaker variability and facilitate acoustic comparisons across speakers. A framework for considering estimation methods is developed from the basic principles of vocal tract acoustics, and an estimation method is proposed that follows naturally from this framework. The proposed method is evaluated using acoustic characteristics of simulated vocal tracts ranging from 14 to 19 cm in length, as well as real-time magnetic resonance imaging data with synchronous audio from five speakers whose vocal tracts range from 14.5 to 18.0 cm in length. Evaluations show improvements in accuracy over previously proposed methods, with 0.631 and 1.277 cm root mean square error on simulated and human speech data, respectively. Empirical results show that the effectiveness of the proposed method is based on emphasizing higher formant frequencies, which seem less affected by speech articulation. Theoretical predictions of formant sensitivity reinforce this empirical finding. Moreover, theoretical insights are explained regarding the reason for differences in formant sensitivity. PMID:26177102

Outcome-Dependent Sampling Design and Inference for Cox's Proportional Hazards Model.

PubMed

Yu, Jichang; Liu, Yanyan; Cai, Jianwen; Sandler, Dale P; Zhou, Haibo

2016-11-01

We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study.

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.

Channel Estimation and Pilot Design for Massive MIMO Systems with Block-Structured Compressive Sensing

NASA Astrophysics Data System (ADS)

Lv, ZhuoKai; Yang, Tiejun; Zhu, Chunhua

2018-03-01

Through utilizing the technology of compressive sensing (CS), the channel estimation methods can achieve the purpose of reducing pilots and improving spectrum efficiency. The channel estimation and pilot design scheme are explored during the correspondence under the help of block-structured CS in massive MIMO systems. The block coherence property of the aggregate system matrix can be minimized so that the pilot design scheme based on stochastic search is proposed. Moreover, the block sparsity adaptive matching pursuit (BSAMP) algorithm under the common sparsity model is proposed so that the channel estimation can be caught precisely. Simulation results are to be proved the proposed design algorithm with superimposed pilots design and the BSAMP algorithm can provide better channel estimation than existing methods.

Combining facial dynamics with appearance for age estimation.

PubMed

Dibeklioglu, Hamdi; Alnajar, Fares; Ali Salah, Albert; Gevers, Theo

2015-06-01

Estimating the age of a human from the captured images of his/her face is a challenging problem. In general, the existing approaches to this problem use appearance features only. In this paper, we show that in addition to appearance information, facial dynamics can be leveraged in age estimation. We propose a method to extract and use dynamic features for age estimation, using a person's smile. Our approach is tested on a large, gender-balanced database with 400 subjects, with an age range between 8 and 76. In addition, we introduce a new database on posed disgust expressions with 324 subjects in the same age range, and evaluate the reliability of the proposed approach when used with another expression. State-of-the-art appearance-based age estimation methods from the literature are implemented as baseline. We demonstrate that for each of these methods, the addition of the proposed dynamic features results in statistically significant improvement. We further propose a novel hierarchical age estimation architecture based on adaptive age grouping. We test our approach extensively, including an exploration of spontaneous versus posed smile dynamics, and gender-specific age estimation. We show that using spontaneity information reduces the mean absolute error by up to 21%, advancing the state of the art for facial age estimation.

Battery state-of-charge estimation using approximate least squares

NASA Astrophysics Data System (ADS)

Unterrieder, C.; Zhang, C.; Lunglmayr, M.; Priewasser, R.; Marsili, S.; Huemer, M.

2015-03-01

In recent years, much effort has been spent to extend the runtime of battery-powered electronic applications. In order to improve the utilization of the available cell capacity, high precision estimation approaches for battery-specific parameters are needed. In this work, an approximate least squares estimation scheme is proposed for the estimation of the battery state-of-charge (SoC). The SoC is determined based on the prediction of the battery's electromotive force. The proposed approach allows for an improved re-initialization of the Coulomb counting (CC) based SoC estimation method. Experimental results for an implementation of the estimation scheme on a fuel gauge system on chip are illustrated. Implementation details and design guidelines are presented. The performance of the presented concept is evaluated for realistic operating conditions (temperature effects, aging, standby current, etc.). For the considered test case of a GSM/UMTS load current pattern of a mobile phone, the proposed method is able to re-initialize the CC-method with a high accuracy, while state-of-the-art methods fail to perform a re-initialization.

Structural Equation Models in a Redundancy Analysis Framework With Covariates.

PubMed

Lovaglio, Pietro Giorgio; Vittadini, Giorgio

2014-01-01

A recent method to specify and fit structural equation modeling in the Redundancy Analysis framework based on so-called Extended Redundancy Analysis (ERA) has been proposed in the literature. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites, estimated as linear combinations of exogenous variables. However, in the presence of direct effects linking exogenous and endogenous variables, or concomitant indicators, the composite scores are estimated by ignoring the presence of the specified direct effects. To fit structural equation models, we propose a new specification and estimation method, called Generalized Redundancy Analysis (GRA), allowing us to specify and fit a variety of relationships among composites, endogenous variables, and external covariates. The proposed methodology extends the ERA method, using a more suitable specification and estimation algorithm, by allowing for covariates that affect endogenous indicators indirectly through the composites and/or directly. To illustrate the advantages of GRA over ERA we propose a simulation study of small samples. Moreover, we propose an application aimed at estimating the impact of formal human capital on the initial earnings of graduates of an Italian university, utilizing a structural model consistent with well-established economic theory.

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

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise.

PubMed

Wang, Junxiang; Wang, Xianpeng; Xu, Dingjie; Bi, Guoan

2018-03-09

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise

PubMed Central

Wang, Junxiang; Wang, Xianpeng; Xu, Dingjie; Bi, Guoan

2018-01-01

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method. PMID:29522499

Spectrum response estimation for deep-water floating platforms via retardation function representation

NASA Astrophysics Data System (ADS)

Liu, Fushun; Liu, Chengcheng; Chen, Jiefeng; Wang, Bin

2017-08-01

The key concept of spectrum response estimation with commercial software, such as the SESAM software tool, typically includes two main steps: finding a suitable loading spectrum and computing the response amplitude operators (RAOs) subjected to a frequency-specified wave component. In this paper, we propose a nontraditional spectrum response estimation method that uses a numerical representation of the retardation functions. Based on estimated added mass and damping matrices of the structure, we decompose and replace the convolution terms with a series of poles and corresponding residues in the Laplace domain. Then, we estimate the power density corresponding to each frequency component using the improved periodogram method. The advantage of this approach is that the frequency-dependent motion equations in the time domain can be transformed into the Laplace domain without requiring Laplace-domain expressions for the added mass and damping. To validate the proposed method, we use a numerical semi-submerged pontoon from the SESAM. The numerical results show that the responses of the proposed method match well with those obtained from the traditional method. Furthermore, the estimated spectrum also matches well, which indicates its potential application to deep-water floating structures.

Shear wave speed estimation by adaptive random sample consensus method.

PubMed

Lin, Haoming; Wang, Tianfu; Chen, Siping

2014-01-01

This paper describes a new method for shear wave velocity estimation that is capable of extruding outliers automatically without preset threshold. The proposed method is an adaptive random sample consensus (ARANDSAC) and the metric used here is finding the certain percentage of inliers according to the closest distance criterion. To evaluate the method, the simulation and phantom experiment results were compared using linear regression with all points (LRWAP) and radon sum transform (RS) method. The assessment reveals that the relative biases of mean estimation are 20.00%, 4.67% and 5.33% for LRWAP, ARANDSAC and RS respectively for simulation, 23.53%, 4.08% and 1.08% for phantom experiment. The results suggested that the proposed ARANDSAC algorithm is accurate in shear wave speed estimation.

Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application.

PubMed

Yu, Jihnhee; Yang, Luge; Vexler, Albert; Hutson, Alan D

2016-06-15

The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Cross-correlation analysis of pulse wave propagation in arteries: in vitro validation and in vivo feasibility.

PubMed

Nauleau, Pierre; Apostolakis, Iason; McGarry, Matthew; Konofagou, Elisa

2018-05-29

The stiffness of the arteries is known to be an indicator of the progression of various cardiovascular diseases. Clinically, the pulse wave velocity (PWV) is used as a surrogate for arterial stiffness. Pulse wave imaging (PWI) is a non-invasive, ultrasound-based imaging technique capable of mapping the motion of the vessel walls, allowing the local assessment of arterial properties. Conventionally, a distinctive feature of the displacement wave (e.g. the 50% upstroke) is tracked across the map to estimate the PWV. However, the presence of reflections, such as those generated at the carotid bifurcation, can bias the PWV estimation. In this paper, we propose a two-step cross-correlation based method to characterize arteries using the information available in the PWI spatio-temporal map. First, the area under the cross-correlation curve is proposed as an index for locating the regions of different properties. Second, a local peak of the cross-correlation function is tracked to obtain a less biased estimate of the PWV. Three series of experiments were conducted in phantoms to evaluate the capabilities of the proposed method compared with the conventional method. In the ideal case of a homogeneous phantom, the two methods performed similarly and correctly estimated the PWV. In the presence of reflections, the proposed method provided a more accurate estimate than conventional processing: e.g. for the soft phantom, biases of  -0.27 and -0.71 m · s -1 were observed. In a third series of experiments, the correlation-based method was able to locate two regions of different properties with an error smaller than 1 mm. It also provided more accurate PWV estimates than conventional processing (biases:  -0.12 versus -0.26 m · s -1 ). Finally, the in vivo feasibility of the proposed method was demonstrated in eleven healthy subjects. The results indicate that the correlation-based method might be less precise in vivo but more accurate than the conventional method.

Dose-volume histogram prediction using density estimation.

PubMed

Skarpman Munter, Johanna; Sjölund, Jens

2015-09-07

Knowledge of what dose-volume histograms can be expected for a previously unseen patient could increase consistency and quality in radiotherapy treatment planning. We propose a machine learning method that uses previous treatment plans to predict such dose-volume histograms. The key to the approach is the framing of dose-volume histograms in a probabilistic setting.The training consists of estimating, from the patients in the training set, the joint probability distribution of some predictive features and the dose. The joint distribution immediately provides an estimate of the conditional probability of the dose given the values of the predictive features. The prediction consists of estimating, from the new patient, the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimate of the dose-volume histogram.To illustrate how the proposed method relates to previously proposed methods, we use the signed distance to the target boundary as a single predictive feature. As a proof-of-concept, we predicted dose-volume histograms for the brainstems of 22 acoustic schwannoma patients treated with stereotactic radiosurgery, and for the lungs of 9 lung cancer patients treated with stereotactic body radiation therapy. Comparing with two previous attempts at dose-volume histogram prediction we find that, given the same input data, the predictions are similar.In summary, we propose a method for dose-volume histogram prediction that exploits the intrinsic probabilistic properties of dose-volume histograms. We argue that the proposed method makes up for some deficiencies in previously proposed methods, thereby potentially increasing ease of use, flexibility and ability to perform well with small amounts of training data.

Deep learning ensemble with asymptotic techniques for oscillometric blood pressure estimation.

PubMed

Lee, Soojeong; Chang, Joon-Hyuk

2017-11-01

This paper proposes a deep learning based ensemble regression estimator with asymptotic techniques, and offers a method that can decrease uncertainty for oscillometric blood pressure (BP) measurements using the bootstrap and Monte-Carlo approach. While the former is used to estimate SBP and DBP, the latter attempts to determine confidence intervals (CIs) for SBP and DBP based on oscillometric BP measurements. This work originally employs deep belief networks (DBN)-deep neural networks (DNN) to effectively estimate BPs based on oscillometric measurements. However, there are some inherent problems with these methods. First, it is not easy to determine the best DBN-DNN estimator, and worthy information might be omitted when selecting one DBN-DNN estimator and discarding the others. Additionally, our input feature vectors, obtained from only five measurements per subject, represent a very small sample size; this is a critical weakness when using the DBN-DNN technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To address these problems, an ensemble with an asymptotic approach (based on combining the bootstrap with the DBN-DNN technique) is utilized to generate the pseudo features needed to estimate the SBP and DBP. In the first stage, the bootstrap-aggregation technique is used to create ensemble parameters. Afterward, the AdaBoost approach is employed for the second-stage SBP and DBP estimation. We then use the bootstrap and Monte-Carlo techniques in order to determine the CIs based on the target BP estimated using the DBN-DNN ensemble regression estimator with the asymptotic technique in the third stage. The proposed method can mitigate the estimation uncertainty such as large the standard deviation of error (SDE) on comparing the proposed DBN-DNN ensemble regression estimator with the DBN-DNN single regression estimator, we identify that the SDEs of the SBP and DBP are reduced by 0.58 and 0.57  mmHg, respectively. These indicate that the proposed method actually enhances the performance by 9.18% and 10.88% compared with the DBN-DNN single estimator. The proposed methodology improves the accuracy of BP estimation and reduces the uncertainty for BP estimation. Copyright © 2017 Elsevier B.V. All rights reserved.

Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors

PubMed Central

Latt, Win Tun; Veluvolu, Kalyana Chakravarthy; Ang, Wei Tech

2011-01-01

Position sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based on analytical integration has previously been developed to obtain accurate position estimate of periodic or quasi-periodic motion from inertial sensors using prior knowledge of the motion but without using aided sensors. In this paper, a new method is proposed which employs linear filtering stage coupled with adaptive filtering stage to remove drift and attenuation. The prior knowledge of the motion the proposed method requires is only approximate band of frequencies of the motion. Existing adaptive filtering methods based on Fourier series such as weighted-frequency Fourier linear combiner (WFLC), and band-limited multiple Fourier linear combiner (BMFLC) are modified to combine with the proposed method. To validate and compare the performance of the proposed method with the method based on analytical integration, simulation study is performed using periodic signals as well as real physiological tremor data, and real-time experiments are conducted using an ADXL-203 accelerometer. Results demonstrate that the performance of the proposed method outperforms the existing analytical integration method. PMID:22163935

Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest-Posttest Study.

PubMed

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A

2008-09-01

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175).

Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

PubMed Central

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A.

2013-01-01

The pretest–posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest–posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175). PMID:23729942

Robust estimation of simulated urinary volume from camera images under bathroom illumination.

PubMed

Honda, Chizuru; Bhuiyan, Md Shoaib; Kawanaka, Haruki; Watanabe, Eiichi; Oguri, Koji

2016-08-01

General uroflowmetry method involves the risk of nosocomial infections or time and effort of the recording. Medical institutions, therefore, need to measure voided volume simply and hygienically. Multiple cylindrical model that can estimate the fluid flow rate from the photographed image using camera has been proposed in an earlier study. This study implemented a flow rate estimation by using a general-purpose camera system (Raspberry Pi Camera Module) and the multiple cylindrical model. However, large amounts of noise in extracting liquid region are generated by the variation of the illumination when performing measurements in the bathroom. So the estimation error gets very large. In other words, the specifications of the previous study's camera setup regarding the shutter type and the frame rate was too strict. In this study, we relax the specifications to achieve a flow rate estimation using a general-purpose camera. In order to determine the appropriate approximate curve, we propose a binarizing method using background subtraction at each scanning row and a curve approximation method using RANSAC. Finally, by evaluating the estimation accuracy of our experiment and by comparing it with the earlier study's results, we show the effectiveness of our proposed method for flow rate estimation.

Estimating relative risks for common outcome using PROC NLP.

PubMed

Yu, Binbing; Wang, Zhuoqiao

2008-05-01

In cross-sectional or cohort studies with binary outcomes, it is biologically interpretable and of interest to estimate the relative risk or prevalence ratio, especially when the response rates are not rare. Several methods have been used to estimate the relative risk, among which the log-binomial models yield the maximum likelihood estimate (MLE) of the parameters. Because of restrictions on the parameter space, the log-binomial models often run into convergence problems. Some remedies, e.g., the Poisson and Cox regressions, have been proposed. However, these methods may give out-of-bound predicted response probabilities. In this paper, a new computation method using the SAS Nonlinear Programming (NLP) procedure is proposed to find the MLEs. The proposed NLP method was compared to the COPY method, a modified method to fit the log-binomial model. Issues in the implementation are discussed. For illustration, both methods were applied to data on the prevalence of microalbuminuria (micro-protein leakage into urine) for kidney disease patients from the Diabetes Control and Complications Trial. The sample SAS macro for calculating relative risk is provided in the appendix.

A MAP-based image interpolation method via Viterbi decoding of Markov chains of interpolation functions.

PubMed

Vedadi, Farhang; Shirani, Shahram

2014-01-01

A new method of image resolution up-conversion (image interpolation) based on maximum a posteriori sequence estimation is proposed. Instead of making a hard decision about the value of each missing pixel, we estimate the missing pixels in groups. At each missing pixel of the high resolution (HR) image, we consider an ensemble of candidate interpolation methods (interpolation functions). The interpolation functions are interpreted as states of a Markov model. In other words, the proposed method undergoes state transitions from one missing pixel position to the next. Accordingly, the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this to-be-estimated sequence of interpolation functions. Then, we solve the estimation problem using a trellis representation and the Viterbi algorithm. Using directional interpolation functions and sequence estimation techniques, we classify the new algorithm as an adaptive directional interpolation using soft-decision estimation techniques. Experimental results show that the proposed algorithm yields images with higher or comparable peak signal-to-noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations.

Semiblind channel estimation for MIMO-OFDM systems

NASA Astrophysics Data System (ADS)

Chen, Yi-Sheng; Song, Jyu-Han

2012-12-01

This article proposes a semiblind channel estimation method for multiple-input multiple-output orthogonal frequency-division multiplexing systems based on circular precoding. Relying on the precoding scheme at the transmitters, the autocorrelation matrix of the received data induces a structure relating the outer product of the channel frequency response matrix and precoding coefficients. This structure makes it possible to extract information about channel product matrices, which can be used to form a Hermitian matrix whose positive eigenvalues and corresponding eigenvectors yield the channel impulse response matrix. This article also tests the resistance of the precoding design to finite-sample estimation errors, and explores the effects of the precoding scheme on channel equalization by performing pairwise error probability analysis. The proposed method is immune to channel zero locations, and is reasonably robust to channel order overestimation. The proposed method is applicable to the scenarios in which the number of transmitters exceeds that of the receivers. Simulation results demonstrate the performance of the proposed method and compare it with some existing methods.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

PubMed

Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

2012-01-01

Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

Estimating Number of People Using Calibrated Monocular Camera Based on Geometrical Analysis of Surface Area

NASA Astrophysics Data System (ADS)

Arai, Hiroyuki; Miyagawa, Isao; Koike, Hideki; Haseyama, Miki

We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image directly indicates the number of people. Because foreground detection is possible even in crowded situations, the proposed method can be applied in such situations. Moreover, it can estimate the number of people in an a priori manner, so it needs no ground-truth data unlike existing feature-based estimation techniques. Experiments show the validity of the proposed method.

Methods to Estimate the Variance of Some Indices of the Signal Detection Theory: A Simulation Study

ERIC Educational Resources Information Center

Suero, Manuel; Privado, Jesús; Botella, Juan

2017-01-01

A simulation study is presented to evaluate and compare three methods to estimate the variance of the estimates of the parameters d and "C" of the signal detection theory (SDT). Several methods have been proposed to calculate the variance of their estimators, "d'" and "c." Those methods have been mostly assessed by…

Profile local linear estimation of generalized semiparametric regression model for longitudinal data.

PubMed

Sun, Yanqing; Sun, Liuquan; Zhou, Jie

2013-07-01

This paper studies the generalized semiparametric regression model for longitudinal data where the covariate effects are constant for some and time-varying for others. Different link functions can be used to allow more flexible modelling of longitudinal data. The nonparametric components of the model are estimated using a local linear estimating equation and the parametric components are estimated through a profile estimating function. The method automatically adjusts for heterogeneity of sampling times, allowing the sampling strategy to depend on the past sampling history as well as possibly time-dependent covariates without specifically model such dependence. A [Formula: see text]-fold cross-validation bandwidth selection is proposed as a working tool for locating an appropriate bandwidth. A criteria for selecting the link function is proposed to provide better fit of the data. Large sample properties of the proposed estimators are investigated. Large sample pointwise and simultaneous confidence intervals for the regression coefficients are constructed. Formal hypothesis testing procedures are proposed to check for the covariate effects and whether the effects are time-varying. A simulation study is conducted to examine the finite sample performances of the proposed estimation and hypothesis testing procedures. The methods are illustrated with a data example.

Wind power error estimation in resource assessments.

PubMed

Rodríguez, Osvaldo; Del Río, Jesús A; Jaramillo, Oscar A; Martínez, Manuel

2015-01-01

Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies.

Wind Power Error Estimation in Resource Assessments

PubMed Central

Rodríguez, Osvaldo; del Río, Jesús A.; Jaramillo, Oscar A.; Martínez, Manuel

2015-01-01

Estimating the power output is one of the elements that determine the techno-economic feasibility of a renewable project. At present, there is a need to develop reliable methods that achieve this goal, thereby contributing to wind power penetration. In this study, we propose a method for wind power error estimation based on the wind speed measurement error, probability density function, and wind turbine power curves. This method uses the actual wind speed data without prior statistical treatment based on 28 wind turbine power curves, which were fitted by Lagrange's method, to calculate the estimate wind power output and the corresponding error propagation. We found that wind speed percentage errors of 10% were propagated into the power output estimates, thereby yielding an error of 5%. The proposed error propagation complements the traditional power resource assessments. The wind power estimation error also allows us to estimate intervals for the power production leveled cost or the investment time return. The implementation of this method increases the reliability of techno-economic resource assessment studies. PMID:26000444

Characterization of background concentrations of contaminants using a mixture of normal distributions.

PubMed

Qian, Song S; Lyons, Regan E

2006-10-01

We present a Bayesian approach for characterizing background contaminant concentration distributions using data from sites that may have been contaminated. Our method, focused on estimation, resolves several technical problems of the existing methods sanctioned by the U.S. Environmental Protection Agency (USEPA) (a hypothesis testing based method), resulting in a simple and quick procedure for estimating background contaminant concentrations. The proposed Bayesian method is applied to two data sets from a federal facility regulated under the Resource Conservation and Restoration Act. The results are compared to background distributions identified using existing methods recommended by the USEPA. The two data sets represent low and moderate levels of censorship in the data. Although an unbiased estimator is elusive, we show that the proposed Bayesian estimation method will have a smaller bias than the EPA recommended method.

Outcome-Dependent Sampling Design and Inference for Cox’s Proportional Hazards Model

PubMed Central

Yu, Jichang; Liu, Yanyan; Cai, Jianwen; Sandler, Dale P.; Zhou, Haibo

2016-01-01

We propose a cost-effective outcome-dependent sampling design for the failure time data and develop an efficient inference procedure for data collected with this design. To account for the biased sampling scheme, we derive estimators from a weighted partial likelihood estimating equation. The proposed estimators for regression parameters are shown to be consistent and asymptotically normally distributed. A criteria that can be used to optimally implement the ODS design in practice is proposed and studied. The small sample performance of the proposed method is evaluated by simulation studies. The proposed design and inference procedure is shown to be statistically more powerful than existing alternative designs with the same sample sizes. We illustrate the proposed method with an existing real data from the Cancer Incidence and Mortality of Uranium Miners Study. PMID:28090134

Uncertainty-based Estimation of the Secure Range for ISO New England Dynamic Interchange Adjustment

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

Etingov, Pavel V.; Makarov, Yuri V.; Wu, Di

2014-04-14

The paper proposes an approach to estimate the secure range for dynamic interchange adjustment, which assists system operators in scheduling the interchange with neighboring control areas. Uncertainties associated with various sources are incorporated. The proposed method is implemented in the dynamic interchange adjustment (DINA) tool developed by Pacific Northwest National Laboratory (PNNL) for ISO New England. Simulation results are used to validate the effectiveness of the proposed method.

An adjoint-based simultaneous estimation method of the asthenosphere's viscosity and afterslip using a fast and scalable finite-element adjoint solver

NASA Astrophysics Data System (ADS)

Agata, Ryoichiro; Ichimura, Tsuyoshi; Hori, Takane; Hirahara, Kazuro; Hashimoto, Chihiro; Hori, Muneo

2018-04-01

The simultaneous estimation of the asthenosphere's viscosity and coseismic slip/afterslip is expected to improve largely the consistency of the estimation results to observation data of crustal deformation collected in widely spread observation points, compared to estimations of slips only. Such an estimate can be formulated as a non-linear inverse problem of material properties of viscosity and input force that is equivalent to fault slips based on large-scale finite-element (FE) modeling of crustal deformation, in which the degree of freedom is in the order of 109. We formulated and developed a computationally efficient adjoint-based estimation method for this inverse problem, together with a fast and scalable FE solver for the associated forward and adjoint problems. In a numerical experiment that imitates the 2011 Tohoku-Oki earthquake, the advantage of the proposed method is confirmed by comparing the estimated results with those obtained using simplified estimation methods. The computational cost required for the optimization shows that the proposed method enabled the targeted estimation to be completed with moderate amount of computational resources.

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.

Gray-world-assumption-based illuminant color estimation using color gamuts with high and low chroma

NASA Astrophysics Data System (ADS)

Kawamura, Harumi; Yonemura, Shunichi; Ohya, Jun; Kojima, Akira

2013-02-01

A new approach is proposed for estimating illuminant colors from color images under an unknown scene illuminant. The approach is based on a combination of a gray-world-assumption-based illuminant color estimation method and a method using color gamuts. The former method, which is one we had previously proposed, improved on the original method that hypothesizes that the average of all the object colors in a scene is achromatic. Since the original method estimates scene illuminant colors by calculating the average of all the image pixel values, its estimations are incorrect when certain image colors are dominant. Our previous method improves on it by choosing several colors on the basis of an opponent-color property, which is that the average color of opponent colors is achromatic, instead of using all colors. However, it cannot estimate illuminant colors when there are only a few image colors or when the image colors are unevenly distributed in local areas in the color space. The approach we propose in this paper combines our previous method and one using high chroma and low chroma gamuts, which makes it possible to find colors that satisfy the gray world assumption. High chroma gamuts are used for adding appropriate colors to the original image and low chroma gamuts are used for narrowing down illuminant color possibilities. Experimental results obtained using actual images show that even if the image colors are localized in a certain area in the color space, the illuminant colors are accurately estimated, with smaller estimation error average than that generated in the conventional method.

Model-based sphere localization (MBSL) in x-ray projections

NASA Astrophysics Data System (ADS)

Sawall, Stefan; Maier, Joscha; Leinweber, Carsten; Funck, Carsten; Kuntz, Jan; Kachelrieß, Marc

2017-08-01

The detection of spherical markers in x-ray projections is an important task in a variety of applications, e.g. geometric calibration and detector distortion correction. Therein, the projection of the sphere center on the detector is of particular interest as the used spherical beads are no ideal point-like objects. Only few methods have been proposed to estimate this respective position on the detector with sufficient accuracy and surrogate positions, e.g. the center of gravity, are used, impairing the results of subsequent algorithms. We propose to estimate the projection of the sphere center on the detector using a simulation-based method matching an artificial projection to the actual measurement. The proposed algorithm intrinsically corrects for all polychromatic effects included in the measurement and absent in the simulation by a polynomial which is estimated simultaneously. Furthermore, neither the acquisition geometry nor any object properties besides the fact that the object is of spherical shape need to be known to find the center of the bead. It is shown by simulations that the algorithm estimates the center projection with an error of less than 1% of the detector pixel size in case of realistic noise levels and that the method is robust to the sphere material, sphere size, and acquisition parameters. A comparison to three reference methods using simulations and measurements indicates that the proposed method is an order of magnitude more accurate compared to these algorithms. The proposed method is an accurate algorithm to estimate the center of spherical markers in CT projections in the presence of polychromatic effects and noise.

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.

A new radial strain and strain rate estimation method using autocorrelation for carotid artery

NASA Astrophysics Data System (ADS)

Ye, Jihui; Kim, Hoonmin; Park, Jongho; Yeo, Sunmi; Shim, Hwan; Lim, Hyungjoon; Yoo, Yangmo

2014-03-01

Atherosclerosis is a leading cause of cardiovascular disease. The early diagnosis of atherosclerosis is of clinical interest since it can prevent any adverse effects of atherosclerotic vascular diseases. In this paper, a new carotid artery radial strain estimation method based on autocorrelation is presented. In the proposed method, the strain is first estimated by the autocorrelation of two complex signals from the consecutive frames. Then, the angular phase from autocorrelation is converted to strain and strain rate and they are analyzed over time. In addition, a 2D strain image over region of interest in a carotid artery can be displayed. To evaluate the feasibility of the proposed radial strain estimation method, radiofrequency (RF) data of 408 frames in the carotid artery of a volunteer were acquired by a commercial ultrasound system equipped with a research package (V10, Samsung Medison, Korea) by using a L5-13IS linear array transducer. From in vivo carotid artery data, the mean strain estimate was -0.1372 while its minimum and maximum values were -2.961 and 0.909, respectively. Moreover, the overall strain estimates are highly correlated with the reconstructed M-mode trace. Similar results were obtained from the estimation of the strain rate change over time. These results indicate that the proposed carotid artery radial strain estimation method is useful for assessing the arterial wall's stiffness noninvasively without increasing the computational complexity.

Online machining error estimation method of numerical control gear grinding machine tool based on data analysis of internal sensors

NASA Astrophysics Data System (ADS)

Zhao, Fei; Zhang, Chi; Yang, Guilin; Chen, Chinyin

2016-12-01

This paper presents an online estimation method of cutting error by analyzing of internal sensor readings. The internal sensors of numerical control (NC) machine tool are selected to avoid installation problem. The estimation mathematic model of cutting error was proposed to compute the relative position of cutting point and tool center point (TCP) from internal sensor readings based on cutting theory of gear. In order to verify the effectiveness of the proposed model, it was simulated and experimented in gear generating grinding process. The cutting error of gear was estimated and the factors which induce cutting error were analyzed. The simulation and experiments verify that the proposed approach is an efficient way to estimate the cutting error of work-piece during machining process.

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves.

PubMed

Pérez-Patricio, Madaín; Camas-Anzueto, Jorge Luis; Sanchez-Alegría, Avisaí; Aguilar-González, Abiel; Gutiérrez-Miceli, Federico; Escobar-Gómez, Elías; Voisin, Yvon; Rios-Rojas, Carlos; Grajales-Coutiño, Ruben

2018-02-22

This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica , Canavalia ensiforme , and Lycopersicon esculentum . Experimental results showed that-in terms of accuracy and processing speed-the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica , where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Dougherty, Edward R

2003-11-22

Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule. We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error. The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).

Blind third-order dispersion estimation based on fractional Fourier transformation for coherent optical communication

NASA Astrophysics Data System (ADS)

Yang, Lin; Guo, Peng; Yang, Aiying; Qiao, Yaojun

2018-02-01

In this paper, we propose a blind third-order dispersion estimation method based on fractional Fourier transformation (FrFT) in optical fiber communication system. By measuring the chromatic dispersion (CD) at different wavelengths, this method can estimation dispersion slope and further calculate the third-order dispersion. The simulation results demonstrate that the estimation error is less than 2 % in 28GBaud dual polarization quadrature phase-shift keying (DP-QPSK) and 28GBaud dual polarization 16 quadrature amplitude modulation (DP-16QAM) system. Through simulations, the proposed third-order dispersion estimation method is shown to be robust against nonlinear and amplified spontaneous emission (ASE) noise. In addition, to reduce the computational complexity, searching step with coarse and fine granularity is chosen to search optimal order of FrFT. The third-order dispersion estimation method based on FrFT can be used to monitor the third-order dispersion in optical fiber system.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm

PubMed Central

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness. PMID:26339228

Generalized Redistribute-to-the-Right Algorithm: Application to the Analysis of Censored Cost Data

PubMed Central

CHEN, SHUAI; ZHAO, HONGWEI

2013-01-01

Medical cost estimation is a challenging task when censoring of data is present. Although researchers have proposed methods for estimating mean costs, these are often derived from theory and are not always easy to understand. We provide an alternative method, based on a replace-from-the-right algorithm, for estimating mean costs more efficiently. We show that our estimator is equivalent to an existing one that is based on the inverse probability weighting principle and semiparametric efficiency theory. We also propose an alternative method for estimating the survival function of costs, based on the redistribute-to-the-right algorithm, that was originally used for explaining the Kaplan–Meier estimator. We show that this second proposed estimator is equivalent to a simple weighted survival estimator of costs. Finally, we develop a more efficient survival estimator of costs, using the same redistribute-to-the-right principle. This estimator is naturally monotone, more efficient than some existing survival estimators, and has a quite small bias in many realistic settings. We conduct numerical studies to examine the finite sample property of the survival estimators for costs, and show that our new estimator has small mean squared errors when the sample size is not too large. We apply both existing and new estimators to a data example from a randomized cardiovascular clinical trial. PMID:24403869

New charging strategy for lithium-ion batteries based on the integration of Taguchi method and state of charge estimation

NASA Astrophysics Data System (ADS)

Vo, Thanh Tu; Chen, Xiaopeng; Shen, Weixiang; Kapoor, Ajay

2015-01-01

In this paper, a new charging strategy of lithium-polymer batteries (LiPBs) has been proposed based on the integration of Taguchi method (TM) and state of charge estimation. The TM is applied to search an optimal charging current pattern. An adaptive switching gain sliding mode observer (ASGSMO) is adopted to estimate the SOC which controls and terminates the charging process. The experimental results demonstrate that the proposed charging strategy can successfully charge the same types of LiPBs with different capacities and cycle life. The proposed charging strategy also provides much shorter charging time, narrower temperature variation and slightly higher energy efficiency than the equivalent constant current constant voltage charging method.

Vascular input function correction of inflow enhancement for improved pharmacokinetic modeling of liver DCE-MRI.

PubMed

Ning, Jia; Schubert, Tilman; Johnson, Kevin M; Roldán-Alzate, Alejandro; Chen, Huijun; Yuan, Chun; Reeder, Scott B

2018-06-01

To propose a simple method to correct vascular input function (VIF) due to inflow effects and to test whether the proposed method can provide more accurate VIFs for improved pharmacokinetic modeling. A spoiled gradient echo sequence-based inflow quantification and contrast agent concentration correction method was proposed. Simulations were conducted to illustrate improvement in the accuracy of VIF estimation and pharmacokinetic fitting. Animal studies with dynamic contrast-enhanced MR scans were conducted before, 1 week after, and 2 weeks after portal vein embolization (PVE) was performed in the left portal circulation of pigs. The proposed method was applied to correct the VIFs for model fitting. Pharmacokinetic parameters fitted using corrected and uncorrected VIFs were compared between different lobes and visits. Simulation results demonstrated that the proposed method can improve accuracy of VIF estimation and pharmacokinetic fitting. In animal study results, pharmacokinetic fitting using corrected VIFs demonstrated changes in perfusion consistent with changes expected after PVE, whereas the perfusion estimates derived by uncorrected VIFs showed no significant changes. The proposed correction method improves accuracy of VIFs and therefore provides more precise pharmacokinetic fitting. This method may be promising in improving the reliability of perfusion quantification. Magn Reson Med 79:3093-3102, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data

PubMed Central

Ran, Bin; Song, Li; Cheng, Yang; Tan, Huachun

2016-01-01

Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%. PMID:27448326

Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.

PubMed

Ran, Bin; Song, Li; Zhang, Jian; Cheng, Yang; Tan, Huachun

2016-01-01

Traffic state estimation from the floating car system is a challenging problem. The low penetration rate and random distribution make available floating car samples usually cover part space and time points of the road networks. To obtain a wide range of traffic state from the floating car system, many methods have been proposed to estimate the traffic state for the uncovered links. However, these methods cannot provide traffic state of the entire road networks. In this paper, the traffic state estimation is transformed to solve a missing data imputation problem, and the tensor completion framework is proposed to estimate missing traffic state. A tensor is constructed to model traffic state in which observed entries are directly derived from floating car system and unobserved traffic states are modeled as missing entries of constructed tensor. The constructed traffic state tensor can represent spatial and temporal correlations of traffic data and encode the multi-way properties of traffic state. The advantage of the proposed approach is that it can fully mine and utilize the multi-dimensional inherent correlations of traffic state. We tested the proposed approach on a well calibrated simulation network. Experimental results demonstrated that the proposed approach yield reliable traffic state estimation from very sparse floating car data, particularly when dealing with the floating car penetration rate is below 1%.

Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector.

PubMed

Fan, Yangyu; Wang, Jianshu; Du, Rui; Lv, Guoyun

2018-06-04

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K ≤ ( M 4 - 2 M 3 + 7 M 2 - 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method.

GPS/DR Error Estimation for Autonomous Vehicle Localization.

PubMed

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-08-21

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

GPS/DR Error Estimation for Autonomous Vehicle Localization

PubMed Central

Lee, Byung-Hyun; Song, Jong-Hwa; Im, Jun-Hyuck; Im, Sung-Hyuck; Heo, Moon-Beom; Jee, Gyu-In

2015-01-01

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level. PMID:26307997

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

PubMed Central

Lam, William H. K.; Li, Qingquan

2017-01-01

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. PMID:29210978

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

PubMed

Shi, Chaoyang; Chen, Bi Yu; Lam, William H K; Li, Qingquan

2017-12-06

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data

PubMed Central

Goto, Takahiro; Aoyagi, Toshio

2018-01-01

Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results. PMID:29337999

A Temperature-Based Bioimpedance Correction for Water Loss Estimation During Sports.

PubMed

Ring, Matthias; Lohmueller, Clemens; Rauh, Manfred; Mester, Joachim; Eskofier, Bjoern M

2016-11-01

The amount of total body water (TBW) can be estimated based on bioimpedance measurements of the human body. In sports, TBW estimations are of importance because mild water losses can impair muscular strength and aerobic endurance. Severe water losses can even be life threatening. TBW estimations based on bioimpedance, however, fail during sports because the increased body temperature corrupts bioimpedance measurements. Therefore, this paper proposes a machine learning method that eliminates the effects of increased temperature on bioimpedance and, consequently, reveals the changes in bioimpedance that are due to TBW loss. This is facilitated by utilizing changes in skin and core temperature. The method was evaluated in a study in which bioimpedance, temperature, and TBW loss were recorded every 15 min during a 2-h running workout. The evaluation demonstrated that the proposed method is able to reduce the error of TBW loss estimation by up to 71%, compared to the state of art. In the future, the proposed method in combination with portable bioimpedance devices might facilitate the development of wearable systems for continuous and noninvasive TBW loss monitoring during sports.

Time Series Decomposition into Oscillation Components and Phase Estimation.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Double inverse-weighted estimation of cumulative treatment effects under nonproportional hazards and dependent censoring.

PubMed

Schaubel, Douglas E; Wei, Guanghui

2011-03-01

In medical studies of time-to-event data, nonproportional hazards and dependent censoring are very common issues when estimating the treatment effect. A traditional method for dealing with time-dependent treatment effects is to model the time-dependence parametrically. Limitations of this approach include the difficulty to verify the correctness of the specified functional form and the fact that, in the presence of a treatment effect that varies over time, investigators are usually interested in the cumulative as opposed to instantaneous treatment effect. In many applications, censoring time is not independent of event time. Therefore, we propose methods for estimating the cumulative treatment effect in the presence of nonproportional hazards and dependent censoring. Three measures are proposed, including the ratio of cumulative hazards, relative risk, and difference in restricted mean lifetime. For each measure, we propose a double inverse-weighted estimator, constructed by first using inverse probability of treatment weighting (IPTW) to balance the treatment-specific covariate distributions, then using inverse probability of censoring weighting (IPCW) to overcome the dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal. We study their finite-sample properties through simulation. The proposed methods are used to compare kidney wait-list mortality by race. © 2010, The International Biometric Society.

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.

An Estimation Method of Waiting Time for Health Service at Hospital by Using a Portable RFID and Robust Estimation

NASA Astrophysics Data System (ADS)

Ishigaki, Tsukasa; Yamamoto, Yoshinobu; Nakamura, Yoshiyuki; Akamatsu, Motoyuki

Patients that have an health service by doctor have to wait long time at many hospitals. The long waiting time is the worst factor of patient's dissatisfaction for hospital service according to questionnaire for patients. The present paper describes an estimation method of the waiting time for each patient without an electronic medical chart system. The method applies a portable RFID system to data acquisition and robust estimation of probability distribution of the health service and test time by doctor for high-accurate waiting time estimation. We carried out an health service of data acquisition at a real hospital and verified the efficiency of the proposed method. The proposed system widely can be used as data acquisition system in various fields such as marketing service, entertainment or human behavior measurement.

Centroid estimation for a Shack-Hartmann wavefront sensor based on stream processing.

PubMed

Kong, Fanpeng; Polo, Manuel Cegarra; Lambert, Andrew

2017-08-10

Using center of gravity to estimate the centroid of the spot in a Shack-Hartmann wavefront sensor, the measurement corrupts with photon and detector noise. Parameters, like window size, often require careful optimization to balance the noise error, dynamic range, and linearity of the response coefficient under different photon flux. It also needs to be substituted by the correlation method for extended sources. We propose a centroid estimator based on stream processing, where the center of gravity calculation window floats with the incoming pixel from the detector. In comparison with conventional methods, we show that the proposed estimator simplifies the choice of optimized parameters, provides a unit linear coefficient response, and reduces the influence of background and noise. It is shown that the stream-based centroid estimator also works well for limited size extended sources. A hardware implementation of the proposed estimator is discussed.

Adaptive kernel function using line transect sampling

NASA Astrophysics Data System (ADS)

Albadareen, Baker; Ismail, Noriszura

2018-04-01

The estimation of f(0) is crucial in the line transect method which is used for estimating population abundance in wildlife survey's. The classical kernel estimator of f(0) has a high negative bias. Our study proposes an adaptation in the kernel function which is shown to be more efficient than the usual kernel estimator. A simulation study is adopted to compare the performance of the proposed estimators with the classical kernel estimators.

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

Efficient Strategies for Estimating the Spatial Coherence of Backscatter

PubMed Central

Hyun, Dongwoon; Crowley, Anna Lisa C.; Dahl, Jeremy J.

2017-01-01

The spatial coherence of ultrasound backscatter has been proposed to reduce clutter in medical imaging, to measure the anisotropy of the scattering source, and to improve the detection of blood flow. These techniques rely on correlation estimates that are obtained using computationally expensive strategies. In this study, we assess existing spatial coherence estimation methods and propose three computationally efficient modifications: a reduced kernel, a downsampled receive aperture, and the use of an ensemble correlation coefficient. The proposed methods are implemented in simulation and in vivo studies. Reducing the kernel to a single sample improved computational throughput and improved axial resolution. Downsampling the receive aperture was found to have negligible effect on estimator variance, and improved computational throughput by an order of magnitude for a downsample factor of 4. The ensemble correlation estimator demonstrated lower variance than the currently used average correlation. Combining the three methods, the throughput was improved 105-fold in simulation with a downsample factor of 4 and 20-fold in vivo with a downsample factor of 2. PMID:27913342

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.

Multiple imputation methods for nonparametric inference on cumulative incidence with missing cause of failure

PubMed Central

Lee, Minjung; Dignam, James J.; Han, Junhee

2014-01-01

We propose a nonparametric approach for cumulative incidence estimation when causes of failure are unknown or missing for some subjects. Under the missing at random assumption, we estimate the cumulative incidence function using multiple imputation methods. We develop asymptotic theory for the cumulative incidence estimators obtained from multiple imputation methods. We also discuss how to construct confidence intervals for the cumulative incidence function and perform a test for comparing the cumulative incidence functions in two samples with missing cause of failure. Through simulation studies, we show that the proposed methods perform well. The methods are illustrated with data from a randomized clinical trial in early stage breast cancer. PMID:25043107

A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter

NASA Astrophysics Data System (ADS)

Li, Yi; Abdel-Monem, Mohamed; Gopalakrishnan, Rahul; Berecibar, Maitane; Nanini-Maury, Elise; Omar, Noshin; van den Bossche, Peter; Van Mierlo, Joeri

2018-01-01

This paper proposes an advanced state of health (SoH) estimation method for high energy NMC lithium-ion batteries based on the incremental capacity (IC) analysis. IC curves are used due to their ability of detect and quantify battery degradation mechanism. A simple and robust smoothing method is proposed based on Gaussian filter to reduce the noise on IC curves, the signatures associated with battery ageing can therefore be accurately identified. A linear regression relationship is found between the battery capacity with the positions of features of interest (FOIs) on IC curves. Results show that the developed SoH estimation function from one single battery cell is able to evaluate the SoH of other batteries cycled under different cycling depth with less than 2.5% maximum errors, which proves the robustness of the proposed method on SoH estimation. With this technique, partial charging voltage curves can be used for SoH estimation and the testing time can be therefore largely reduced. This method shows great potential to be applied in reality, as it only requires static charging curves and can be easily implemented in battery management system (BMS).

Autonomous Landmark Calibration Method for Indoor Localization

PubMed Central

Kim, Jae-Hoon; Kim, Byoung-Seop

2017-01-01

Machine-generated data expansion is a global phenomenon in recent Internet services. The proliferation of mobile communication and smart devices has increased the utilization of machine-generated data significantly. One of the most promising applications of machine-generated data is the estimation of the location of smart devices. The motion sensors integrated into smart devices generate continuous data that can be used to estimate the location of pedestrians in an indoor environment. We focus on the estimation of the accurate location of smart devices by determining the landmarks appropriately for location error calibration. In the motion sensor-based location estimation, the proposed threshold control method determines valid landmarks in real time to avoid the accumulation of errors. A statistical method analyzes the acquired motion sensor data and proposes a valid landmark for every movement of the smart devices. Motion sensor data used in the testbed are collected from the actual measurements taken throughout a commercial building to demonstrate the practical usefulness of the proposed method. PMID:28837071

Iterative Refinement of Transmission Map for Stereo Image Defogging Using a Dual Camera Sensor.

PubMed

Kim, Heegwang; Park, Jinho; Park, Hasil; Paik, Joonki

2017-12-09

Recently, the stereo imaging-based image enhancement approach has attracted increasing attention in the field of video analysis. This paper presents a dual camera-based stereo image defogging algorithm. Optical flow is first estimated from the stereo foggy image pair, and the initial disparity map is generated from the estimated optical flow. Next, an initial transmission map is generated using the initial disparity map. Atmospheric light is then estimated using the color line theory. The defogged result is finally reconstructed using the estimated transmission map and atmospheric light. The proposed method can refine the transmission map iteratively. Experimental results show that the proposed method can successfully remove fog without color distortion. The proposed method can be used as a pre-processing step for an outdoor video analysis system and a high-end smartphone with a dual camera system.

Iterative Refinement of Transmission Map for Stereo Image Defogging Using a Dual Camera Sensor

PubMed Central

Park, Jinho; Park, Hasil

2017-01-01

Recently, the stereo imaging-based image enhancement approach has attracted increasing attention in the field of video analysis. This paper presents a dual camera-based stereo image defogging algorithm. Optical flow is first estimated from the stereo foggy image pair, and the initial disparity map is generated from the estimated optical flow. Next, an initial transmission map is generated using the initial disparity map. Atmospheric light is then estimated using the color line theory. The defogged result is finally reconstructed using the estimated transmission map and atmospheric light. The proposed method can refine the transmission map iteratively. Experimental results show that the proposed method can successfully remove fog without color distortion. The proposed method can be used as a pre-processing step for an outdoor video analysis system and a high-end smartphone with a dual camera system. PMID:29232826

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.

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

PubMed Central

Wang, Chen; Zhang, Xiaojuan; Qu, Xiaodong; Pan, Xiao; Fang, Guangyou; Chen, Luzhao

2016-01-01

The Scalar Triangulation and Ranging (STAR) method, which is based upon the unique properties of magnetic gradient contraction, is a high real-time ferromagnetic target localization method. Only one measurement point is required in the STAR method and it is not sensitive to changes in sensing platform orientation. However, the localization accuracy of the method is limited by the asphericity errors and the inaccurate value of position leads to larger errors in the estimation of magnetic moment. To improve the localization accuracy, a modified STAR method is proposed. In the proposed method, the asphericity errors of the traditional STAR method are compensated with an iterative algorithm. The proposed method has a fast convergence rate which meets the requirement of high real-time localization. Simulations and field experiments have been done to evaluate the performance of the proposed method. The results indicate that target parameters estimated by the modified STAR method are more accurate than the traditional STAR method. PMID:27999322

Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations

PubMed Central

Cao, Jiguo; Huang, Jianhua Z.; Wu, Hulin

2012-01-01

Ordinary differential equations (ODEs) are widely used in biomedical research and other scientific areas to model complex dynamic systems. It is an important statistical problem to estimate parameters in ODEs from noisy observations. In this article we propose a method for estimating the time-varying coefficients in an ODE. Our method is a variation of the nonlinear least squares where penalized splines are used to model the functional parameters and the ODE solutions are approximated also using splines. We resort to the implicit function theorem to deal with the nonlinear least squares objective function that is only defined implicitly. The proposed penalized nonlinear least squares method is applied to estimate a HIV dynamic model from a real dataset. Monte Carlo simulations show that the new method can provide much more accurate estimates of functional parameters than the existing two-step local polynomial method which relies on estimation of the derivatives of the state function. Supplemental materials for the article are available online. PMID:23155351

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.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter.

PubMed

Choi, Jihoon; Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-09-13

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

Flexible Modeling of Survival Data with Covariates Subject to Detection Limits via Multiple Imputation.

PubMed

Bernhardt, Paul W; Wang, Huixia Judy; Zhang, Daowen

2014-01-01

Models for survival data generally assume that covariates are fully observed. However, in medical studies it is not uncommon for biomarkers to be censored at known detection limits. A computationally-efficient multiple imputation procedure for modeling survival data with covariates subject to detection limits is proposed. This procedure is developed in the context of an accelerated failure time model with a flexible seminonparametric error distribution. The consistency and asymptotic normality of the multiple imputation estimator are established and a consistent variance estimator is provided. An iterative version of the proposed multiple imputation algorithm that approximates the EM algorithm for maximum likelihood is also suggested. Simulation studies demonstrate that the proposed multiple imputation methods work well while alternative methods lead to estimates that are either biased or more variable. The proposed methods are applied to analyze the dataset from a recently-conducted GenIMS study.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter

PubMed Central

Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-01-01

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected. PMID:28902154

Automatic draft reading based on image processing

NASA Astrophysics Data System (ADS)

Tsujii, Takahiro; Yoshida, Hiromi; Iiguni, Youji

2016-10-01

In marine transportation, a draft survey is a means to determine the quantity of bulk cargo. Automatic draft reading based on computer image processing has been proposed. However, the conventional draft mark segmentation may fail when the video sequence has many other regions than draft marks and a hull, and the estimated waterline is inherently higher than the true one. To solve these problems, we propose an automatic draft reading method that uses morphological operations to detect draft marks and estimate the waterline for every frame with Canny edge detection and a robust estimation. Moreover, we emulate surveyors' draft reading process for getting the understanding of a shipper and a receiver. In an experiment in a towing tank, the draft reading error of the proposed method was <1 cm, showing the advantage of the proposed method. It is also shown that accurate draft reading has been achieved in a real-world scene.

Correction method for influence of tissue scattering for sidestream dark-field oximetry using multicolor LEDs

NASA Astrophysics Data System (ADS)

Kurata, Tomohiro; Oda, Shigeto; Kawahira, Hiroshi; Haneishi, Hideaki

2016-12-01

We have previously proposed an estimation method of intravascular oxygen saturation (SO_2) from the images obtained by sidestream dark-field (SDF) imaging (we call it SDF oximetry) and we investigated its fundamental characteristics by Monte Carlo simulation. In this paper, we propose a correction method for scattering by the tissue and performed experiments with turbid phantoms as well as Monte Carlo simulation experiments to investigate the influence of the tissue scattering in the SDF imaging. In the estimation method, we used modified extinction coefficients of hemoglobin called average extinction coefficients (AECs) to correct the influence from the bandwidth of the illumination sources, the imaging camera characteristics, and the tissue scattering. We estimate the scattering coefficient of the tissue from the maximum slope of pixel value profile along a line perpendicular to the blood vessel running direction in an SDF image and correct AECs using the scattering coefficient. To evaluate the proposed method, we developed a trial SDF probe to obtain three-band images by switching multicolor light-emitting diodes and obtained the image of turbid phantoms comprised of agar powder, fat emulsion, and bovine blood-filled glass tubes. As a result, we found that the increase of scattering by the phantom body brought about the decrease of the AECs. The experimental results showed that the use of suitable values for AECs led to more accurate SO_2 estimation. We also confirmed the validity of the proposed correction method to improve the accuracy of the SO_2 estimation.

Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

PubMed

Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

2011-12-01

The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs.

PubMed

Han, Lianghao; Dong, Hua; McClelland, Jamie R; Han, Liangxiu; Hawkes, David J; Barratt, Dean C

2017-07-01

This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated. Copyright © 2017 Elsevier B.V. All rights reserved.

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

Tuo, Rui; Wu, C. F. Jeff

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

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

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

A new approach for continuous estimation of baseflow using discrete water quality data: Method description and comparison with baseflow estimates from two existing approaches

USGS Publications Warehouse

Miller, Matthew P.; Johnson, Henry M.; Susong, David D.; Wolock, David M.

2015-01-01

Understanding how watershed characteristics and climate influence the baseflow component of stream discharge is a topic of interest to both the scientific and water management communities. Therefore, the development of baseflow estimation methods is a topic of active research. Previous studies have demonstrated that graphical hydrograph separation (GHS) and conductivity mass balance (CMB) methods can be applied to stream discharge data to estimate daily baseflow. While CMB is generally considered to be a more objective approach than GHS, its application across broad spatial scales is limited by a lack of high frequency specific conductance (SC) data. We propose a new method that uses discrete SC data, which are widely available, to estimate baseflow at a daily time step using the CMB method. The proposed approach involves the development of regression models that relate discrete SC concentrations to stream discharge and time. Regression-derived CMB baseflow estimates were more similar to baseflow estimates obtained using a CMB approach with measured high frequency SC data than were the GHS baseflow estimates at twelve snowmelt dominated streams and rivers. There was a near perfect fit between the regression-derived and measured CMB baseflow estimates at sites where the regression models were able to accurately predict daily SC concentrations. We propose that the regression-derived approach could be applied to estimate baseflow at large numbers of sites, thereby enabling future investigations of watershed and climatic characteristics that influence the baseflow component of stream discharge across large spatial scales.

A vision-based method for planar position measurement

NASA Astrophysics Data System (ADS)

Chen, Zong-Hao; Huang, Peisen S.

2016-12-01

In this paper, a vision-based method is proposed for three-degree-of-freedom (3-DOF) planar position (XY{θZ} ) measurement. This method uses a single camera to capture the image of a 2D periodic pattern and then uses the 2D discrete Fourier transform (2D DFT) method to estimate the phase of its fundamental frequency component for position measurement. To improve position measurement accuracy, the phase estimation error of 2D DFT is analyzed and a phase estimation method is proposed. Different simulations are done to verify the feasibility of this method and study the factors that influence the accuracy and precision of phase estimation. To demonstrate the performance of the proposed method for position measurement, a prototype encoder consisting of a black-and-white industrial camera with VGA resolution (480  ×  640 pixels) and an iPhone 4s has been developed. Experimental results show the peak-to-peak resolutions to be 3.5 nm in X axis, 8 nm in Y axis and 4 μ \\text{rad} in {θZ} axis. The corresponding RMS resolutions are 0.52 nm, 1.06 nm, and 0.60 μ \\text{rad} respectively.

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

Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

NASA Astrophysics Data System (ADS)

Anayah, F. M.; Kaluarachchi, J. J.

2014-06-01

Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month-1, 10.55 mm month-1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

Noise parameter estimation for poisson corrupted images using variance stabilization transforms.

PubMed

Jin, Xiaodan; Xu, Zhenyu; Hirakawa, Keigo

2014-03-01

Noise is present in all images captured by real-world image sensors. Poisson distribution is said to model the stochastic nature of the photon arrival process and agrees with the distribution of measured pixel values. We propose a method for estimating unknown noise parameters from Poisson corrupted images using properties of variance stabilization. With a significantly lower computational complexity and improved stability, the proposed estimation technique yields noise parameters that are comparable in accuracy to the state-of-art methods.

Joint Smoothed l₀-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar.

PubMed

Liu, Jing; Zhou, Weidong; Juwono, Filbert H

2017-05-08

Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l 0 -norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance.

A different approach to estimate nonlinear regression model using numerical methods

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Mokeshrayalu, G.; Balasiddamuni, P.

2017-11-01

This research paper concerns with the computational methods namely the Gauss-Newton method, Gradient algorithm methods (Newton-Raphson method, Steepest Descent or Steepest Ascent algorithm method, the Method of Scoring, the Method of Quadratic Hill-Climbing) based on numerical analysis to estimate parameters of nonlinear regression model in a very different way. Principles of matrix calculus have been used to discuss the Gradient-Algorithm methods. Yonathan Bard [1] discussed a comparison of gradient methods for the solution of nonlinear parameter estimation problems. However this article discusses an analytical approach to the gradient algorithm methods in a different way. This paper describes a new iterative technique namely Gauss-Newton method which differs from the iterative technique proposed by Gorden K. Smyth [2]. Hans Georg Bock et.al [10] proposed numerical methods for parameter estimation in DAE’s (Differential algebraic equation). Isabel Reis Dos Santos et al [11], Introduced weighted least squares procedure for estimating the unknown parameters of a nonlinear regression metamodel. For large-scale non smooth convex minimization the Hager and Zhang (HZ) conjugate gradient Method and the modified HZ (MHZ) method were presented by Gonglin Yuan et al [12].

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.

SymPS: BRDF Symmetry Guided Photometric Stereo for Shape and Light Source Estimation.

PubMed

Lu, Feng; Chen, Xiaowu; Sato, Imari; Sato, Yoichi

2018-01-01

We propose uncalibrated photometric stereo methods that address the problem due to unknown isotropic reflectance. At the core of our methods is the notion of "constrained half-vector symmetry" for general isotropic BRDFs. We show that such symmetry can be observed in various real-world materials, and it leads to new techniques for shape and light source estimation. Based on the 1D and 2D representations of the symmetry, we propose two methods for surface normal estimation; one focuses on accurate elevation angle recovery for surface normals when the light sources only cover the visible hemisphere, and the other for comprehensive surface normal optimization in the case that the light sources are also non-uniformly distributed. The proposed robust light source estimation method also plays an essential role to let our methods work in an uncalibrated manner with good accuracy. Quantitative evaluations are conducted with both synthetic and real-world scenes, which produce the state-of-the-art accuracy for all of the non-Lambertian materials in MERL database and the real-world datasets.

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

Model-based inference for small area estimation with sampling weights

PubMed Central

Vandendijck, Y.; Faes, C.; Kirby, R.S.; Lawson, A.; Hens, N.

2017-01-01

Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised in SAE as a spatial smoothing method, but often ignore the sampling weights that reflect the complex sampling design. In this paper, we focus on data obtained from a health survey where the sampling weights of the sampled individuals are the only information available about the design. We develop a predictive model-based approach to estimate the prevalence of a binary outcome for both the sampled and non-sampled individuals, using hierarchical Bayesian models that take into account the sampling weights. A simulation study is carried out to compare the performance of our proposed method with other established methods. The results indicate that our proposed method achieves great reductions in mean squared error when compared with standard approaches. It performs equally well or better when compared with more elaborate methods when there is a relationship between the responses and the sampling weights. The proposed method is applied to estimate asthma prevalence across districts. PMID:28989860

Enabling real-time ultrasound imaging of soft tissue mechanical properties by simplification of the shear wave motion equation.

PubMed

Engel, Aaron J; Bashford, Gregory R

2015-08-01

Ultrasound based shear wave elastography (SWE) is a technique used for non-invasive characterization and imaging of soft tissue mechanical properties. Robust estimation of shear wave propagation speed is essential for imaging of soft tissue mechanical properties. In this study we propose to estimate shear wave speed by inversion of the first-order wave equation following directional filtering. This approach relies on estimation of first-order derivatives which allows for accurate estimations using smaller smoothing filters than when estimating second-order derivatives. The performance was compared to three current methods used to estimate shear wave propagation speed: direct inversion of the wave equation (DIWE), time-to-peak (TTP) and cross-correlation (CC). The shear wave speed of three homogeneous phantoms of different elastic moduli (gelatin by weight of 5%, 7%, and 9%) were measured with each method. The proposed method was shown to produce shear speed estimates comparable to the conventional methods (standard deviation of measurements being 0.13 m/s, 0.05 m/s, and 0.12 m/s), but with simpler processing and usually less time (by a factor of 1, 13, and 20 for DIWE, CC, and TTP respectively). The proposed method was able to produce a 2-D speed estimate from a single direction of wave propagation in about four seconds using an off-the-shelf PC, showing the feasibility of performing real-time or near real-time elasticity imaging with dedicated hardware.

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.

Hazard Function Estimation with Cause-of-Death Data Missing at Random.

PubMed

Wang, Qihua; Dinse, Gregg E; Liu, Chunling

2012-04-01

Hazard function estimation is an important part of survival analysis. Interest often centers on estimating the hazard function associated with a particular cause of death. We propose three nonparametric kernel estimators for the hazard function, all of which are appropriate when death times are subject to random censorship and censoring indicators can be missing at random. Specifically, we present a regression surrogate estimator, an imputation estimator, and an inverse probability weighted estimator. All three estimators are uniformly strongly consistent and asymptotically normal. We derive asymptotic representations of the mean squared error and the mean integrated squared error for these estimators and we discuss a data-driven bandwidth selection method. A simulation study, conducted to assess finite sample behavior, demonstrates that the proposed hazard estimators perform relatively well. We illustrate our methods with an analysis of some vascular disease data.

A new combined approach on Hurst exponent estimate and its applications in realized volatility

NASA Astrophysics Data System (ADS)

Luo, Yi; Huang, Yirong

2018-02-01

The purpose of this paper is to propose a new estimator of Hurst exponent based on the combined information of the conventional rescaled range methods. We demonstrate the superiority of the proposed estimator by Monte Carlo simulations, and the applications in estimating the Hurst exponent of daily volatility series in Chinese stock market. Moreover, we indicate the impact of the type of estimator and structural break on the estimating results of Hurst exponent.

A wavelet-based Gaussian method for energy dispersive X-ray fluorescence spectrum.

PubMed

Liu, Pan; Deng, Xiaoyan; Tang, Xin; Shen, Shijian

2017-05-01

This paper presents a wavelet-based Gaussian method (WGM) for the peak intensity estimation of energy dispersive X-ray fluorescence (EDXRF). The relationship between the parameters of Gaussian curve and the wavelet coefficients of Gaussian peak point is firstly established based on the Mexican hat wavelet. It is found that the Gaussian parameters can be accurately calculated by any two wavelet coefficients at the peak point which has to be known. This fact leads to a local Gaussian estimation method for spectral peaks, which estimates the Gaussian parameters based on the detail wavelet coefficients of Gaussian peak point. The proposed method is tested via simulated and measured spectra from an energy X-ray spectrometer, and compared with some existing methods. The results prove that the proposed method can directly estimate the peak intensity of EDXRF free from the background information, and also effectively distinguish overlap peaks in EDXRF spectrum.

An estimating equation approach to dimension reduction for longitudinal data

PubMed Central

Xu, Kelin; Guo, Wensheng; Xiong, Momiao; Zhu, Liping; Jin, Li

2016-01-01

Sufficient dimension reduction has been extensively explored in the context of independent and identically distributed data. In this article we generalize sufficient dimension reduction to longitudinal data and propose an estimating equation approach to estimating the central mean subspace. The proposed method accounts for the covariance structure within each subject and improves estimation efficiency when the covariance structure is correctly specified. Even if the covariance structure is misspecified, our estimator remains consistent. In addition, our method relaxes distributional assumptions on the covariates and is doubly robust. To determine the structural dimension of the central mean subspace, we propose a Bayesian-type information criterion. We show that the estimated structural dimension is consistent and that the estimated basis directions are root-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$n$\\end{document} consistent, asymptotically normal and locally efficient. Simulations and an analysis of the Framingham Heart Study data confirm the effectiveness of our approach. PMID:27017956

Motion estimation in the frequency domain using fuzzy c-planes clustering.

PubMed

Erdem, C E; Karabulut, G Z; Yanmaz, E; Anarim, E

2001-01-01

A recent work explicitly models the discontinuous motion estimation problem in the frequency domain where the motion parameters are estimated using a harmonic retrieval approach. The vertical and horizontal components of the motion are independently estimated from the locations of the peaks of respective periodogram analyses and they are paired to obtain the motion vectors using a procedure proposed. In this paper, we present a more efficient method that replaces the motion component pairing task and hence eliminates the problems of the pairing method described. The method described in this paper uses the fuzzy c-planes (FCP) clustering approach to fit planes to three-dimensional (3-D) frequency domain data obtained from the peaks of the periodograms. Experimental results are provided to demonstrate the effectiveness of the proposed method.

Receiver IQ mismatch estimation in PDM CO-OFDM system using training symbol

NASA Astrophysics Data System (ADS)

Peng, Dandan; Ma, Xiurong; Yao, Xin; Zhang, Haoyuan

2017-07-01

Receiver in-phase/quadrature (IQ) mismatch is hard to mitigate at the receiver via using conventional method in polarization division multiplexed (PDM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. In this paper, a novel training symbol structure is proposed to estimate IQ mismatch and channel distortion. Combined this structure with Gram Schmidt orthogonalization procedure (GSOP) algorithm, we can get lower bit error rate (BER). Meanwhile, based on this structure one estimation method is deduced in frequency domain which can achieve the estimation of IQ mismatch and channel distortion independently and improve the system performance obviously. Numerical simulation shows that the proposed two methods have better performance than compared method at 100 Gb/s after 480 km fiber transmission. Besides, the calculation complexity is also analyzed.

Rotor Position Sensorless Control and Its Parameter Sensitivity of Permanent Magnet Motor Based on Model Reference Adaptive System

NASA Astrophysics Data System (ADS)

Ohara, Masaki; Noguchi, Toshihiko

This paper describes a new method for a rotor position sensorless control of a surface permanent magnet synchronous motor based on a model reference adaptive system (MRAS). This method features the MRAS in a current control loop to estimate a rotor speed and position by using only current sensors. This method as well as almost all the conventional methods incorporates a mathematical model of the motor, which consists of parameters such as winding resistances, inductances, and an induced voltage constant. Hence, the important thing is to investigate how the deviation of these parameters affects the estimated rotor position. First, this paper proposes a structure of the sensorless control applied in the current control loop. Next, it proves the stability of the proposed method when motor parameters deviate from the nominal values, and derives the relationship between the estimated position and the deviation of the parameters in a steady state. Finally, some experimental results are presented to show performance and effectiveness of the proposed method.

Cross-correlation analysis of pulse wave propagation in arteries: in vitro validation and in vivo feasibility

NASA Astrophysics Data System (ADS)

Nauleau, Pierre; Apostolakis, Iason; McGarry, Matthew; Konofagou, Elisa

2018-06-01

The stiffness of the arteries is known to be an indicator of the progression of various cardiovascular diseases. Clinically, the pulse wave velocity (PWV) is used as a surrogate for arterial stiffness. Pulse wave imaging (PWI) is a non-invasive, ultrasound-based imaging technique capable of mapping the motion of the vessel walls, allowing the local assessment of arterial properties. Conventionally, a distinctive feature of the displacement wave (e.g. the 50% upstroke) is tracked across the map to estimate the PWV. However, the presence of reflections, such as those generated at the carotid bifurcation, can bias the PWV estimation. In this paper, we propose a two-step cross-correlation based method to characterize arteries using the information available in the PWI spatio-temporal map. First, the area under the cross-correlation curve is proposed as an index for locating the regions of different properties. Second, a local peak of the cross-correlation function is tracked to obtain a less biased estimate of the PWV. Three series of experiments were conducted in phantoms to evaluate the capabilities of the proposed method compared with the conventional method. In the ideal case of a homogeneous phantom, the two methods performed similarly and correctly estimated the PWV. In the presence of reflections, the proposed method provided a more accurate estimate than conventional processing: e.g. for the soft phantom, biases of  ‑0.27 and ‑0.71 m · s–1 were observed. In a third series of experiments, the correlation-based method was able to locate two regions of different properties with an error smaller than 1 mm. It also provided more accurate PWV estimates than conventional processing (biases:  ‑0.12 versus ‑0.26 m · s–1). Finally, the in vivo feasibility of the proposed method was demonstrated in eleven healthy subjects. The results indicate that the correlation-based method might be less precise in vivo but more accurate than the conventional method.

A method for estimating abundance of mobile populations using telemetry and counts of unmarked animals

USGS Publications Warehouse

Clement, Matthew; O'Keefe, Joy M; Walters, Brianne

2015-01-01

While numerous methods exist for estimating abundance when detection is imperfect, these methods may not be appropriate due to logistical difficulties or unrealistic assumptions. In particular, if highly mobile taxa are frequently absent from survey locations, methods that estimate a probability of detection conditional on presence will generate biased abundance estimates. Here, we propose a new estimator for estimating abundance of mobile populations using telemetry and counts of unmarked animals. The estimator assumes that the target population conforms to a fission-fusion grouping pattern, in which the population is divided into groups that frequently change in size and composition. If assumptions are met, it is not necessary to locate all groups in the population to estimate abundance. We derive an estimator, perform a simulation study, conduct a power analysis, and apply the method to field data. The simulation study confirmed that our estimator is asymptotically unbiased with low bias, narrow confidence intervals, and good coverage, given a modest survey effort. The power analysis provided initial guidance on survey effort. When applied to small data sets obtained by radio-tracking Indiana bats, abundance estimates were reasonable, although imprecise. The proposed method has the potential to improve abundance estimates for mobile species that have a fission-fusion social structure, such as Indiana bats, because it does not condition detection on presence at survey locations and because it avoids certain restrictive assumptions.

The estimation of 3D SAR distributions in the human head from mobile phone compliance testing data for epidemiological studies

NASA Astrophysics Data System (ADS)

Wake, Kanako; Varsier, Nadège; Watanabe, Soichi; Taki, Masao; Wiart, Joe; Mann, Simon; Deltour, Isabelle; Cardis, Elisabeth

2009-10-01

A worldwide epidemiological study called 'INTERPHONE' has been conducted to estimate the hypothetical relationship between brain tumors and mobile phone use. In this study, we proposed a method to estimate 3D distribution of the specific absorption rate (SAR) in the human head due to mobile phone use to provide the exposure gradient for epidemiological studies. 3D SAR distributions due to exposure to an electromagnetic field from mobile phones are estimated from mobile phone compliance testing data for actual devices. The data for compliance testing are measured only on the surface in the region near the device and in a small 3D region around the maximum on the surface in a homogeneous phantom with a specific shape. The method includes an interpolation/extrapolation and a head shape conversion. With the interpolation/extrapolation, SAR distributions in the whole head are estimated from the limited measured data. 3D SAR distributions in the numerical head models, where the tumor location is identified in the epidemiological studies, are obtained from measured SAR data with the head shape conversion by projection. Validation of the proposed method was performed experimentally and numerically. It was confirmed that the proposed method provided good estimation of 3D SAR distribution in the head, especially in the brain, which is the tissue of major interest in epidemiological studies. We conclude that it is possible to estimate 3D SAR distributions in a realistic head model from the data obtained by compliance testing measurements to provide a measure for the exposure gradient in specific locations of the brain for the purpose of exposure assessment in epidemiological studies. The proposed method has been used in several studies in the INTERPHONE.

The estimation of 3D SAR distributions in the human head from mobile phone compliance testing data for epidemiological studies.

PubMed

Wake, Kanako; Varsier, Nadège; Watanabe, Soichi; Taki, Masao; Wiart, Joe; Mann, Simon; Deltour, Isabelle; Cardis, Elisabeth

2009-10-07

A worldwide epidemiological study called 'INTERPHONE' has been conducted to estimate the hypothetical relationship between brain tumors and mobile phone use. In this study, we proposed a method to estimate 3D distribution of the specific absorption rate (SAR) in the human head due to mobile phone use to provide the exposure gradient for epidemiological studies. 3D SAR distributions due to exposure to an electromagnetic field from mobile phones are estimated from mobile phone compliance testing data for actual devices. The data for compliance testing are measured only on the surface in the region near the device and in a small 3D region around the maximum on the surface in a homogeneous phantom with a specific shape. The method includes an interpolation/extrapolation and a head shape conversion. With the interpolation/extrapolation, SAR distributions in the whole head are estimated from the limited measured data. 3D SAR distributions in the numerical head models, where the tumor location is identified in the epidemiological studies, are obtained from measured SAR data with the head shape conversion by projection. Validation of the proposed method was performed experimentally and numerically. It was confirmed that the proposed method provided good estimation of 3D SAR distribution in the head, especially in the brain, which is the tissue of major interest in epidemiological studies. We conclude that it is possible to estimate 3D SAR distributions in a realistic head model from the data obtained by compliance testing measurements to provide a measure for the exposure gradient in specific locations of the brain for the purpose of exposure assessment in epidemiological studies. The proposed method has been used in several studies in the INTERPHONE.

Walking Distance Estimation Using Walking Canes with Inertial Sensors

PubMed Central

Suh, Young Soo

2018-01-01

A walking distance estimation algorithm for cane users is proposed using an inertial sensor unit attached to various positions on the cane. A standard inertial navigation algorithm using an indirect Kalman filter was applied to update the velocity and position of the cane during movement. For quadripod canes, a standard zero-velocity measurement-updating method is proposed. For standard canes, a velocity-updating method based on an inverted pendulum model is proposed. The proposed algorithms were verified by three walking experiments with two different types of canes and different positions of the sensor module. PMID:29342971

Road-Aided Ground Slowly Moving Target 2D Motion Estimation for Single-Channel Synthetic Aperture Radar.

PubMed

Wang, Zhirui; Xu, Jia; Huang, Zuzhen; Zhang, Xudong; Xia, Xiang-Gen; Long, Teng; Bao, Qian

2016-03-16

To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target's position on the road as well as its radial velocity can be determined according to the target's offset distance and traffic rules. Furthermore, the target's azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.

Brief Report: Investigating Uncertainty in the Minimum Mortality Temperature: Methods and Application to 52 Spanish Cities.

PubMed

Tobías, Aurelio; Armstrong, Ben; Gasparrini, Antonio

2017-01-01

The minimum mortality temperature from J- or U-shaped curves varies across cities with different climates. This variation conveys information on adaptation, but ability to characterize is limited by the absence of a method to describe uncertainty in estimated minimum mortality temperatures. We propose an approximate parametric bootstrap estimator of confidence interval (CI) and standard error (SE) for the minimum mortality temperature from a temperature-mortality shape estimated by splines. The coverage of the estimated CIs was close to nominal value (95%) in the datasets simulated, although SEs were slightly high. Applying the method to 52 Spanish provincial capital cities showed larger minimum mortality temperatures in hotter cities, rising almost exactly at the same rate as annual mean temperature. The method proposed for computing CIs and SEs for minimums from spline curves allows comparing minimum mortality temperatures in different cities and investigating their associations with climate properly, allowing for estimation uncertainty.

Maximum Likelihood Estimations and EM Algorithms with Length-biased Data

PubMed Central

Qin, Jing; Ning, Jing; Liu, Hao; Shen, Yu

2012-01-01

SUMMARY Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, epidemiological, genetic and cancer screening studies. Length-biased right-censored data have a unique data structure different from traditional survival data. The nonparametric and semiparametric estimations and inference methods for traditional survival data are not directly applicable for length-biased right-censored data. We propose new expectation-maximization algorithms for estimations based on full likelihoods involving infinite dimensional parameters under three settings for length-biased data: estimating nonparametric distribution function, estimating nonparametric hazard function under an increasing failure rate constraint, and jointly estimating baseline hazards function and the covariate coefficients under the Cox proportional hazards model. Extensive empirical simulation studies show that the maximum likelihood estimators perform well with moderate sample sizes and lead to more efficient estimators compared to the estimating equation approaches. The proposed estimates are also more robust to various right-censoring mechanisms. We prove the strong consistency properties of the estimators, and establish the asymptotic normality of the semi-parametric maximum likelihood estimators under the Cox model using modern empirical processes theory. We apply the proposed methods to a prevalent cohort medical study. Supplemental materials are available online. PMID:22323840

Molar axis estimation from computed tomography images.

PubMed

Dongxia Zhang; Yangzhou Gan; Zeyang Xia; Xinwen Zhou; Shoubin Liu; Jing Xiong; Guanglin Li

2016-08-01

Estimation of tooth axis is needed for some clinical dental treatment. Existing methods require to segment the tooth volume from Computed Tomography (CT) images, and then estimate the axis from the tooth volume. However, they may fail during estimating molar axis due to that the tooth segmentation from CT images is challenging and current segmentation methods may get poor segmentation results especially for these molars with angle which will result in the failure of axis estimation. To resolve this problem, this paper proposes a new method for molar axis estimation from CT images. The key innovation point is that: instead of estimating the 3D axis of each molar from the segmented volume, the method estimates the 3D axis from two projection images. The method includes three steps. (1) The 3D images of each molar are projected to two 2D image planes. (2) The molar contour are segmented and the contour's 2D axis are extracted in each 2D projection image. Principal Component Analysis (PCA) and a modified symmetry axis detection algorithm are employed to extract the 2D axis from the segmented molar contour. (3) A 3D molar axis is obtained by combining the two 2D axes. Experimental results verified that the proposed method was effective to estimate the axis of molar from CT images.

Shear wave elastography using Wigner-Ville distribution: a simulated multilayer media study.

PubMed

Bidari, Pooya Sobhe; Alirezaie, Javad; Tavakkoli, Jahan

2016-08-01

Shear Wave Elastography (SWE) is a quantitative ultrasound-based imaging modality for distinguishing normal and abnormal tissue types by estimating the local viscoelastic properties of the tissue. These properties have been estimated in many studies by propagating ultrasound shear wave within the tissue and estimating parameters such as speed of wave. Vast majority of the proposed techniques are based on the cross-correlation of consecutive ultrasound images. In this study, we propose a new method of wave detection based on time-frequency (TF) analysis of the ultrasound signal. The proposed method is a modified version of the Wigner-Ville Distribution (WVD) technique. The TF components of the wave are detected in a propagating ultrasound wave within a simulated multilayer tissue and the local properties are estimated based on the detected waves. Image processing techniques such as Alternative Sequential Filters (ASF) and Circular Hough Transform (CHT) have been utilized to improve the estimation of TF components. This method has been applied to a simulated data from Wave3000™ software (CyberLogic Inc., New York, NY). This data simulates the propagation of an acoustic radiation force impulse within a two-layer tissue with slightly different viscoelastic properties between the layers. By analyzing the local TF components of the wave, we estimate the longitudinal and shear elasticities and viscosities of the media. This work shows that our proposed method is capable of distinguishing between different layers of a tissue.

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. PMID:23593445

An Empirical Comparison of Heterogeneity Variance Estimators in 12,894 Meta-Analyses

ERIC Educational Resources Information Center

Langan, Dean; Higgins, Julian P. T.; Simmonds, Mark

2015-01-01

Heterogeneity in meta-analysis is most commonly estimated using a moment-based approach described by DerSimonian and Laird. However, this method has been shown to produce biased estimates. Alternative methods to estimate heterogeneity include the restricted maximum likelihood approach and those proposed by Paule and Mandel, Sidik and Jonkman, and…

Evaluation of the Current Techniques and Introduction of a Novel Approach for Estimating Maxillary Anterior Teeth Dimensions.

PubMed

Sayed, Mohammed E; Porwal, Amit; Al-Faraj, Nida A; Bajonaid, Amal M; Sumayli, Hassan A

2017-07-01

Several techniques and methods have been proposed to estimate the anterior teeth dimensions in edentulous patients. However, this procedure remains challenging especially when preextraction records are not available. Therefore, the purpose of this study is to evaluate some of the existing extraoral and intraoral methods for estimation of anterior tooth dimensions and to propose a novel method for estimation of central incisor width (CIW) and length (CIL) for Saudi population. Extraoral and intraoral measurements were recorded for a total of 236 subjects. Descriptive statistical analysis and Pearson's correlation tests were performed. Association was evaluated between combined anterior teeth width (CATW) and interalar width (IAW), intercommisural width (ICoW) and interhamular notch distance (IHND) plus 10 mm. Evaluation of the linear relationship between central incisor length (CIL) with facial height (FH) and CIW with bizygomatic width (BZW) was also performed. Significant correlation was found between the CATW and ICoW and IAW (p-values <0.0001); however, no correlation was found relative to IHND plus 10 mm (p-value = 0.456). Further, no correlation was found between the FH and right CIL and BZW and right CIW (p-values = 0.255 and 0.822). The means of CIL, CIW, incisive papillae-fovea palatinae (IP-FP), and IHND were used to estimate the central incisor dimensions: CIL = FP-IP distance/4.45, CIW = IHND/4.49. It was concluded that the ICoW and IAW measurements are the only predictable methods to estimate the initial reference value for CATW. A proposed intraoral approach was hypothesized for estimation of CIW and CIL for the given population. Based on the results of the study, ICoW and IAW measurements can be useful in estimating the initial reference value for CATW, while the proposed novel approach using specific palatal dimensions can be used for estimating the width and length of central incisors. These methods are crucial to obtain esthetic treatment results within the parameters of the given population.

Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.

PubMed

Du, Pang; Tang, Liansheng

2009-01-30

When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example. Copyright (c) 2008 John Wiley & Sons, Ltd.

Nonparametric estimation of median survival times with applications to multi-site or multi-center studies.

PubMed

Rahbar, Mohammad H; Choi, Sangbum; Hong, Chuan; Zhu, Liang; Jeon, Sangchoon; Gardiner, Joseph C

2018-01-01

We propose a nonparametric shrinkage estimator for the median survival times from several independent samples of right-censored data, which combines the samples and hypothesis information to improve the efficiency. We compare efficiency of the proposed shrinkage estimation procedure to unrestricted estimator and combined estimator through extensive simulation studies. Our results indicate that performance of these estimators depends on the strength of homogeneity of the medians. When homogeneity holds, the combined estimator is the most efficient estimator. However, it becomes inconsistent when homogeneity fails. On the other hand, the proposed shrinkage estimator remains efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good as or equal to the unrestricted estimator. Our simulation studies also indicate that the proposed shrinkage estimator is robust to moderate levels of censoring. We demonstrate application of these methods to estimating median time for trauma patients to receive red blood cells in the Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study.

Nonparametric estimation of median survival times with applications to multi-site or multi-center studies

PubMed Central

Choi, Sangbum; Hong, Chuan; Zhu, Liang; Jeon, Sangchoon; Gardiner, Joseph C.

2018-01-01

We propose a nonparametric shrinkage estimator for the median survival times from several independent samples of right-censored data, which combines the samples and hypothesis information to improve the efficiency. We compare efficiency of the proposed shrinkage estimation procedure to unrestricted estimator and combined estimator through extensive simulation studies. Our results indicate that performance of these estimators depends on the strength of homogeneity of the medians. When homogeneity holds, the combined estimator is the most efficient estimator. However, it becomes inconsistent when homogeneity fails. On the other hand, the proposed shrinkage estimator remains efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good as or equal to the unrestricted estimator. Our simulation studies also indicate that the proposed shrinkage estimator is robust to moderate levels of censoring. We demonstrate application of these methods to estimating median time for trauma patients to receive red blood cells in the Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study. PMID:29772007

Non-Rigid Structure Estimation in Trajectory Space from Monocular Vision

PubMed Central

Wang, Yaming; Tong, Lingling; Jiang, Mingfeng; Zheng, Junbao

2015-01-01

In this paper, the problem of non-rigid structure estimation in trajectory space from monocular vision is investigated. Similar to the Point Trajectory Approach (PTA), based on characteristic points’ trajectories described by a predefined Discrete Cosine Transform (DCT) basis, the structure matrix was also calculated by using a factorization method. To further optimize the non-rigid structure estimation from monocular vision, the rank minimization problem about structure matrix is proposed to implement the non-rigid structure estimation by introducing the basic low-rank condition. Moreover, the Accelerated Proximal Gradient (APG) algorithm is proposed to solve the rank minimization problem, and the initial structure matrix calculated by the PTA method is optimized. The APG algorithm can converge to efficient solutions quickly and lessen the reconstruction error obviously. The reconstruction results of real image sequences indicate that the proposed approach runs reliably, and effectively improves the accuracy of non-rigid structure estimation from monocular vision. PMID:26473863

Discovering graphical Granger causality using the truncating lasso penalty

PubMed Central

Shojaie, Ali; Michailidis, George

2010-01-01

Motivation: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem in systems biology. Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used to discover regulatory interactions among genes. Results: In this article, we propose a novel penalization method, called truncating lasso, for estimation of causal relationships from time-course gene expression data. The proposed penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. Moreover, the resulting estimate provides information on the time lag between activation of transcription factors and their effects on regulated genes. We provide an efficient algorithm for estimation of model parameters, and show that the proposed method can consistently discover causal relationships in the large p, small n setting. The performance of the proposed model is evaluated favorably in simulated, as well as real, data examples. Availability: The proposed truncating lasso method is implemented in the R-package ‘grangerTlasso’ and is freely available at http://www.stat.lsa.umich.edu/∼shojaie/ Contact: shojaie@umich.edu PMID:20823316

Self-calibration method without joint iteration for distributed small satellite SAR systems

NASA Astrophysics Data System (ADS)

Xu, Qing; Liao, Guisheng; Liu, Aifei; Zhang, Juan

2013-12-01

The performance of distributed small satellite synthetic aperture radar systems degrades significantly due to the unavoidable array errors, including gain, phase, and position errors, in real operating scenarios. In the conventional method proposed in (IEEE T Aero. Elec. Sys. 42:436-451, 2006), the spectrum components within one Doppler bin are considered as calibration sources. However, it is found in this article that the gain error estimation and the position error estimation in the conventional method can interact with each other. The conventional method may converge to suboptimal solutions in large position errors since it requires the joint iteration between gain-phase error estimation and position error estimation. In addition, it is also found that phase errors can be estimated well regardless of position errors when the zero Doppler bin is chosen. In this article, we propose a method obtained by modifying the conventional one, based on these two observations. In this modified method, gain errors are firstly estimated and compensated, which eliminates the interaction between gain error estimation and position error estimation. Then, by using the zero Doppler bin data, the phase error estimation can be performed well independent of position errors. Finally, position errors are estimated based on the Taylor-series expansion. Meanwhile, the joint iteration between gain-phase error estimation and position error estimation is not required. Therefore, the problem of suboptimal convergence, which occurs in the conventional method, can be avoided with low computational method. The modified method has merits of faster convergence and lower estimation error compared to the conventional one. Theoretical analysis and computer simulation results verified the effectiveness of the modified method.

Estimating roadside encroachment rates with the combined strengths of accident- and encroachment-based approaches

DOT National Transportation Integrated Search

2001-09-01

In two recent studies by Miaou, he proposed a method to estimate vehicle roadside encroachment rates using accident-based models. He further illustrated the use of this method to estimate roadside encroachment rates for rural two-lane undivided roads...

A pose estimation method for unmanned ground vehicles in GPS denied environments

NASA Astrophysics Data System (ADS)

Tamjidi, Amirhossein; Ye, Cang

2012-06-01

This paper presents a pose estimation method based on the 1-Point RANSAC EKF (Extended Kalman Filter) framework. The method fuses the depth data from a LIDAR and the visual data from a monocular camera to estimate the pose of a Unmanned Ground Vehicle (UGV) in a GPS denied environment. Its estimation framework continuy updates the vehicle's 6D pose state and temporary estimates of the extracted visual features' 3D positions. In contrast to the conventional EKF-SLAM (Simultaneous Localization And Mapping) frameworks, the proposed method discards feature estimates from the extended state vector once they are no longer observed for several steps. As a result, the extended state vector always maintains a reasonable size that is suitable for online calculation. The fusion of laser and visual data is performed both in the feature initialization part of the EKF-SLAM process and in the motion prediction stage. A RANSAC pose calculation procedure is devised to produce pose estimate for the motion model. The proposed method has been successfully tested on the Ford campus's LIDAR-Vision dataset. The results are compared with the ground truth data of the dataset and the estimation error is ~1.9% of the path length.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

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

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

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.

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.

Hybrid method to estimate two-layered superficial tissue optical properties from simulated data of diffuse reflectance spectroscopy.

PubMed

Hsieh, Hong-Po; Ko, Fan-Hua; Sung, Kung-Bin

2018-04-20

An iterative curve fitting method has been applied in both simulation [J. Biomed. Opt.17, 107003 (2012)JBOPFO1083-366810.1117/1.JBO.17.10.107003] and phantom [J. Biomed. Opt.19, 077002 (2014)JBOPFO1083-366810.1117/1.JBO.19.7.077002] studies to accurately extract optical properties and the top layer thickness of a two-layered superficial tissue model from diffuse reflectance spectroscopy (DRS) data. This paper describes a hybrid two-step parameter estimation procedure to address two main issues of the previous method, including (1) high computational intensity and (2) converging to local minima. The parameter estimation procedure contained a novel initial estimation step to obtain an initial guess, which was used by a subsequent iterative fitting step to optimize the parameter estimation. A lookup table was used in both steps to quickly obtain reflectance spectra and reduce computational intensity. On simulated DRS data, the proposed parameter estimation procedure achieved high estimation accuracy and a 95% reduction of computational time compared to previous studies. Furthermore, the proposed initial estimation step led to better convergence of the following fitting step. Strategies used in the proposed procedure could benefit both the modeling and experimental data processing of not only DRS but also related approaches such as near-infrared spectroscopy.

Optimization-based scatter estimation using primary modulation for computed tomography

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

Chen, Yi; Ma, Jingchen; Zhao, Jun, E-mail: junzhao

Purpose: Scatter reduces the image quality in computed tomography (CT), but scatter correction remains a challenge. A previously proposed primary modulation method simultaneously obtains the primary and scatter in a single scan. However, separating the scatter and primary in primary modulation is challenging because it is an underdetermined problem. In this study, an optimization-based scatter estimation (OSE) algorithm is proposed to estimate and correct scatter. Methods: In the concept of primary modulation, the primary is modulated, but the scatter remains smooth by inserting a modulator between the x-ray source and the object. In the proposed algorithm, an objective function ismore » designed for separating the scatter and primary. Prior knowledge is incorporated in the optimization-based framework to improve the accuracy of the estimation: (1) the primary is always positive; (2) the primary is locally smooth and the scatter is smooth; (3) the location of penumbra can be determined; and (4) the scatter-contaminated data provide knowledge about which part is smooth. Results: The simulation study shows that the edge-preserving weighting in OSE improves the estimation accuracy near the object boundary. Simulation study also demonstrates that OSE outperforms the two existing primary modulation algorithms for most regions of interest in terms of the CT number accuracy and noise. The proposed method was tested on a clinical cone beam CT, demonstrating that OSE corrects the scatter even when the modulator is not accurately registered. Conclusions: The proposed OSE algorithm improves the robustness and accuracy in scatter estimation and correction. This method is promising for scatter correction of various kinds of x-ray imaging modalities, such as x-ray radiography, cone beam CT, and the fourth-generation CT.« less

Estimating survival probabilities by exposure levels: utilizing vital statistics and complex survey data with mortality follow-up.

PubMed

Landsman, V; Lou, W Y W; Graubard, B I

2015-05-20

We present a two-step approach for estimating hazard rates and, consequently, survival probabilities, by levels of general categorical exposure. The resulting estimator utilizes three sources of data: vital statistics data and census data are used at the first step to estimate the overall hazard rate for a given combination of gender and age group, and cohort data constructed from a nationally representative complex survey with linked mortality records, are used at the second step to divide the overall hazard rate by exposure levels. We present an explicit expression for the resulting estimator and consider two methods for variance estimation that account for complex multistage sample design: (1) the leaving-one-out jackknife method, and (2) the Taylor linearization method, which provides an analytic formula for the variance estimator. The methods are illustrated with smoking and all-cause mortality data from the US National Health Interview Survey Linked Mortality Files, and the proposed estimator is compared with a previously studied crude hazard rate estimator that uses survey data only. The advantages of a two-step approach and possible extensions of the proposed estimator are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

System identification of velocity mechanomyogram measured with a capacitor microphone for muscle stiffness estimation.

PubMed

Uchiyama, Takanori; Tomoshige, Taiki

2017-04-01

A mechanomyogram (MMG) measured with a displacement sensor (displacement MMG) can provide a better estimation of longitudinal muscle stiffness than that measured with an acceleration sensor (acceleration MMG), but the displacement MMG cannot provide transverse muscle stiffness. We propose a method to estimate both longitudinal and transverse muscle stiffness from a velocity MMG using a system identification technique. The aims of this study are to show the advantages of the proposed method. The velocity MMG was measured using a capacitor microphone and a differential circuit, and the MMG, evoked by electrical stimulation, of the tibialis anterior muscle was measured five times in seven healthy young male volunteers. The evoked MMG system was identified using the singular value decomposition method and was approximated with a fourth-order model, which provides two undamped natural frequencies corresponding to the longitudinal and transverse muscle stiffness. The fluctuation of the undamped natural frequencies estimated from the velocity MMG was significantly smaller than that from the acceleration MMG. There was no significant difference between the fluctuations of the undamped natural frequencies estimated from the velocity MMG and that from the displacement MMG. The proposed method using the velocity MMG is thus more advantageous for muscle stiffness estimation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images

PubMed Central

Fan, Chong; Wu, Chaoyun; Li, Grand; Ma, Jun

2017-01-01

To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the high-resolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method. PMID:28208837

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 Method for Block Size Forensics Based on Morphological Operations

NASA Astrophysics Data System (ADS)

Luo, Weiqi; Huang, Jiwu; Qiu, Guoping

Passive forensics analysis aims to find out how multimedia data is acquired and processed without relying on pre-embedded or pre-registered information. Since most existing compression schemes for digital images are based on block processing, one of the fundamental steps for subsequent forensics analysis is to detect the presence of block artifacts and estimate the block size for a given image. In this paper, we propose a novel method for blind block size estimation. A 2×2 cross-differential filter is first applied to detect all possible block artifact boundaries, morphological operations are then used to remove the boundary effects caused by the edges of the actual image contents, and finally maximum-likelihood estimation (MLE) is employed to estimate the block size. The experimental results evaluated on over 1300 nature images show the effectiveness of our proposed method. Compared with existing gradient-based detection method, our method achieves over 39% accuracy improvement on average.

An improved principal component analysis based region matching method for fringe direction estimation

NASA Astrophysics Data System (ADS)

He, A.; Quan, C.

2018-04-01

The principal component analysis (PCA) and region matching combined method is effective for fringe direction estimation. However, its mask construction algorithm for region matching fails in some circumstances, and the algorithm for conversion of orientation to direction in mask areas is computationally-heavy and non-optimized. We propose an improved PCA based region matching method for the fringe direction estimation, which includes an improved and robust mask construction scheme, and a fast and optimized orientation-direction conversion algorithm for the mask areas. Along with the estimated fringe direction map, filtered fringe pattern by automatic selective reconstruction modification and enhanced fast empirical mode decomposition (ASRm-EFEMD) is used for Hilbert spiral transform (HST) to demodulate the phase. Subsequently, windowed Fourier ridge (WFR) method is used for the refinement of the phase. The robustness and effectiveness of proposed method are demonstrated by both simulated and experimental fringe patterns.

The Dynamic Photometric Stereo Method Using a Multi-Tap CMOS Image Sensor.

PubMed

Yoda, Takuya; Nagahara, Hajime; Taniguchi, Rin-Ichiro; Kagawa, Keiichiro; Yasutomi, Keita; Kawahito, Shoji

2018-03-05

The photometric stereo method enables estimation of surface normals from images that have been captured using different but known lighting directions. The classical photometric stereo method requires at least three images to determine the normals in a given scene. However, this method cannot be applied to dynamic scenes because it is assumed that the scene remains static while the required images are captured. In this work, we present a dynamic photometric stereo method for estimation of the surface normals in a dynamic scene. We use a multi-tap complementary metal-oxide-semiconductor (CMOS) image sensor to capture the input images required for the proposed photometric stereo method. This image sensor can divide the electrons from the photodiode from a single pixel into the different taps of the exposures and can thus capture multiple images under different lighting conditions with almost identical timing. We implemented a camera lighting system and created a software application to enable estimation of the normal map in real time. We also evaluated the accuracy of the estimated surface normals and demonstrated that our proposed method can estimate the surface normals of dynamic scenes.

Hazard Function Estimation with Cause-of-Death Data Missing at Random

PubMed Central

Wang, Qihua; Dinse, Gregg E.; Liu, Chunling

2010-01-01

Hazard function estimation is an important part of survival analysis. Interest often centers on estimating the hazard function associated with a particular cause of death. We propose three nonparametric kernel estimators for the hazard function, all of which are appropriate when death times are subject to random censorship and censoring indicators can be missing at random. Specifically, we present a regression surrogate estimator, an imputation estimator, and an inverse probability weighted estimator. All three estimators are uniformly strongly consistent and asymptotically normal. We derive asymptotic representations of the mean squared error and the mean integrated squared error for these estimators and we discuss a data-driven bandwidth selection method. A simulation study, conducted to assess finite sample behavior, demonstrates that the proposed hazard estimators perform relatively well. We illustrate our methods with an analysis of some vascular disease data. PMID:22267874

Accurately estimating PSF with straight lines detected by Hough transform

NASA Astrophysics Data System (ADS)

Wang, Ruichen; Xu, Liangpeng; Fan, Chunxiao; Li, Yong

2018-04-01

This paper presents an approach to estimating point spread function (PSF) from low resolution (LR) images. Existing techniques usually rely on accurate detection of ending points of the profile normal to edges. In practice however, it is often a great challenge to accurately localize profiles of edges from a LR image, which hence leads to a poor PSF estimation of the lens taking the LR image. For precisely estimating the PSF, this paper proposes firstly estimating a 1-D PSF kernel with straight lines, and then robustly obtaining the 2-D PSF from the 1-D kernel by least squares techniques and random sample consensus. Canny operator is applied to the LR image for obtaining edges and then Hough transform is utilized to extract straight lines of all orientations. Estimating 1-D PSF kernel with straight lines effectively alleviates the influence of the inaccurate edge detection on PSF estimation. The proposed method is investigated on both natural and synthetic images for estimating PSF. Experimental results show that the proposed method outperforms the state-ofthe- art and does not rely on accurate edge detection.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation.

PubMed

Vargas-Meléndez, Leandro; Boada, Beatriz L; Boada, María Jesús L; Gauchía, Antonio; Díaz, Vicente

2016-08-31

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a "pseudo-roll angle" through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors' estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation

PubMed Central

Vargas-Meléndez, Leandro; Boada, Beatriz L.; Boada, María Jesús L.; Gauchía, Antonio; Díaz, Vicente

2016-01-01

This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator. PMID:27589763

Generalized Structured Component Analysis

ERIC Educational Resources Information Center

Hwang, Heungsun; Takane, Yoshio

2004-01-01

We propose an alternative method to partial least squares for path analysis with components, called generalized structured component analysis. The proposed method replaces factors by exact linear combinations of observed variables. It employs a well-defined least squares criterion to estimate model parameters. As a result, the proposed method…

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.

Efficient calibration for imperfect computer models

DOE PAGES

Tuo, Rui; Wu, C. F. Jeff

2015-12-01

Many computer models contain unknown parameters which need to be estimated using physical observations. Furthermore, the calibration method based on Gaussian process models may lead to unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L 2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Here, numerical examples show that the proposed method outperforms the existing ones.

Methodical approaches to value assessment and determination of the capitalization level of high-rise construction

NASA Astrophysics Data System (ADS)

Smirnov, Vitaly; Dashkov, Leonid; Gorshkov, Roman; Burova, Olga; Romanova, Alina

2018-03-01

The article presents the analysis of the methodological approaches to cost estimation and determination of the capitalization level of high-rise construction objects. Factors determining the value of real estate were considered, three main approaches for estimating the value of real estate objects are given. The main methods of capitalization estimation were analyzed, the most reasonable method for determining the level of capitalization of high-rise buildings was proposed. In order to increase the value of real estate objects, the author proposes measures that enable to increase significantly the capitalization of the enterprise through more efficient use of intangible assets and goodwill.

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.

Ultrasound coefficient of nonlinearity imaging.

PubMed

van Sloun, Ruud; Demi, Libertario; Shan, Caifeng; Mischi, Massimo

2015-07-01

Imaging the acoustical coefficient of nonlinearity, β, is of interest in several healthcare interventional applications. It is an important feature that can be used for discriminating tissues. In this paper, we propose a nonlinearity characterization method with the goal of locally estimating the coefficient of nonlinearity. The proposed method is based on a 1-D solution of the nonlinear lossy Westerfelt equation, thereby deriving a local relation between β and the pressure wave field. Based on several assumptions, a β imaging method is then presented that is based on the ratio between the harmonic and fundamental fields, thereby reducing the effect of spatial amplitude variations of the speckle pattern. By testing the method on simulated ultrasound pressure fields and an in vitro B-mode ultrasound acquisition, we show that the designed algorithm is able to estimate the coefficient of nonlinearity, and that the tissue types of interest are well discriminable. The proposed imaging method provides a new approach to β estimation, not requiring a special measurement setup or transducer, that seems particularly promising for in vivo imaging.

Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection.

PubMed

Wang, Kai; Zhang, Xianmin; Ota, Jun; Huang, Yanjiang

2018-02-24

This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods.

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions.

PubMed

Peressutti, Devis; Penney, Graeme P; Housden, R James; Kolbitsch, Christoph; Gomez, Alberto; Rijkhorst, Erik-Jan; Barratt, Dean C; Rhode, Kawal S; King, Andrew P

2013-05-01

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method. Copyright © 2013 Elsevier B.V. All rights reserved.

Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

NASA Astrophysics Data System (ADS)

Nam, Hyeong-Min; Park, Chun-Su; Jung, Seung-Won; Ko, Sung-Jea

Currently deployed mobile networks including High Speed Downlink Packet Access (HSDPA) offer only best-effort Quality of Service (QoS). In wireless best effort networks, the bandwidth variation is a critical problem, especially, for mobile devices with small buffers. This is because the bandwidth variation leads to packet losses caused by buffer overflow as well as picture freezing due to high transmission delay or buffer underflow. In this paper, in order to provide seamless video streaming over HSDPA, we propose an efficient real-time video streaming method that consists of the available bandwidth (AB) estimation for the HSDPA network and the transmission rate control to prevent buffer overflows/underflows. In the proposed method, the client estimates the AB and the estimated AB is fed back to the server through real-time transport control protocol (RTCP) packets. Then, the server adaptively adjusts the transmission rate according to the estimated AB and the buffer state obtained from the RTCP feedback information. Experimental results show that the proposed method achieves seamless video streaming over the HSDPA network providing higher video quality and lower transmission delay.

Estimation and prediction under local volatility jump-diffusion model

NASA Astrophysics Data System (ADS)

Kim, Namhyoung; Lee, Younhee

2018-02-01

Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

Extending birthday paradox theory to estimate the number of tags in RFID systems.

PubMed

Shakiba, Masoud; Singh, Mandeep Jit; Sundararajan, Elankovan; Zavvari, Azam; Islam, Mohammad Tariqul

2014-01-01

The main objective of Radio Frequency Identification systems is to provide fast identification for tagged objects. However, there is always a chance of collision, when tags transmit their data to the reader simultaneously. Collision is a time-consuming event that reduces the performance of RFID systems. Consequently, several anti-collision algorithms have been proposed in the literature. Dynamic Framed Slotted ALOHA (DFSA) is one of the most popular of these algorithms. DFSA dynamically modifies the frame size based on the number of tags. Since the real number of tags is unknown, it needs to be estimated. Therefore, an accurate tag estimation method has an important role in increasing the efficiency and overall performance of the tag identification process. In this paper, we propose a novel estimation technique for DFSA anti-collision algorithms that applies birthday paradox theory to estimate the number of tags accurately. The analytical discussion and simulation results prove that the proposed method increases the accuracy of tag estimation and, consequently, outperforms previous schemes.

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.

Extending Birthday Paradox Theory to Estimate the Number of Tags in RFID Systems

PubMed Central

Shakiba, Masoud; Singh, Mandeep Jit; Sundararajan, Elankovan; Zavvari, Azam; Islam, Mohammad Tariqul

2014-01-01

The main objective of Radio Frequency Identification systems is to provide fast identification for tagged objects. However, there is always a chance of collision, when tags transmit their data to the reader simultaneously. Collision is a time-consuming event that reduces the performance of RFID systems. Consequently, several anti-collision algorithms have been proposed in the literature. Dynamic Framed Slotted ALOHA (DFSA) is one of the most popular of these algorithms. DFSA dynamically modifies the frame size based on the number of tags. Since the real number of tags is unknown, it needs to be estimated. Therefore, an accurate tag estimation method has an important role in increasing the efficiency and overall performance of the tag identification process. In this paper, we propose a novel estimation technique for DFSA anti-collision algorithms that applies birthday paradox theory to estimate the number of tags accurately. The analytical discussion and simulation results prove that the proposed method increases the accuracy of tag estimation and, consequently, outperforms previous schemes. PMID:24752285

Estimation of Gravity Parameters Related to Simple Geometrical Structures by Developing an Approach Based on Deconvolution and Linear Optimization Techniques

NASA Astrophysics Data System (ADS)

Asfahani, J.; Tlas, M.

2015-10-01

An easy and practical method for interpreting residual gravity anomalies due to simple geometrically shaped models such as cylinders and spheres has been proposed in this paper. This proposed method is based on both the deconvolution technique and the simplex algorithm for linear optimization to most effectively estimate the model parameters, e.g., the depth from the surface to the center of a buried structure (sphere or horizontal cylinder) or the depth from the surface to the top of a buried object (vertical cylinder), and the amplitude coefficient from the residual gravity anomaly profile. The method was tested on synthetic data sets corrupted by different white Gaussian random noise levels to demonstrate the capability and reliability of the method. The results acquired show that the estimated parameter values derived by this proposed method are close to the assumed true parameter values. The validity of this method is also demonstrated using real field residual gravity anomalies from Cuba and Sweden. Comparable and acceptable agreement is shown between the results derived by this method and those derived from real field data.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

On-line estimation and compensation of measurement delay in GPS/SINS integration

NASA Astrophysics Data System (ADS)

Yang, Tao; Wang, Wei

2008-10-01

The chief aim of this paper is to propose a simple on-line estimation and compensation method of GPS/SINS measurement delay. The causes of time delay for GPS/SINS integration are analyzed in this paper. New Kalman filter state equations augmented by measurement delay and modified measurement equations are derived. Based on an open-loop Kalman filter, several simulations are run, results of which show that by the proposed method, the estimation and compensation error of measurement delay is below 0.1s.

A new DOD and DOA estimation method for MIMO radar

NASA Astrophysics Data System (ADS)

Gong, Jian; Lou, Shuntian; Guo, Yiduo

2018-04-01

The battlefield electromagnetic environment is becoming more and more complex, and MIMO radar will inevitably be affected by coherent and non-stationary noise. To solve this problem, an angle estimation method based on oblique projection operator and Teoplitz matrix reconstruction is proposed. Through the reconstruction of Toeplitz, nonstationary noise is transformed into Gauss white noise, and then the oblique projection operator is used to separate independent and correlated sources. Finally, simulations are carried out to verify the performance of the proposed algorithm in terms of angle estimation performance and source overload.

Toward unbiased estimations of the statefinder parameters

NASA Astrophysics Data System (ADS)

Aviles, Alejandro; Klapp, Jaime; Luongo, Orlando

2017-09-01

With the use of simulated supernova catalogs, we show that the statefinder parameters turn out to be poorly and biased estimated by standard cosmography. To this end, we compute their standard deviations and several bias statistics on cosmologies near the concordance model, demonstrating that these are very large, making standard cosmography unsuitable for future and wider compilations of data. To overcome this issue, we propose a new method that consists in introducing the series of the Hubble function into the luminosity distance, instead of considering the usual direct Taylor expansions of the luminosity distance. Moreover, in order to speed up the numerical computations, we estimate the coefficients of our expansions in a hierarchical manner, in which the order of the expansion depends on the redshift of every single piece of data. In addition, we propose two hybrids methods that incorporates standard cosmography at low redshifts. The methods presented here perform better than the standard approach of cosmography both in the errors and bias of the estimated statefinders. We further propose a one-parameter diagnostic to reject non-viable methods in cosmography.

Estimation of color modification in digital images by CFA pattern change.

PubMed

Choi, Chang-Hee; Lee, Hae-Yeoun; Lee, Heung-Kyu

2013-03-10

Extensive studies have been carried out for detecting image forgery such as copy-move, re-sampling, blurring, and contrast enhancement. Although color modification is a common forgery technique, there is no reported forensic method for detecting this type of manipulation. In this paper, we propose a novel algorithm for estimating color modification in images acquired from digital cameras when the images are modified. Most commercial digital cameras are equipped with a color filter array (CFA) for acquiring the color information of each pixel. As a result, the images acquired from such digital cameras include a trace from the CFA pattern. This pattern is composed of the basic red green blue (RGB) colors, and it is changed when color modification is carried out on the image. We designed an advanced intermediate value counting method for measuring the change in the CFA pattern and estimating the extent of color modification. The proposed method is verified experimentally by using 10,366 test images. The results confirmed the ability of the proposed method to estimate color modification with high accuracy. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Titrimetric and photometric methods for determination of hypochlorite in commercial bleaches.

PubMed

Jonnalagadda, Sreekanth B; Gengan, Prabhashini

2010-01-01

Two methods, simple titration and photometric methods for determination of hypochlorite are developed, based its reaction with hydrogen peroxide and titration of the residual peroxide by acidic permanganate. In the titration method, the residual hydrogen peroxide is estimated by titration with standard permanganate solution to estimate the hypochlorite concentration. The photometric method is devised to measure the concentration of remaining permanganate, after the reaction with residual hydrogen peroxide. It employs 4 ranges of calibration curves to enable the determination of hypochlorite accurately. The new photometric method measures hypochlorite in the range 1.90 x 10(-3) to 1.90 x 10(-2) M, with high accuracy and with low variance. The concentrations of hypochlorite in diverse commercial bleach samples and in seawater which is enriched with hypochlorite were estimated using the proposed method and compared with the arsenite method. The statistical analysis validates the superiority of the proposed method.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

PubMed Central

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-01-01

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison. PMID:29614028

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

PubMed

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-04-03

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

Empirical Bayes Gaussian likelihood estimation of exposure distributions from pooled samples in human biomonitoring.

PubMed

Li, Xiang; Kuk, Anthony Y C; Xu, Jinfeng

2014-12-10

Human biomonitoring of exposure to environmental chemicals is important. Individual monitoring is not viable because of low individual exposure level or insufficient volume of materials and the prohibitive cost of taking measurements from many subjects. Pooling of samples is an efficient and cost-effective way to collect data. Estimation is, however, complicated as individual values within each pool are not observed but are only known up to their average or weighted average. The distribution of such averages is intractable when the individual measurements are lognormally distributed, which is a common assumption. We propose to replace the intractable distribution of the pool averages by a Gaussian likelihood to obtain parameter estimates. If the pool size is large, this method produces statistically efficient estimates, but regardless of pool size, the method yields consistent estimates as the number of pools increases. An empirical Bayes (EB) Gaussian likelihood approach, as well as its Bayesian analog, is developed to pool information from various demographic groups by using a mixed-effect formulation. We also discuss methods to estimate the underlying mean-variance relationship and to select a good model for the means, which can be incorporated into the proposed EB or Bayes framework. By borrowing strength across groups, the EB estimator is more efficient than the individual group-specific estimator. Simulation results show that the EB Gaussian likelihood estimates outperform a previous method proposed for the National Health and Nutrition Examination Surveys with much smaller bias and better coverage in interval estimation, especially after correction of bias. Copyright © 2014 John Wiley & Sons, Ltd.

Predicting individual brain functional connectivity using a Bayesian hierarchical model.

PubMed

Dai, Tian; Guo, Ying

2017-02-15

Network-oriented analysis of functional magnetic resonance imaging (fMRI), especially resting-state fMRI, has revealed important association between abnormal connectivity and brain disorders such as schizophrenia, major depression and Alzheimer's disease. Imaging-based brain connectivity measures have become a useful tool for investigating the pathophysiology, progression and treatment response of psychiatric disorders and neurodegenerative diseases. Recent studies have started to explore the possibility of using functional neuroimaging to help predict disease progression and guide treatment selection for individual patients. These studies provide the impetus to develop statistical methodology that would help provide predictive information on disease progression-related or treatment-related changes in neural connectivity. To this end, we propose a prediction method based on Bayesian hierarchical model that uses individual's baseline fMRI scans, coupled with relevant subject characteristics, to predict the individual's future functional connectivity. A key advantage of the proposed method is that it can improve the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns that are common to subjects with similar characteristics as well as individual-level connectivity features that are particular to the specific subject. Furthermore, our method also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes. The proposed prediction method could be a useful approach to predict the changes in individual patient's brain connectivity with the progression of a disease. It can also be used to predict a patient's post-treatment brain connectivity after a specified treatment regimen. Another utility of the proposed method is that it can be applied to test-retest imaging data to develop a more reliable estimator for individual functional connectivity. We show there exists a nice connection between our proposed estimator and a recently developed shrinkage estimator of connectivity measures in the neuroimaging community. We develop an expectation-maximization (EM) algorithm for estimation of the proposed Bayesian hierarchical model. Simulations studies are performed to evaluate the accuracy of our proposed prediction methods. We illustrate the application of the methods with two data examples: the longitudinal resting-state fMRI from ADNI2 study and the test-retest fMRI data from Kirby21 study. In both the simulation studies and the fMRI data applications, we demonstrate that the proposed methods provide more accurate prediction and more reliable estimation of individual functional connectivity as compared with alternative methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Small area estimation for semicontinuous data.

PubMed

Chandra, Hukum; Chambers, Ray

2016-03-01

Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linear mixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Inferring Interaction Force from Visual Information without Using Physical Force Sensors.

PubMed

Hwang, Wonjun; Lim, Soo-Chul

2017-10-26

In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.

Estimating and Identifying Unspecified Correlation Structure for Longitudinal Data

PubMed Central

Hu, Jianhua; Wang, Peng; Qu, Annie

2014-01-01

Identifying correlation structure is important to achieving estimation efficiency in analyzing longitudinal data, and is also crucial for drawing valid statistical inference for large size clustered data. In this paper, we propose a nonparametric method to estimate the correlation structure, which is applicable for discrete longitudinal data. We utilize eigenvector-based basis matrices to approximate the inverse of the empirical correlation matrix and determine the number of basis matrices via model selection. A penalized objective function based on the difference between the empirical and model approximation of the correlation matrices is adopted to select an informative structure for the correlation matrix. The eigenvector representation of the correlation estimation is capable of reducing the risk of model misspecification, and also provides useful information on the specific within-cluster correlation pattern of the data. We show that the proposed method possesses the oracle property and selects the true correlation structure consistently. The proposed method is illustrated through simulations and two data examples on air pollution and sonar signal studies. PMID:26361433

Estimation of the auto frequency response function at unexcited points using dummy masses

NASA Astrophysics Data System (ADS)

Hosoya, Naoki; Yaginuma, Shinji; Onodera, Hiroshi; Yoshimura, Takuya

2015-02-01

If structures with complex shapes have space limitations, vibration tests using an exciter or impact hammer for the excitation are difficult. Although measuring the auto frequency response function at an unexcited point may not be practical via a vibration test, it can be obtained by assuming that the inertia acting on a dummy mass is an external force on the target structure upon exciting a different excitation point. We propose a method to estimate the auto frequency response functions at unexcited points by attaching a small mass (dummy mass), which is comparable to the accelerometer mass. The validity of the proposed method is demonstrated by comparing the auto frequency response functions estimated at unexcited points in a beam structure to those obtained from numerical simulations. We also consider random measurement errors by finite element analysis and vibration tests, but not bias errors. Additionally, the applicability of the proposed method is demonstrated by applying it to estimate the auto frequency response function of the lower arm in a car suspension.

A frame selective dynamic programming approach for noise robust pitch estimation.

PubMed

Yarra, Chiranjeevi; Deshmukh, Om D; Ghosh, Prasanta Kumar

2018-04-01

The principles of the existing pitch estimation techniques are often different and complementary in nature. In this work, a frame selective dynamic programming (FSDP) method is proposed which exploits the complementary characteristics of two existing methods, namely, sub-harmonic to harmonic ratio (SHR) and sawtooth-wave inspired pitch estimator (SWIPE). Using variants of SHR and SWIPE, the proposed FSDP method classifies all the voiced frames into two classes-the first class consists of the frames where a confidence score maximization criterion is used for pitch estimation, while for the second class, a dynamic programming (DP) based approach is proposed. Experiments are performed on speech signals separately from KEELE, CSLU, and PaulBaghsaw corpora under clean and additive white Gaussian noise at 20, 10, 5, and 0 dB SNR conditions using four baseline schemes including SHR, SWIPE, and two DP based techniques. The pitch estimation performance of FSDP, when averaged over all SNRs, is found to be better than those of the baseline schemes suggesting the benefit of applying smoothness constraint using DP in selected frames in the proposed FSDP scheme. The VuV classification error from FSDP is also found to be lower than that from all four baseline schemes in almost all SNR conditions on three corpora.

Regularized Semiparametric Estimation for Ordinary Differential Equations

PubMed Central

Li, Yun; Zhu, Ji; Wang, Naisyin

2015-01-01

Ordinary differential equations (ODEs) are widely used in modeling dynamic systems and have ample applications in the fields of physics, engineering, economics and biological sciences. The ODE parameters often possess physiological meanings and can help scientists gain better understanding of the system. One key interest is thus to well estimate these parameters. Ideally, constant parameters are preferred due to their easy interpretation. In reality, however, constant parameters can be too restrictive such that even after incorporating error terms, there could still be unknown sources of disturbance that lead to poor agreement between observed data and the estimated ODE system. In this paper, we address this issue and accommodate short-term interferences by allowing parameters to vary with time. We propose a new regularized estimation procedure on the time-varying parameters of an ODE system so that these parameters could change with time during transitions but remain constants within stable stages. We found, through simulation studies, that the proposed method performs well and tends to have less variation in comparison to the non-regularized approach. On the theoretical front, we derive finite-sample estimation error bounds for the proposed method. Applications of the proposed method to modeling the hare-lynx relationship and the measles incidence dynamic in Ontario, Canada lead to satisfactory and meaningful results. PMID:26392639

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach

PubMed Central

Kang, Le; Chen, Weijie; Petrick, Nicholas A.; Gallas, Brandon D.

2014-01-01

The area under the receiver operating characteristic (ROC) curve (AUC) is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of AUC, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. PMID:25399736

Generating Health Estimates by Zip Code: A Semiparametric Small Area Estimation Approach Using the California Health Interview Survey.

PubMed

Wang, Yueyan; Ponce, Ninez A; Wang, Pan; Opsomer, Jean D; Yu, Hongjian

2015-12-01

We propose a method to meet challenges in generating health estimates for granular geographic areas in which the survey sample size is extremely small. Our generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model's feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, we demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for Zip Code Tabulation Areas (ZCTAs). Our method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California. For some areas with great socioeconomic diversity across adjacent neighborhoods, such as Los Angeles County, the modeled uninsured estimates revealed much heterogeneity among geographically adjacent ZCTAs. The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.

A note on the kappa statistic for clustered dichotomous data.

PubMed

Zhou, Ming; Yang, Zhao

2014-06-30

The kappa statistic is widely used to assess the agreement between two raters. Motivated by a simulation-based cluster bootstrap method to calculate the variance of the kappa statistic for clustered physician-patients dichotomous data, we investigate its special correlation structure and develop a new simple and efficient data generation algorithm. For the clustered physician-patients dichotomous data, based on the delta method and its special covariance structure, we propose a semi-parametric variance estimator for the kappa statistic. An extensive Monte Carlo simulation study is performed to evaluate the performance of the new proposal and five existing methods with respect to the empirical coverage probability, root-mean-square error, and average width of the 95% confidence interval for the kappa statistic. The variance estimator ignoring the dependence within a cluster is generally inappropriate, and the variance estimators from the new proposal, bootstrap-based methods, and the sampling-based delta method perform reasonably well for at least a moderately large number of clusters (e.g., the number of clusters K ⩾50). The new proposal and sampling-based delta method provide convenient tools for efficient computations and non-simulation-based alternatives to the existing bootstrap-based methods. Moreover, the new proposal has acceptable performance even when the number of clusters is as small as K = 25. To illustrate the practical application of all the methods, one psychiatric research data and two simulated clustered physician-patients dichotomous data are analyzed. Copyright © 2014 John Wiley & Sons, Ltd.

Nonparametric estimation and testing of fixed effects panel data models

PubMed Central

Henderson, Daniel J.; Carroll, Raymond J.; Li, Qi

2009-01-01

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics. PMID:19444335

Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

Human Body 3D Posture Estimation Using Significant Points and Two Cameras

PubMed Central

Juang, Chia-Feng; Chen, Teng-Chang; Du, Wei-Chin

2014-01-01

This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures. PMID:24883422

Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

PubMed Central

Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington

2014-01-01

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively. PMID:25207868

Motion field estimation for a dynamic scene using a 3D LiDAR.

PubMed

Li, Qingquan; Zhang, Liang; Mao, Qingzhou; Zou, Qin; Zhang, Pin; Feng, Shaojun; Ochieng, Washington

2014-09-09

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

Multiscale spatial and temporal estimation of the b-value

NASA Astrophysics Data System (ADS)

García-Hernández, R.; D'Auria, L.; Barrancos, J.; Padilla, G.

2017-12-01

The estimation of the spatial and temporal variations of the Gutenberg-Richter b-value is of great importance in different seismological applications. One of the problems affecting its estimation is the heterogeneous distribution of the seismicity which makes its estimate strongly dependent upon the selected spatial and/or temporal scale. This is especially important in volcanoes where dense clusters of earthquakes often overlap the background seismicity. Proposed solutions for estimating temporal variations of the b-value include considering equally spaced time intervals or variable intervals having an equal number of earthquakes. Similar approaches have been proposed to image the spatial variations of this parameter as well.We propose a novel multiscale approach, based on the method of Ogata and Katsura (1993), allowing a consistent estimation of the b-value regardless of the considered spatial and/or temporal scales. Our method, named MUST-B (MUltiscale Spatial and Temporal characterization of the B-value), basically consists in computing estimates of the b-value at multiple temporal and spatial scales, extracting for a give spatio-temporal point a statistical estimator of the value, as well as and indication of the characteristic spatio-temporal scale. This approach includes also a consistent estimation of the completeness magnitude (Mc) and of the uncertainties over both b and Mc.We applied this method to example datasets for volcanic (Tenerife, El Hierro) and tectonic areas (Central Italy) as well as an example application at global scale.

Estimation of a partially linear additive model for data from an outcome-dependent sampling design with a continuous outcome

PubMed Central

Tan, Ziwen; Qin, Guoyou; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme. We propose the estimation method and establish the asymptotic properties of the proposed estimator. Simulation studies are conducted to show the improved efficiency of the proposed ODS estimator for PLAM compared with that from a traditional simple random sampling design with the same sample size. The data of the above-mentioned study is analyzed to illustrate the proposed method. PMID:27006375

Interference correction by extracting the information of interference dominant regions: Application to near-infrared spectra

NASA Astrophysics Data System (ADS)

Bi, Yiming; Tang, Liang; Shan, Peng; Xie, Qiong; Hu, Yong; Peng, Silong; Tan, Jie; Li, Changwen

2014-08-01

Interference such as baseline drift and light scattering can degrade the model predictability in multivariate analysis of near-infrared (NIR) spectra. Usually interference can be represented by an additive and a multiplicative factor. In order to eliminate these interferences, correction parameters are needed to be estimated from spectra. However, the spectra are often mixed of physical light scattering effects and chemical light absorbance effects, making it difficult for parameter estimation. Herein, a novel algorithm was proposed to find a spectral region automatically that the interesting chemical absorbance and noise are low, that is, finding an interference dominant region (IDR). Based on the definition of IDR, a two-step method was proposed to find the optimal IDR and the corresponding correction parameters estimated from IDR. Finally, the correction was performed to the full spectral range using previously obtained parameters for the calibration set and test set, respectively. The method can be applied to multi target systems with one IDR suitable for all targeted analytes. Tested on two benchmark data sets of near-infrared spectra, the performance of the proposed method provided considerable improvement compared with full spectral estimation methods and comparable with other state-of-art methods.

Proportional hazards model with varying coefficients for length-biased data.

PubMed

Zhang, Feipeng; Chen, Xuerong; Zhou, Yong

2014-01-01

Length-biased data arise in many important applications including epidemiological cohort studies, cancer prevention trials and studies of labor economics. Such data are also often subject to right censoring due to loss of follow-up or the end of study. In this paper, we consider a proportional hazards model with varying coefficients for right-censored and length-biased data, which is used to study the interact effect nonlinearly of covariates with an exposure variable. A local estimating equation method is proposed for the unknown coefficients and the intercept function in the model. The asymptotic properties of the proposed estimators are established by using the martingale theory and kernel smoothing techniques. Our simulation studies demonstrate that the proposed estimators have an excellent finite-sample performance. The Channing House data is analyzed to demonstrate the applications of the proposed method.

Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach

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

Ren, Shangjie; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California; Hara, Wendy

Purpose: To develop a reliable method to estimate electron density based on anatomic magnetic resonance imaging (MRI) of the brain. Methods and Materials: We proposed a unifying multi-atlas approach for electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas was constructed through a voxelwise matching process using multiple atlases, with the goal of mitigating effects of inherent anatomic variations between patients. Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron density given its image intensity on T1- and T2-weighted MR images; and (2) electron density given its spatial location in a referencemore » anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed. Results: The proposed method significantly reduced the errors in electron density estimation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144 (P<.0001) using conventional T1-weighted intensity and geometry-based approaches, respectively. For detection of bony anatomy, the proposed method achieved an 89% area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which improved upon intensity and geometry-based approaches (area under the curve: 79% and 80%, respectively). Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning.« less

Modeling and quantification of repolarization feature dependency on heart rate.

PubMed

Minchole, A; Zacur, E; Pueyo, E; Laguna, P

2014-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". This work aims at providing an efficient method to estimate the parameters of a non linear model including memory, previously proposed to characterize rate adaptation of repolarization indices. The physiological restrictions on the model parameters have been included in the cost function in such a way that unconstrained optimization techniques such as descent optimization methods can be used for parameter estimation. The proposed method has been evaluated on electrocardiogram (ECG) recordings of healthy subjects performing a tilt test, where rate adaptation of QT and Tpeak-to-Tend (Tpe) intervals has been characterized. The proposed strategy results in an efficient methodology to characterize rate adaptation of repolarization features, improving the convergence time with respect to previous strategies. Moreover, Tpe interval adapts faster to changes in heart rate than the QT interval. In this work an efficient estimation of the parameters of a model aimed at characterizing rate adaptation of repolarization features has been proposed. The Tpe interval has been shown to be rate related and with a shorter memory lag than the QT interval.

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.

Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics

NASA Astrophysics Data System (ADS)

Li, C. James; Lee, Hyungdae

2005-07-01

This paper presents a model-based method that predicts remaining useful life of a gear with a fatigue crack. The method consists of an embedded model to identify gear meshing stiffness from measured gear torsional vibration, an inverse method to estimate crack size from the estimated meshing stiffness; a gear dynamic model to simulate gear meshing dynamics and determine the dynamic load on the cracked tooth; and a fast crack propagation model to forecast the remaining useful life based on the estimated crack size and dynamic load. The fast crack propagation model was established to avoid repeated calculations of FEM and facilitate field deployment of the proposed method. Experimental studies were conducted to validate and demonstrate the feasibility of the proposed method for prognosis of a cracked gear.

Experimental Quasi-Microwave Whole-Body Averaged SAR Estimation Method Using Cylindrical-External Field Scanning

NASA Astrophysics Data System (ADS)

Kawamura, Yoshifumi; Hikage, Takashi; Nojima, Toshio

The aim of this study is to develop a new whole-body averaged specific absorption rate (SAR) estimation method based on the external-cylindrical field scanning technique. This technique is adopted with the goal of simplifying the dosimetry estimation of human phantoms that have different postures or sizes. An experimental scaled model system is constructed. In order to examine the validity of the proposed method for realistic human models, we discuss the pros and cons of measurements and numerical analyses based on the finite-difference time-domain (FDTD) method. We consider the anatomical European human phantoms and plane-wave in the 2GHz mobile phone frequency band. The measured whole-body averaged SAR results obtained by the proposed method are compared with the results of the FDTD analyses.

Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement.

PubMed

Roy, Venkat; Simonetto, Andrea; Leus, Geert

2018-06-01

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method.

A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix.

PubMed

Hu, Zongliang; Dong, Kai; Dai, Wenlin; Tong, Tiejun

2017-09-21

The determinant of the covariance matrix for high-dimensional data plays an important role in statistical inference and decision. It has many real applications including statistical tests and information theory. Due to the statistical and computational challenges with high dimensionality, little work has been proposed in the literature for estimating the determinant of high-dimensional covariance matrix. In this paper, we estimate the determinant of the covariance matrix using some recent proposals for estimating high-dimensional covariance matrix. Specifically, we consider a total of eight covariance matrix estimation methods for comparison. Through extensive simulation studies, we explore and summarize some interesting comparison results among all compared methods. We also provide practical guidelines based on the sample size, the dimension, and the correlation of the data set for estimating the determinant of high-dimensional covariance matrix. Finally, from a perspective of the loss function, the comparison study in this paper may also serve as a proxy to assess the performance of the covariance matrix estimation.

[Nonparametric method of estimating survival functions containing right-censored and interval-censored data].

PubMed

Xu, Yonghong; Gao, Xiaohuan; Wang, Zhengxi

2014-04-01

Missing data represent a general problem in many scientific fields, especially in medical survival analysis. Dealing with censored data, interpolation method is one of important methods. However, most of the interpolation methods replace the censored data with the exact data, which will distort the real distribution of the censored data and reduce the probability of the real data falling into the interpolation data. In order to solve this problem, we in this paper propose a nonparametric method of estimating the survival function of right-censored and interval-censored data and compare its performance to SC (self-consistent) algorithm. Comparing to the average interpolation and the nearest neighbor interpolation method, the proposed method in this paper replaces the right-censored data with the interval-censored data, and greatly improves the probability of the real data falling into imputation interval. Then it bases on the empirical distribution theory to estimate the survival function of right-censored and interval-censored data. The results of numerical examples and a real breast cancer data set demonstrated that the proposed method had higher accuracy and better robustness for the different proportion of the censored data. This paper provides a good method to compare the clinical treatments performance with estimation of the survival data of the patients. This pro vides some help to the medical survival data analysis.

Joint Bearing and Range Estimation of Multiple Objects from Time-Frequency Analysis.

PubMed

Liu, Jeng-Cheng; Cheng, Yuang-Tung; Hung, Hsien-Sen

2018-01-19

Direction-of-arrival (DOA) and range estimation is an important issue of sonar signal processing. In this paper, a novel approach using Hilbert-Huang transform (HHT) is proposed for joint bearing and range estimation of multiple targets based on a uniform linear array (ULA) of hydrophones. The structure of this ULA based on micro-electro-mechanical systems (MEMS) technology, and thus has attractive features of small size, high sensitivity and low cost, and is suitable for Autonomous Underwater Vehicle (AUV) operations. This proposed target localization method has the following advantages: only a single snapshot of data is needed and real-time processing is feasible. The proposed algorithm transforms a very complicated nonlinear estimation problem to a simple nearly linear one via time-frequency distribution (TFD) theory and is verified with HHT. Theoretical discussions of resolution issue are also provided to facilitate the design of a MEMS sensor with high sensitivity. Simulation results are shown to verify the effectiveness of the proposed method.

Functional Linear Model with Zero-value Coefficient Function at Sub-regions.

PubMed

Zhou, Jianhui; Wang, Nae-Yuh; Wang, Naisyin

2013-01-01

We propose a shrinkage method to estimate the coefficient function in a functional linear regression model when the value of the coefficient function is zero within certain sub-regions. Besides identifying the null region in which the coefficient function is zero, we also aim to perform estimation and inferences for the nonparametrically estimated coefficient function without over-shrinking the values. Our proposal consists of two stages. In stage one, the Dantzig selector is employed to provide initial location of the null region. In stage two, we propose a group SCAD approach to refine the estimated location of the null region and to provide the estimation and inference procedures for the coefficient function. Our considerations have certain advantages in this functional setup. One goal is to reduce the number of parameters employed in the model. With a one-stage procedure, it is needed to use a large number of knots in order to precisely identify the zero-coefficient region; however, the variation and estimation difficulties increase with the number of parameters. Owing to the additional refinement stage, we avoid this necessity and our estimator achieves superior numerical performance in practice. We show that our estimator enjoys the Oracle property; it identifies the null region with probability tending to 1, and it achieves the same asymptotic normality for the estimated coefficient function on the non-null region as the functional linear model estimator when the non-null region is known. Numerically, our refined estimator overcomes the shortcomings of the initial Dantzig estimator which tends to under-estimate the absolute scale of non-zero coefficients. The performance of the proposed method is illustrated in simulation studies. We apply the method in an analysis of data collected by the Johns Hopkins Precursors Study, where the primary interests are in estimating the strength of association between body mass index in midlife and the quality of life in physical functioning at old age, and in identifying the effective age ranges where such associations exist.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring.

PubMed

Xiong, Naixue; Liu, Ryan Wen; Liang, Maohan; Wu, Di; Liu, Zhao; Wu, Huisi

2017-01-18

Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System

PubMed Central

Chen, Jing; Zhou, Zixiang; Leng, Zhen; Fan, Lei

2018-01-01

The fusion of monocular visual and inertial cues has become popular in robotics, unmanned vehicles and augmented reality fields. Recent results have shown that optimization-based fusion strategies outperform filtering strategies. Robust state estimation is the core capability for optimization-based visual–inertial Simultaneous Localization and Mapping (SLAM) systems. As a result of the nonlinearity of visual–inertial systems, the performance heavily relies on the accuracy of initial values (visual scale, gravity, velocity and Inertial Measurement Unit (IMU) biases). Therefore, this paper aims to propose a more accurate initial state estimation method. On the basis of the known gravity magnitude, we propose an approach to refine the estimated gravity vector by optimizing the two-dimensional (2D) error state on its tangent space, then estimate the accelerometer bias separately, which is difficult to be distinguished under small rotation. Additionally, we propose an automatic termination criterion to determine when the initialization is successful. Once the initial state estimation converges, the initial estimated values are used to launch the nonlinear tightly coupled visual–inertial SLAM system. We have tested our approaches with the public EuRoC dataset. Experimental results show that the proposed methods can achieve good initial state estimation, the gravity refinement approach is able to efficiently speed up the convergence process of the estimated gravity vector, and the termination criterion performs well. PMID:29419751

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.

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.

A method for estimating both the solubility parameters and molar volumes of liquids

NASA Technical Reports Server (NTRS)

Fedors, R. F.

1974-01-01

Development of an indirect method of estimating the solubility parameter of high molecular weight polymers. The proposed method of estimating the solubility parameter, like Small's method, is based on group additive constants, but is believed to be superior to Small's method for two reasons: (1) the contribution of a much larger number of functional groups have been evaluated, and (2) the method requires only a knowledge of structural formula of the compound.

Development and validation of multivariate calibration methods for simultaneous estimation of Paracetamol, Enalapril maleate and hydrochlorothiazide in pharmaceutical dosage form

NASA Astrophysics Data System (ADS)

Singh, Veena D.; Daharwal, Sanjay J.

2017-01-01

Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.

Efficient multidimensional regularization for Volterra series estimation

NASA Astrophysics Data System (ADS)

Birpoutsoukis, Georgios; Csurcsia, Péter Zoltán; Schoukens, Johan

2018-05-01

This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates of linear time invariant systems. To avoid the excessive memory needs in case of long measurements or large number of estimated parameters, a practical gradient-based estimation method is also provided, leading to the same numerical results as the proposed Volterra estimation method. Moreover, the transient effects in the simulated output are removed by a special regularization method based on the novel ideas of transient removal for Linear Time-Varying (LTV) systems. Combining the proposed methodologies, the nonparametric Volterra models of the cascaded water tanks benchmark are presented in this paper. The results for different scenarios varying from a simple Finite Impulse Response (FIR) model to a 3rd degree Volterra series with and without transient removal are compared and studied. It is clear that the obtained models capture the system dynamics when tested on a validation dataset, and their performance is comparable with the white-box (physical) models.

Robust estimation of the proportion of treatment effect explained by surrogate marker information.

PubMed

Parast, Layla; McDermott, Mary M; Tian, Lu

2016-05-10

In randomized treatment studies where the primary outcome requires long follow-up of patients and/or expensive or invasive obtainment procedures, the availability of a surrogate marker that could be used to estimate the treatment effect and could potentially be observed earlier than the primary outcome would allow researchers to make conclusions regarding the treatment effect with less required follow-up time and resources. The Prentice criterion for a valid surrogate marker requires that a test for treatment effect on the surrogate marker also be a valid test for treatment effect on the primary outcome of interest. Based on this criterion, methods have been developed to define and estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker. These methods aim to identify useful statistical surrogates that capture a large proportion of the treatment effect. However, current methods to estimate this proportion usually require restrictive model assumptions that may not hold in practice and thus may lead to biased estimates of this quantity. In this paper, we propose a nonparametric procedure to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on a potential surrogate marker and extend this procedure to a setting with multiple surrogate markers. We compare our approach with previously proposed model-based approaches and propose a variance estimation procedure based on a perturbation-resampling method. Simulation studies demonstrate that the procedure performs well in finite samples and outperforms model-based procedures when the specified models are not correct. We illustrate our proposed procedure using a data set from a randomized study investigating a group-mediated cognitive behavioral intervention for peripheral artery disease participants. Copyright © 2015 John Wiley & Sons, Ltd.

Distributed least-squares estimation of a remote chemical source via convex combination in wireless sensor networks.

PubMed

Cao, Meng-Li; Meng, Qing-Hao; Zeng, Ming; Sun, Biao; Li, Wei; Ding, Cheng-Jun

2014-06-27

This paper investigates the problem of locating a continuous chemical source using the concentration measurements provided by a wireless sensor network (WSN). Such a problem exists in various applications: eliminating explosives or drugs, detecting the leakage of noxious chemicals, etc. The limited power and bandwidth of WSNs have motivated collaborative in-network processing which is the focus of this paper. We propose a novel distributed least-squares estimation (DLSE) method to solve the chemical source localization (CSL) problem using a WSN. The DLSE method is realized by iteratively conducting convex combination of the locally estimated chemical source locations in a distributed manner. Performance assessments of our method are conducted using both simulations and real experiments. In the experiments, we propose a fitting method to identify both the release rate and the eddy diffusivity. The results show that the proposed DLSE method can overcome the negative interference of local minima and saddle points of the objective function, which would hinder the convergence of local search methods, especially in the case of locating a remote chemical source.

Direct Regularized Estimation of Retinal Vascular Oxygen Tension Based on an Experimental Model

PubMed Central

Yildirim, Isa; Ansari, Rashid; Yetik, I. Samil; Shahidi, Mahnaz

2014-01-01

Phosphorescence lifetime imaging is commonly used to generate oxygen tension maps of retinal blood vessels by classical least squares (LS) estimation method. A spatial regularization method was later proposed and provided improved results. However, both methods obtain oxygen tension values from the estimates of intermediate variables, and do not yield an optimum estimate of oxygen tension values, due to their nonlinear dependence on the ratio of intermediate variables. In this paper, we provide an improved solution by devising a regularized direct least squares (RDLS) method that exploits available knowledge in studies that provide models of oxygen tension in retinal arteries and veins, unlike the earlier regularized LS approach where knowledge about intermediate variables is limited. The performance of the proposed RDLS method is evaluated by investigating and comparing the bias, variance, oxygen tension maps, 1-D profiles of arterial oxygen tension, and mean absolute error with those of earlier methods, and its superior performance both quantitatively and qualitatively is demonstrated. PMID:23732915

Nonparametric Discrete Survival Function Estimation with Uncertain Endpoints Using an Internal Validation Subsample

PubMed Central

Zee, Jarcy; Xie, Sharon X.

2015-01-01

Summary When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop an estimated likelihood function for the situation where we have both uncertain endpoints for all participants and true endpoints for only a subset of participants. We propose a nonparametric maximum estimated likelihood estimator of the discrete survival function of time to the true endpoint. We show that the proposed estimator is consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimator has little bias compared to the naïve Kaplan-Meier survival function estimator, which uses only uncertain endpoints, and more efficient with moderate missingness compared to the complete-case Kaplan-Meier survival function estimator, which uses only available true endpoints. Finally, we apply the proposed method to a dataset for estimating the risk of developing Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative. PMID:25916510

A method of bias correction for maximal reliability with dichotomous measures.

PubMed

Penev, Spiridon; Raykov, Tenko

2010-02-01

This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.

A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Kujirai, Toshihiro

2014-10-01

A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

Inference of Vohradský's Models of Genetic Networks by Solving Two-Dimensional Function Optimization Problems

PubMed Central

Kimura, Shuhei; Sato, Masanao; Okada-Hatakeyama, Mariko

2013-01-01

The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations. PMID:24386175

Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.

PubMed

Rajan, Jeny; Veraart, Jelle; Van Audekerke, Johan; Verhoye, Marleen; Sijbers, Jan

2012-12-01

Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. Copyright © 2012 Elsevier Inc. All rights reserved.

Reducing the width of confidence intervals for the difference between two population means by inverting adaptive tests.

PubMed

O'Gorman, Thomas W

2018-05-01

In the last decade, it has been shown that an adaptive testing method could be used, along with the Robbins-Monro search procedure, to obtain confidence intervals that are often narrower than traditional confidence intervals. However, these confidence interval limits require a great deal of computation and some familiarity with stochastic search methods. We propose a method for estimating the limits of confidence intervals that uses only a few tests of significance. We compare these limits to those obtained by a lengthy Robbins-Monro stochastic search and find that the proposed method is nearly as accurate as the Robbins-Monro search. Adaptive confidence intervals that are produced by the proposed method are often narrower than traditional confidence intervals when the distributions are long-tailed, skewed, or bimodal. Moreover, the proposed method of estimating confidence interval limits is easy to understand, because it is based solely on the p-values from a few tests of significance.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Simulated maximum likelihood method for estimating kinetic rates in gene expression.

PubMed

Tian, Tianhai; Xu, Songlin; Gao, Junbin; Burrage, Kevin

2007-01-01

Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment. In this paper, we develop effective methods for estimating kinetic rates in genetic regulatory networks. The simulated maximum likelihood method is used to evaluate parameters in stochastic models described by either stochastic differential equations or discrete biochemical reactions. Different types of non-parametric density functions are used to measure the transitional probability of experimental observations. For stochastic models described by biochemical reactions, we propose to use the simulated frequency distribution to evaluate the transitional density based on the discrete nature of stochastic simulations. The genetic optimization algorithm is used as an efficient tool to search for optimal reaction rates. Numerical results indicate that the proposed methods can give robust estimations of kinetic rates with good accuracy.

Nonparametric methods for analyzing recurrent gap time data with application to infections after hematopoietic cell transplant.

PubMed

Lee, Chi Hyun; Luo, Xianghua; Huang, Chiung-Yu; DeFor, Todd E; Brunstein, Claudio G; Weisdorf, Daniel J

2016-06-01

Infection is one of the most common complications after hematopoietic cell transplantation. Many patients experience infectious complications repeatedly after transplant. Existing statistical methods for recurrent gap time data typically assume that patients are enrolled due to the occurrence of an event of interest, and subsequently experience recurrent events of the same type; moreover, for one-sample estimation, the gap times between consecutive events are usually assumed to be identically distributed. Applying these methods to analyze the post-transplant infection data will inevitably lead to incorrect inferential results because the time from transplant to the first infection has a different biological meaning than the gap times between consecutive recurrent infections. Some unbiased yet inefficient methods include univariate survival analysis methods based on data from the first infection or bivariate serial event data methods based on the first and second infections. In this article, we propose a nonparametric estimator of the joint distribution of time from transplant to the first infection and the gap times between consecutive infections. The proposed estimator takes into account the potentially different distributions of the two types of gap times and better uses the recurrent infection data. Asymptotic properties of the proposed estimators are established. © 2015, The International Biometric Society.

Nonparametric methods for analyzing recurrent gap time data with application to infections after hematopoietic cell transplant

PubMed Central

Lee, Chi Hyun; Huang, Chiung-Yu; DeFor, Todd E.; Brunstein, Claudio G.; Weisdorf, Daniel J.

2015-01-01

Summary Infection is one of the most common complications after hematopoietic cell transplantation. Many patients experience infectious complications repeatedly after transplant. Existing statistical methods for recurrent gap time data typically assume that patients are enrolled due to the occurrence of an event of interest, and subsequently experience recurrent events of the same type; moreover, for one-sample estimation, the gap times between consecutive events are usually assumed to be identically distributed. Applying these methods to analyze the post-transplant infection data will inevitably lead to incorrect inferential results because the time from transplant to the first infection has a different biological meaning than the gap times between consecutive recurrent infections. Some unbiased yet inefficient methods include univariate survival analysis methods based on data from the first infection or bivariate serial event data methods based on the first and second infections. In this paper, we propose a nonparametric estimator of the joint distribution of time from transplant to the first infection and the gap times between consecutive infections. The proposed estimator takes into account the potentially different distributions of the two types of gap times and better uses the recurrent infection data. Asymptotic properties of the proposed estimators are established. PMID:26575402

Analysis of Non Local Image Denoising Methods

NASA Astrophysics Data System (ADS)

Pardo, Álvaro

Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non local denoising was introduced. The Non Local Means method proposed by Buades, Morel and Coll attracted the attention of other researches who proposed improvements and modifications to their proposal. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral graph properties. We also propose some improvements to automatically estimate the parameters used on these methods.

A new frequency approach for light flicker evaluation in electric power systems

NASA Astrophysics Data System (ADS)

Feola, Luigi; Langella, Roberto; Testa, Alfredo

2015-12-01

In this paper, a new analytical estimator for light flicker in frequency domain, which is able to take into account also the frequency components neglected by the classical methods proposed in literature, is proposed. The analytical solutions proposed apply for any generic stationary signal affected by interharmonic distortion. The light flicker analytical estimator proposed is applied to numerous numerical case studies with the goal of showing i) the correctness and the improvements of the analytical approach proposed with respect to the other methods proposed in literature and ii) the accuracy of the results compared to those obtained by means of the classical International Electrotechnical Commission (IEC) flickermeter. The usefulness of the proposed analytical approach is that it can be included in signal processing tools for interharmonic penetration studies for the integration of renewable energy sources in future smart grids.

Parameter Estimation of Partial Differential Equation Models.

PubMed

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

Functional Mixed Effects Model for Small Area Estimation.

PubMed

Maiti, Tapabrata; Sinha, Samiran; Zhong, Ping-Shou

2016-09-01

Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area level data, and fit a varying coefficient linear mixed effect model where the varying coefficients are semi-parametrically modeled via B-splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors, and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.

Center of pressure based segment inertial parameters validation

PubMed Central

Rezzoug, Nasser; Gorce, Philippe; Isableu, Brice; Venture, Gentiane

2017-01-01

By proposing efficient methods for estimating Body Segment Inertial Parameters’ (BSIP) estimation and validating them with a force plate, it is possible to improve the inverse dynamic computations that are necessary in multiple research areas. Until today a variety of studies have been conducted to improve BSIP estimation but to our knowledge a real validation has never been completely successful. In this paper, we propose a validation method using both kinematic and kinetic parameters (contact forces) gathered from optical motion capture system and a force plate respectively. To compare BSIPs, we used the measured contact forces (Force plate) as the ground truth, and reconstructed the displacements of the Center of Pressure (COP) using inverse dynamics from two different estimation techniques. Only minor differences were seen when comparing the estimated segment masses. Their influence on the COP computation however is large and the results show very distinguishable patterns of the COP movements. Improving BSIP techniques is crucial and deviation from the estimations can actually result in large errors. This method could be used as a tool to validate BSIP estimation techniques. An advantage of this approach is that it facilitates the comparison between BSIP estimation methods and more specifically it shows the accuracy of those parameters. PMID:28662090

Estimation of salient regions related to chronic gastritis using gastric X-ray images.

PubMed

Togo, Ren; Ishihara, Kenta; Ogawa, Takahiro; Haseyama, Miki

2016-10-01

Since technical knowledge and a high degree of experience are necessary for diagnosis of chronic gastritis, computer-aided diagnosis (CAD) systems that analyze gastric X-ray images are desirable in the field of medicine. Therefore, a new method that estimates salient regions related to chronic gastritis/non-gastritis for supporting diagnosis is presented in this paper. In order to estimate salient regions related to chronic gastritis/non-gastritis, the proposed method monitors the distance between a target image feature and Support Vector Machine (SVM)-based hyperplane for its classification. Furthermore, our method realizes removal of the influence of regions outside the stomach by using positional relationships between the stomach and other organs. Consequently, since the proposed method successfully estimates salient regions of gastric X-ray images for which chronic gastritis and non-gastritis are unknown, visual support for inexperienced clinicians becomes feasible. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bias correction of nutritional status estimates when reported age is used for calculating WHO indicators in children under five years of age.

PubMed

Quezada, Amado D; García-Guerra, Armando; Escobar, Leticia

2016-06-01

To assess the performance of a simple correction method for nutritional status estimates in children under five years of age when exact age is not available from the data. The proposed method was based on the assumption of symmetry of age distributions within a given month of age and validated in a large population-based survey sample of Mexican preschool children. The main distributional assumption was consistent with the data. All prevalence estimates derived from the correction method showed no statistically significant bias. In contrast, failing to correct attained age resulted in an underestimation of stunting in general and an overestimation of overweight or obesity among the youngest. The proposed method performed remarkably well in terms of bias correction of estimates and could be easily applied in situations in which either birth or interview dates are not available from the data.

Experimental evaluation of fingerprint verification system based on double random phase encoding

NASA Astrophysics Data System (ADS)

Suzuki, Hiroyuki; Yamaguchi, Masahiro; Yachida, Masuyoshi; Ohyama, Nagaaki; Tashima, Hideaki; Obi, Takashi

2006-03-01

We proposed a smart card holder authentication system that combines fingerprint verification with PIN verification by applying a double random phase encoding scheme. In this system, the probability of accurate verification of an authorized individual reduces when the fingerprint is shifted significantly. In this paper, a review of the proposed system is presented and preprocessing for improving the false rejection rate is proposed. In the proposed method, the position difference between two fingerprint images is estimated by using an optimized template for core detection. When the estimated difference exceeds the permissible level, the user inputs the fingerprint again. The effectiveness of the proposed method is confirmed by a computational experiment; its results show that the false rejection rate is improved.

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.

Fast Markerless Tracking for Augmented Reality in Planar Environment

NASA Astrophysics Data System (ADS)

Basori, Ahmad Hoirul; Afif, Fadhil Noer; Almazyad, Abdulaziz S.; AbuJabal, Hamza Ali S.; Rehman, Amjad; Alkawaz, Mohammed Hazim

2015-12-01

Markerless tracking for augmented reality should not only be accurate but also fast enough to provide a seamless synchronization between real and virtual beings. Current reported methods showed that a vision-based tracking is accurate but requires high computational power. This paper proposes a real-time hybrid-based method for tracking unknown environments in markerless augmented reality. The proposed method provides collaboration of vision-based approach with accelerometers and gyroscopes sensors as camera pose predictor. To align the augmentation relative to camera motion, the tracking method is done by substituting feature-based camera estimation with combination of inertial sensors with complementary filter to provide more dynamic response. The proposed method managed to track unknown environment with faster processing time compared to available feature-based approaches. Moreover, the proposed method can sustain its estimation in a situation where feature-based tracking loses its track. The collaboration of sensor tracking managed to perform the task for about 22.97 FPS, up to five times faster than feature-based tracking method used as comparison. Therefore, the proposed method can be used to track unknown environments without depending on amount of features on scene, while requiring lower computational cost.

Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC).

PubMed

Kwon, Deukwoo; Reis, Isildinha M

2015-08-12

When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, the maximum, and quartiles. We propose a simulation-based estimation approach using the Approximate Bayesian Computation (ABC) technique for estimating mean and standard deviation based on various sets of summary statistics found in published studies. We conduct a simulation study to compare the proposed ABC method with the existing methods of Hozo et al. (2005), Bland (2015), and Wan et al. (2014). In the estimation of the standard deviation, our ABC method performs better than the other methods when data are generated from skewed or heavy-tailed distributions. The corresponding average relative error (ARE) approaches zero as sample size increases. In data generated from the normal distribution, our ABC performs well. However, the Wan et al. method is best for estimating standard deviation under normal distribution. In the estimation of the mean, our ABC method is best regardless of assumed distribution. ABC is a flexible method for estimating the study-specific mean and standard deviation for meta-analysis, especially with underlying skewed or heavy-tailed distributions. The ABC method can be applied using other reported summary statistics such as the posterior mean and 95 % credible interval when Bayesian analysis has been employed.

Motion-induced phase error estimation and correction in 3D diffusion tensor imaging.

PubMed

Van, Anh T; Hernando, Diego; Sutton, Bradley P

2011-11-01

A multishot data acquisition strategy is one way to mitigate B0 distortion and T2∗ blurring for high-resolution diffusion-weighted magnetic resonance imaging experiments. However, different object motions that take place during different shots cause phase inconsistencies in the data, leading to significant image artifacts. This work proposes a maximum likelihood estimation and k-space correction of motion-induced phase errors in 3D multishot diffusion tensor imaging. The proposed error estimation is robust, unbiased, and approaches the Cramer-Rao lower bound. For rigid body motion, the proposed correction effectively removes motion-induced phase errors regardless of the k-space trajectory used and gives comparable performance to the more computationally expensive 3D iterative nonlinear phase error correction method. The method has been extended to handle multichannel data collected using phased-array coils. Simulation and in vivo data are shown to demonstrate the performance of the method.

A novel navigation method used in a ballistic missile

NASA Astrophysics Data System (ADS)

Qian, Hua-ming; Sun, Long; Cai, Jia-nan; Peng, Yu

2013-10-01

The traditional strapdown inertial/celestial integrated navigation method used in a ballistic missile cannot accurately estimate the accelerometer bias. It might cause a divergence of navigation errors. To solve this problem, a new navigation method named strapdown inertial/starlight refractive celestial integrated navigation is proposed. To verify the feasibility of the proposed method, a simulated program of a ballistic missile is presented. The simulation results indicated that, when multiple refraction stars are used, the proposed method can accurately estimate the accelerometer bias, and suppress the divergence of navigation errors completely. Specifically, in order to apply this method to a ballistic missile, a novel measurement equation based on stellar refraction was developed. Furthermore a method to calculate the number of refraction stars observed by the stellar sensor was given. Finally, the relationship between the number of refraction stars used and the navigation accuracy is analysed.

Estimation of Phase in Fringe Projection Technique Using High-order Instantaneous Moments Based Method

NASA Astrophysics Data System (ADS)

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

2010-04-01

For three-dimensional (3D) shape measurement using fringe projection techniques, the information about the 3D shape of an object is encoded in the phase of a recorded fringe pattern. The paper proposes a high-order instantaneous moments based method to estimate phase from a single fringe pattern in fringe projection. The proposed method works by approximating the phase as a piece-wise polynomial and subsequently determining the polynomial coefficients using high-order instantaneous moments to construct the polynomial phase. Simulation results are presented to show the method's potential.

Optshrink LR + S: accelerated fMRI reconstruction using non-convex optimal singular value shrinkage.

PubMed

Aggarwal, Priya; Shrivastava, Parth; Kabra, Tanay; Gupta, Anubha

2017-03-01

This paper presents a new accelerated fMRI reconstruction method, namely, OptShrink LR + S method that reconstructs undersampled fMRI data using a linear combination of low-rank and sparse components. The low-rank component has been estimated using non-convex optimal singular value shrinkage algorithm, while the sparse component has been estimated using convex l 1 minimization. The performance of the proposed method is compared with the existing state-of-the-art algorithms on real fMRI dataset. The proposed OptShrink LR + S method yields good qualitative and quantitative results.

Mutual Information between Discrete Variables with Many Categories using Recursive Adaptive Partitioning

PubMed Central

Seok, Junhee; Seon Kang, Yeong

2015-01-01

Mutual information, a general measure of the relatedness between two random variables, has been actively used in the analysis of biomedical data. The mutual information between two discrete variables is conventionally calculated by their joint probabilities estimated from the frequency of observed samples in each combination of variable categories. However, this conventional approach is no longer efficient for discrete variables with many categories, which can be easily found in large-scale biomedical data such as diagnosis codes, drug compounds, and genotypes. Here, we propose a method to provide stable estimations for the mutual information between discrete variables with many categories. Simulation studies showed that the proposed method reduced the estimation errors by 45 folds and improved the correlation coefficients with true values by 99 folds, compared with the conventional calculation of mutual information. The proposed method was also demonstrated through a case study for diagnostic data in electronic health records. This method is expected to be useful in the analysis of various biomedical data with discrete variables. PMID:26046461

A novel application of artificial neural network for wind speed estimation

NASA Astrophysics Data System (ADS)

Fang, Da; Wang, Jianzhou

2017-05-01

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.

Script-independent text line segmentation in freestyle handwritten documents.

PubMed

Li, Yi; Zheng, Yefeng; Doermann, David; Jaeger, Stefan; Li, Yi

2008-08-01

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike connected component based methods ( [1], [2] for example), the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [1]-[3]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise.

An investigation of new methods for estimating parameter sensitivities

NASA Technical Reports Server (NTRS)

Beltracchi, Todd J.; Gabriele, Gary A.

1988-01-01

Parameter sensitivity is defined as the estimation of changes in the modeling functions and the design variables due to small changes in the fixed parameters of the formulation. There are currently several methods for estimating parameter sensitivities requiring either difficult to obtain second order information, or do not return reliable estimates for the derivatives. Additionally, all the methods assume that the set of active constraints does not change in a neighborhood of the estimation point. If the active set does in fact change, than any extrapolations based on these derivatives may be in error. The objective here is to investigate more efficient new methods for estimating parameter sensitivities when the active set changes. The new method is based on the recursive quadratic programming (RQP) method and in conjunction a differencing formula to produce estimates of the sensitivities. This is compared to existing methods and is shown to be very competitive in terms of the number of function evaluations required. In terms of accuracy, the method is shown to be equivalent to a modified version of the Kuhn-Tucker method, where the Hessian of the Lagrangian is estimated using the BFS method employed by the RPQ algorithm. Inital testing on a test set with known sensitivities demonstrates that the method can accurately calculate the parameter sensitivity. To handle changes in the active set, a deflection algorithm is proposed for those cases where the new set of active constraints remains linearly independent. For those cases where dependencies occur, a directional derivative is proposed. A few simple examples are included for the algorithm, but extensive testing has not yet been performed.

Detecting isotopic ratio outliers

NASA Astrophysics Data System (ADS)

Bayne, C. K.; Smith, D. H.

An alternative method is proposed for improving isotopic ratio estimates. This method mathematically models pulse-count data and uses iterative reweighted Poisson regression to estimate model parameters to calculate the isotopic ratios. This computer-oriented approach provides theoretically better methods than conventional techniques to establish error limits and to identify outliers.

Algorithm Summary and Evaluation: Automatic Implementation of Ringdown Analysis for Electromechanical Mode Identification from Phasor Measurements

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

Zhou, Ning; Huang, Zhenyu; Tuffner, Francis K.

2010-02-28

Small signal stability problems are one of the major threats to grid stability and reliability. Prony analysis has been successfully applied on ringdown data to monitor electromechanical modes of a power system using phasor measurement unit (PMU) data. To facilitate an on-line application of mode estimation, this paper develops a recursive algorithm for implementing Prony analysis and proposed an oscillation detection method to detect ringdown data in real time. By automatically detecting ringdown data, the proposed method helps guarantee that Prony analysis is applied properly and timely on the ringdown data. Thus, the mode estimation results can be performed reliablymore » and timely. The proposed method is tested using Monte Carlo simulations based on a 17-machine model and is shown to be able to properly identify the oscillation data for on-line application of Prony analysis. In addition, the proposed method is applied to field measurement data from WECC to show the performance of the proposed algorithm.« less

Correction for FDG PET dose extravasations: Monte Carlo validation and quantitative evaluation of patient studies

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

Silva-Rodríguez, Jesús, E-mail: jesus.silva.rodriguez@sergas.es; Aguiar, Pablo, E-mail: pablo.aguiar.fernandez@sergas.es; Servicio de Medicina Nuclear, Complexo Hospitalario Universidade de Santiago de Compostela

Purpose: Current procedure guidelines for whole body [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET) state that studies with visible dose extravasations should be rejected for quantification protocols. Our work is focused on the development and validation of methods for estimating extravasated doses in order to correct standard uptake value (SUV) values for this effect in clinical routine. Methods: One thousand three hundred sixty-seven consecutive whole body FDG-PET studies were visually inspected looking for extravasation cases. Two methods for estimating the extravasated dose were proposed and validated in different scenarios using Monte Carlo simulations. All visible extravasations were retrospectively evaluated using a manualmore » ROI based method. In addition, the 50 patients with higher extravasated doses were also evaluated using a threshold-based method. Results: Simulation studies showed that the proposed methods for estimating extravasated doses allow us to compensate the impact of extravasations on SUV values with an error below 5%. The quantitative evaluation of patient studies revealed that paravenous injection is a relatively frequent effect (18%) with a small fraction of patients presenting considerable extravasations ranging from 1% to a maximum of 22% of the injected dose. A criterion based on the extravasated volume and maximum concentration was established in order to identify this fraction of patients that might be corrected for paravenous injection effect. Conclusions: The authors propose the use of a manual ROI based method for estimating the effectively administered FDG dose and then correct SUV quantification in those patients fulfilling the proposed criterion.« less

An investigation of new methods for estimating parameter sensitivities

NASA Technical Reports Server (NTRS)

Beltracchi, Todd J.; Gabriele, Gary A.

1989-01-01

The method proposed for estimating sensitivity derivatives is based on the Recursive Quadratic Programming (RQP) method and in conjunction a differencing formula to produce estimates of the sensitivities. This method is compared to existing methods and is shown to be very competitive in terms of the number of function evaluations required. In terms of accuracy, the method is shown to be equivalent to a modified version of the Kuhn-Tucker method, where the Hessian of the Lagrangian is estimated using the BFS method employed by the RQP algorithm. Initial testing on a test set with known sensitivities demonstrates that the method can accurately calculate the parameter sensitivity.

Dual energy approach for cone beam artifacts correction

NASA Astrophysics Data System (ADS)

Han, Chulhee; Choi, Shinkook; Lee, Changwoo; Baek, Jongduk

2017-03-01

Cone beam computed tomography systems generate 3D volumetric images, which provide further morphological information compared to radiography and tomosynthesis systems. However, reconstructed images by FDK algorithm contain cone beam artifacts when a cone angle is large. To reduce the cone beam artifacts, two-pass algorithm has been proposed. The two-pass algorithm considers the cone beam artifacts are mainly caused by high density materials, and proposes an effective method to estimate error images (i.e., cone beam artifacts images) by the high density materials. While this approach is simple and effective with a small cone angle (i.e., 5 - 7 degree), the correction performance is degraded as the cone angle increases. In this work, we propose a new method to reduce the cone beam artifacts using a dual energy technique. The basic idea of the proposed method is to estimate the error images generated by the high density materials more reliably. To do this, projection data of the high density materials are extracted from dual energy CT projection data using a material decomposition technique, and then reconstructed by iterative reconstruction using total-variation regularization. The reconstructed high density materials are used to estimate the error images from the original FDK images. The performance of the proposed method is compared with the two-pass algorithm using root mean square errors. The results show that the proposed method reduces the cone beam artifacts more effectively, especially with a large cone angle.

a Preliminary Work on Layout Slam for Reconstruction of Indoor Corridor Environments

NASA Astrophysics Data System (ADS)

Baligh Jahromi, A.; Sohn, G.; Shahbazi, M.; Kang, J.

2017-09-01

We propose a real time indoor corridor layout estimation method based on visual Simultaneous Localization and Mapping (SLAM). The proposed method adopts the Manhattan World Assumption at indoor spaces and uses the detected single image straight line segments and their corresponding orthogonal vanishing points to improve the feature matching scheme in the adopted visual SLAM system. Using the proposed real time indoor corridor layout estimation method, the system is able to build an online sparse map of structural corner point features. The challenges presented by abrupt camera rotation in the 3D space are successfully handled through matching vanishing directions of consecutive video frames on the Gaussian sphere. Using the single image based indoor layout features for initializing the system, permitted the proposed method to perform real time layout estimation and camera localization in indoor corridor areas. For layout structural corner points matching, we adopted features which are invariant under scale, translation, and rotation. We proposed a new feature matching cost function which considers both local and global context information. The cost function consists of a unary term, which measures pixel to pixel orientation differences of the matched corners, and a binary term, which measures the amount of angle differences between directly connected layout corner features. We have performed the experiments on real scenes at York University campus buildings and the available RAWSEEDS dataset. The incoming results depict that the proposed method robustly performs along with producing very limited position and orientation errors.

Point Cloud Based Relative Pose Estimation of a Satellite in Close Range

PubMed Central

Liu, Lujiang; Zhao, Gaopeng; Bo, Yuming

2016-01-01

Determination of the relative pose of satellites is essential in space rendezvous operations and on-orbit servicing missions. The key problems are the adoption of suitable sensor on board of a chaser and efficient techniques for pose estimation. This paper aims to estimate the pose of a target satellite in close range on the basis of its known model by using point cloud data generated by a flash LIDAR sensor. A novel model based pose estimation method is proposed; it includes a fast and reliable pose initial acquisition method based on global optimal searching by processing the dense point cloud data directly, and a pose tracking method based on Iterative Closest Point algorithm. Also, a simulation system is presented in this paper in order to evaluate the performance of the sensor and generate simulated sensor point cloud data. It also provides truth pose of the test target so that the pose estimation error can be quantified. To investigate the effectiveness of the proposed approach and achievable pose accuracy, numerical simulation experiments are performed; results demonstrate algorithm capability of operating with point cloud directly and large pose variations. Also, a field testing experiment is conducted and results show that the proposed method is effective. PMID:27271633

Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

PubMed

Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

2012-07-01

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

Depth-estimation-enabled compound eyes

NASA Astrophysics Data System (ADS)

Lee, Woong-Bi; Lee, Heung-No

2018-04-01

Most animals that have compound eyes determine object distances by using monocular cues, especially motion parallax. In artificial compound eye imaging systems inspired by natural compound eyes, object depths are typically estimated by measuring optic flow; however, this requires mechanical movement of the compound eyes or additional acquisition time. In this paper, we propose a method for estimating object depths in a monocular compound eye imaging system based on the computational compound eye (COMPU-EYE) framework. In the COMPU-EYE system, acceptance angles are considerably larger than interommatidial angles, causing overlap between the ommatidial receptive fields. In the proposed depth estimation technique, the disparities between these receptive fields are used to determine object distances. We demonstrate that the proposed depth estimation technique can estimate the distances of multiple objects.

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

PubMed

Ramadan, Ahmed; Boss, Connor; Choi, Jongeun; Peter Reeves, N; Cholewicki, Jacek; Popovich, John M; Radcliffe, Clark J

2018-07-01

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Methods for Sexually Transmitted Disease Prevention Programs to Estimate the Health and Medical Cost Impact of Changes in Their Budget.

PubMed

Chesson, Harrell W; Ludovic, Jennifer A; Berruti, Andrés A; Gift, Thomas L

2018-01-01

The purpose of this article was to describe methods that sexually transmitted disease (STD) programs can use to estimate the potential effects of changes in their budgets in terms of disease burden and direct medical costs. We proposed 2 distinct approaches to estimate the potential effect of changes in funding on subsequent STD burden, one based on an analysis of state-level STD prevention funding and gonorrhea case rates and one based on analyses of the effect of Disease Intervention Specialist (DIS) activities on gonorrhea case rates. We also illustrated how programs can estimate the impact of budget changes on intermediate outcomes, such as partner services. Finally, we provided an example of the application of these methods for a hypothetical state STD prevention program. The methods we proposed can provide general approximations of how a change in STD prevention funding might affect the level of STD prevention services provided, STD incidence rates, and the direct medical cost burden of STDs. In applying these methods to a hypothetical state, a reduction in annual funding of US $200,000 was estimated to lead to subsequent increases in STDs of 1.6% to 3.6%. Over 10 years, the reduction in funding totaled US $2.0 million, whereas the cumulative, additional direct medical costs of the increase in STDs totaled US $3.7 to US $8.4 million. The methods we proposed, though subject to important limitations, can allow STD prevention personnel to calculate evidence-based estimates of the effects of changes in their budget.

Item Response Theory Equating Using Bayesian Informative Priors.

ERIC Educational Resources Information Center

de la Torre, Jimmy; Patz, Richard J.

This paper seeks to extend the application of Markov chain Monte Carlo (MCMC) methods in item response theory (IRT) to include the estimation of equating relationships along with the estimation of test item parameters. A method is proposed that incorporates estimation of the equating relationship in the item calibration phase. Item parameters from…

An Improved Aerial Target Localization Method with a Single Vector Sensor

PubMed Central

Zhao, Anbang; Bi, Xuejie; Hui, Juan; Zeng, Caigao; Ma, Lin

2017-01-01

This paper focuses on the problems encountered in the actual data processing with the use of the existing aerial target localization methods, analyzes the causes of the problems, and proposes an improved algorithm. Through the processing of the sea experiment data, it is found that the existing algorithms have higher requirements for the accuracy of the angle estimation. The improved algorithm reduces the requirements of the angle estimation accuracy and obtains the robust estimation results. The closest distance matching estimation algorithm and the horizontal distance estimation compensation algorithm are proposed. The smoothing effect of the data after being post-processed by using the forward and backward two-direction double-filtering method has been improved, thus the initial stage data can be filtered, so that the filtering results retain more useful information. In this paper, the aerial target height measurement methods are studied, the estimation results of the aerial target are given, so as to realize the three-dimensional localization of the aerial target and increase the understanding of the underwater platform to the aerial target, so that the underwater platform has better mobility and concealment. PMID:29135956

Radiance and atmosphere propagation-based method for the target range estimation

NASA Astrophysics Data System (ADS)

Cho, Hoonkyung; Chun, Joohwan

2012-06-01

Target range estimation is traditionally based on radar and active sonar systems in modern combat system. However, the performance of such active sensor devices is degraded tremendously by jamming signal from the enemy. This paper proposes a simple range estimation method between the target and the sensor. Passive IR sensors measures infrared (IR) light radiance radiating from objects in dierent wavelength and this method shows robustness against electromagnetic jamming. The measured target radiance of each wavelength at the IR sensor depends on the emissive properties of target material and is attenuated by various factors, in particular the distance between the sensor and the target and atmosphere environment. MODTRAN is a tool that models atmospheric propagation of electromagnetic radiation. Based on the result from MODTRAN and measured radiance, the target range is estimated. To statistically analyze the performance of proposed method, we use maximum likelihood estimation (MLE) and evaluate the Cramer-Rao Lower Bound (CRLB) via the probability density function of measured radiance. And we also compare CRLB and the variance of and ML estimation using Monte-Carlo.

Network Reconstruction From High-Dimensional Ordinary Differential Equations.

PubMed

Chen, Shizhe; Shojaie, Ali; Witten, Daniela M

2017-01-01

We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.

On the more accurate channel model and positioning based on time-of-arrival for visible light localization

NASA Astrophysics Data System (ADS)

Amini, Changeez; Taherpour, Abbas; Khattab, Tamer; Gazor, Saeed

2017-01-01

This paper presents an improved propagation channel model for the visible light in indoor environments. We employ this model to derive an enhanced positioning algorithm using on the relation between the time-of-arrivals (TOAs) and the distances for two cases either by assuming known or unknown transmitter and receiver vertical distances. We propose two estimators, namely the maximum likelihood estimator and an estimator by employing the method of moments. To have an evaluation basis for these methods, we calculate the Cramer-Rao lower bound (CRLB) for the performance of the estimations. We show that the proposed model and estimations result in a superior performance in positioning when the transmitter and receiver are perfectly synchronized in comparison to the existing state-of-the-art counterparts. Moreover, the corresponding CRLB of the proposed model represents almost about 20 dB reduction in the localization error bound in comparison with the previous model for some practical scenarios.

The Dynamic Photometric Stereo Method Using a Multi-Tap CMOS Image Sensor †

PubMed Central

Yoda, Takuya; Nagahara, Hajime; Taniguchi, Rin-ichiro; Kagawa, Keiichiro; Yasutomi, Keita; Kawahito, Shoji

2018-01-01

The photometric stereo method enables estimation of surface normals from images that have been captured using different but known lighting directions. The classical photometric stereo method requires at least three images to determine the normals in a given scene. However, this method cannot be applied to dynamic scenes because it is assumed that the scene remains static while the required images are captured. In this work, we present a dynamic photometric stereo method for estimation of the surface normals in a dynamic scene. We use a multi-tap complementary metal-oxide-semiconductor (CMOS) image sensor to capture the input images required for the proposed photometric stereo method. This image sensor can divide the electrons from the photodiode from a single pixel into the different taps of the exposures and can thus capture multiple images under different lighting conditions with almost identical timing. We implemented a camera lighting system and created a software application to enable estimation of the normal map in real time. We also evaluated the accuracy of the estimated surface normals and demonstrated that our proposed method can estimate the surface normals of dynamic scenes. PMID:29510599

Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data.

PubMed

Ding, Aidong Adam; Hsieh, Jin-Jian; Wang, Weijing

2015-01-01

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen; Wald, Lawrence L.

2017-01-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization. PMID:26915119

Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.

PubMed

Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L

2016-08-01

This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.

Bilateral step length estimation using a single inertial measurement unit attached to the pelvis

PubMed Central

2012-01-01

Background The estimation of the spatio-temporal gait parameters is of primary importance in both physical activity monitoring and clinical contexts. A method for estimating step length bilaterally, during level walking, using a single inertial measurement unit (IMU) attached to the pelvis is proposed. In contrast to previous studies, based either on a simplified representation of the human gait mechanics or on a general linear regressive model, the proposed method estimates the step length directly from the integration of the acceleration along the direction of progression. Methods The IMU was placed at pelvis level fixed to the subject's belt on the right side. The method was validated using measurements from a stereo-photogrammetric system as a gold standard on nine subjects walking ten laps along a closed loop track of about 25 m, varying their speed. For each loop, only the IMU data recorded in a 4 m long portion of the track included in the calibrated volume of the SP system, were used for the analysis. The method takes advantage of the cyclic nature of gait and it requires an accurate determination of the foot contact instances. A combination of a Kalman filter and of an optimally filtered direct and reverse integration applied to the IMU signals formed a single novel method (Kalman and Optimally filtered Step length Estimation - KOSE method). A correction of the IMU displacement due to the pelvic rotation occurring in gait was implemented to estimate the step length and the traversed distance. Results The step length was estimated for all subjects with less than 3% error. Traversed distance was assessed with less than 2% error. Conclusions The proposed method provided estimates of step length and traversed distance more accurate than any other method applied to measurements obtained from a single IMU that can be found in the literature. In healthy subjects, it is reasonable to expect that, errors in traversed distance estimation during daily monitoring activity would be of the same order of magnitude of those presented. PMID:22316235

Development of method for evaluating estimated inundation area by using river flood analysis based on multiple flood scenarios

NASA Astrophysics Data System (ADS)

Ono, T.; Takahashi, T.

2017-12-01

Non-structural mitigation measures such as flood hazard map based on estimated inundation area have been more important because heavy rains exceeding the design rainfall frequently occur in recent years. However, conventional method may lead to an underestimation of the area because assumed locations of dike breach in river flood analysis are limited to the cases exceeding the high-water level. The objective of this study is to consider the uncertainty of estimated inundation area with difference of the location of dike breach in river flood analysis. This study proposed multiple flood scenarios which can set automatically multiple locations of dike breach in river flood analysis. The major premise of adopting this method is not to be able to predict the location of dike breach correctly. The proposed method utilized interval of dike breach which is distance of dike breaches placed next to each other. That is, multiple locations of dike breach were set every interval of dike breach. The 2D shallow water equations was adopted as the governing equation of river flood analysis, and the leap-frog scheme with staggered grid was used. The river flood analysis was verified by applying for the 2015 Kinugawa river flooding, and the proposed multiple flood scenarios was applied for the Akutagawa river in Takatsuki city. As the result of computation in the Akutagawa river, a comparison with each computed maximum inundation depth of dike breaches placed next to each other proved that the proposed method enabled to prevent underestimation of estimated inundation area. Further, the analyses on spatial distribution of inundation class and maximum inundation depth in each of the measurement points also proved that the optimum interval of dike breach which can evaluate the maximum inundation area using the minimum assumed locations of dike breach. In brief, this study found the optimum interval of dike breach in the Akutagawa river, which enabled estimated maximum inundation area to predict efficiently and accurately. The river flood analysis by using this proposed method will contribute to mitigate flood disaster by improving the accuracy of estimated inundation area.

Kappa statistic for clustered matched-pair data.

PubMed

Yang, Zhao; Zhou, Ming

2014-07-10

Kappa statistic is widely used to assess the agreement between two procedures in the independent matched-pair data. For matched-pair data collected in clusters, on the basis of the delta method and sampling techniques, we propose a nonparametric variance estimator for the kappa statistic without within-cluster correlation structure or distributional assumptions. The results of an extensive Monte Carlo simulation study demonstrate that the proposed kappa statistic provides consistent estimation and the proposed variance estimator behaves reasonably well for at least a moderately large number of clusters (e.g., K ≥50). Compared with the variance estimator ignoring dependence within a cluster, the proposed variance estimator performs better in maintaining the nominal coverage probability when the intra-cluster correlation is fair (ρ ≥0.3), with more pronounced improvement when ρ is further increased. To illustrate the practical application of the proposed estimator, we analyze two real data examples of clustered matched-pair data. Copyright © 2014 John Wiley & Sons, Ltd.

Instrumental variables vs. grouping approach for reducing bias due to measurement error.

PubMed

Batistatou, Evridiki; McNamee, Roseanne

2008-01-01

Attenuation of the exposure-response relationship due to exposure measurement error is often encountered in epidemiology. Given that error cannot be totally eliminated, bias correction methods of analysis are needed. Many methods require more than one exposure measurement per person to be made, but the `group mean OLS method,' in which subjects are grouped into several a priori defined groups followed by ordinary least squares (OLS) regression on the group means, can be applied with one measurement. An alternative approach is to use an instrumental variable (IV) method in which both the single error-prone measure and an IV are used in IV analysis. In this paper we show that the `group mean OLS' estimator is equal to an IV estimator with the group mean used as IV, but that the variance estimators for the two methods are different. We derive a simple expression for the bias in the common estimator which is a simple function of group size, reliability and contrast of exposure between groups, and show that the bias can be very small when group size is large. We compare this method with a new proposal (group mean ranking method), also applicable with a single exposure measurement, in which the IV is the rank of the group means. When there are two independent exposure measurements per subject, we propose a new IV method (EVROS IV) and compare it with Carroll and Stefanski's (CS IV) proposal in which the second measure is used as an IV; the new IV estimator combines aspects of the `group mean' and `CS' strategies. All methods are evaluated in terms of bias, precision and root mean square error via simulations and a dataset from occupational epidemiology. The `group mean ranking method' does not offer much improvement over the `group mean method.' Compared with the `CS' method, the `EVROS' method is less affected by low reliability of exposure. We conclude that the group IV methods we propose may provide a useful way to handle mismeasured exposures in epidemiology with or without replicate measurements. Our finding may also have implications for the use of aggregate variables in epidemiology to control for unmeasured confounding.

Estimation of phase derivatives using discrete chirp-Fourier-transform-based method.

PubMed

Gorthi, Sai Siva; Rastogi, Pramod

2009-08-15

Estimation of phase derivatives is an important task in many interferometric measurements in optical metrology. This Letter introduces a method based on discrete chirp-Fourier transform for accurate and direct estimation of phase derivatives, even in the presence of noise. The method is introduced in the context of the analysis of reconstructed interference fields in digital holographic interferometry. We present simulation and experimental results demonstrating the utility of the proposed method.

Estimating conditional proportion curves by regression residuals.

PubMed

Han, Bing; Lim, Nelson

2010-06-15

Researchers often derive a categorical outcome from an observed continuous measurement y. For example, human obesity status can be defined by the body mass index. They proceed to estimate the conditional proportion curve p(x) = P(y

Motion direction estimation based on active RFID with changing environment

NASA Astrophysics Data System (ADS)

Jie, Wu; Minghua, Zhu; Wei, He

2018-05-01

The gate system is used to estimate the direction of RFID tags carriers when they are going through the gate. Normally, it is difficult to achieve and keep a high accuracy in estimating motion direction of RFID tags because the received signal strength of tag changes sharply according to the changing electromagnetic environment. In this paper, a method of motion direction estimation for RFID tags is presented. To improve estimation accuracy, the machine leaning algorithm is used to get the fitting function of the received data by readers which are deployed inside and outside gate respectively. Then the fitted data are sampled to get the standard vector. We compare the stand vector with template vectors to get the motion direction estimation result. Then the corresponding template vector is updated according to the surrounding environment. We conducted the simulation and implement of the proposed method and the result shows that the proposed method in this work can improve and keep a high accuracy under the condition of the constantly changing environment.

A hierarchical estimator development for estimation of tire-road friction coefficient

PubMed Central

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified “magic formula” tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method. PMID:28178332

A hierarchical estimator development for estimation of tire-road friction coefficient.

PubMed

Zhang, Xudong; Göhlich, Dietmar

2017-01-01

The effect of vehicle active safety systems is subject to the friction force arising from the contact of tires and the road surface. Therefore, an adequate knowledge of the tire-road friction coefficient is of great importance to achieve a good performance of these control systems. This paper presents a tire-road friction coefficient estimation method for an advanced vehicle configuration, four-motorized-wheel electric vehicles, in which the longitudinal tire force is easily obtained. A hierarchical structure is adopted for the proposed estimation design. An upper estimator is developed based on unscented Kalman filter to estimate vehicle state information, while a hybrid estimation method is applied as the lower estimator to identify the tire-road friction coefficient using general regression neural network (GRNN) and Bayes' theorem. GRNN aims at detecting road friction coefficient under small excitations, which are the most common situations in daily driving. GRNN is able to accurately create a mapping from input parameters to the friction coefficient, avoiding storing an entire complex tire model. As for large excitations, the estimation algorithm is based on Bayes' theorem and a simplified "magic formula" tire model. The integrated estimation method is established by the combination of the above-mentioned estimators. Finally, the simulations based on a high-fidelity CarSim vehicle model are carried out on different road surfaces and driving maneuvers to verify the effectiveness of the proposed estimation method.

Compressed Symmetric Nested Arrays and Their Application for Direction-of-Arrival Estimation of Near-Field Sources.

PubMed

Li, Shuang; Xie, Dongfeng

2016-11-17

In this paper, a new sensor array geometry, called a compressed symmetric nested array (CSNA), is designed to increase the degrees of freedom in the near field. As its name suggests, a CSNA is constructed by getting rid of some elements from two identical nested arrays. The closed form expressions are also presented for the sensor locations and the largest degrees of freedom obtainable as a function of the total number of sensors. Furthermore, a novel DOA estimation method is proposed by utilizing the CSNA in the near field. By employing this new array geometry, our method can identify more sources than sensors. Compared with other existing methods, the proposed method achieves higher resolution because of increased array aperture. Simulation results are demonstrated to verify the effectiveness of the proposed method.

Myocardial motion estimation of tagged cardiac magnetic resonance images using tag motion constraints and multi-level b-splines interpolation.

PubMed

Liu, Hong; Yan, Meng; Song, Enmin; Wang, Jie; Wang, Qian; Jin, Renchao; Jin, Lianghai; Hung, Chih-Cheng

2016-05-01

Myocardial motion estimation of tagged cardiac magnetic resonance (TCMR) images is of great significance in clinical diagnosis and the treatment of heart disease. Currently, the harmonic phase analysis method (HARP) and the local sine-wave modeling method (SinMod) have been proven as two state-of-the-art motion estimation methods for TCMR images, since they can directly obtain the inter-frame motion displacement vector field (MDVF) with high accuracy and fast speed. By comparison, SinMod has better performance over HARP in terms of displacement detection, noise and artifacts reduction. However, the SinMod method has some drawbacks: 1) it is unable to estimate local displacements larger than half of the tag spacing; 2) it has observable errors in tracking of tag motion; and 3) the estimated MDVF usually has large local errors. To overcome these problems, we present a novel motion estimation method in this study. The proposed method tracks the motion of tags and then estimates the dense MDVF by using the interpolation. In this new method, a parameter estimation procedure for global motion is applied to match tag intersections between different frames, ensuring specific kinds of large displacements being correctly estimated. In addition, a strategy of tag motion constraints is applied to eliminate most of errors produced by inter-frame tracking of tags and the multi-level b-splines approximation algorithm is utilized, so as to enhance the local continuity and accuracy of the final MDVF. In the estimation of the motion displacement, our proposed method can obtain a more accurate MDVF compared with the SinMod method and our method can overcome the drawbacks of the SinMod method. However, the motion estimation accuracy of our method depends on the accuracy of tag lines detection and our method has a higher time complexity. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Inverse modelling for real-time estimation of radiological consequences in the early stage of an accidental radioactivity release.

PubMed

Pecha, Petr; Šmídl, Václav

2016-11-01

A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Practical Torque Estimation Method for Interior Permanent Magnet Synchronous Machine in Electric Vehicles.

PubMed

Wu, Zhihong; Lu, Ke; Zhu, Yuan

2015-01-01

The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary for the safety of the vehicle. In this paper, a torque estimation method based on flux estimator with a modified low pass filter is presented. Moreover, by taking into account the non-ideal characteristic of the inverter, the torque estimation accuracy is improved significantly. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulation and experiment.

A Practical Torque Estimation Method for Interior Permanent Magnet Synchronous Machine in Electric Vehicles

PubMed Central

Zhu, Yuan

2015-01-01

The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary for the safety of the vehicle. In this paper, a torque estimation method based on flux estimator with a modified low pass filter is presented. Moreover, by taking into account the non-ideal characteristic of the inverter, the torque estimation accuracy is improved significantly. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulation and experiment. PMID:26114557

A reference estimator based on composite sensor pattern noise for source device identification

NASA Astrophysics Data System (ADS)

Li, Ruizhe; Li, Chang-Tsun; Guan, Yu

2014-02-01

It has been proved that Sensor Pattern Noise (SPN) can serve as an imaging device fingerprint for source camera identification. Reference SPN estimation is a very important procedure within the framework of this application. Most previous works built reference SPN by averaging the SPNs extracted from 50 images of blue sky. However, this method can be problematic. Firstly, in practice we may face the problem of source camera identification in the absence of the imaging cameras and reference SPNs, which means only natural images with scene details are available for reference SPN estimation rather than blue sky images. It is challenging because the reference SPN can be severely contaminated by image content. Secondly, the number of available reference images sometimes is too few for existing methods to estimate a reliable reference SPN. In fact, existing methods lack consideration of the number of available reference images as they were designed for the datasets with abundant images to estimate the reference SPN. In order to deal with the aforementioned problem, in this work, a novel reference estimator is proposed. Experimental results show that our proposed method achieves better performance than the methods based on the averaged reference SPN, especially when few reference images used.

Bias and variance reduction in estimating the proportion of true-null hypotheses

PubMed Central

Cheng, Yebin; Gao, Dexiang; Tong, Tiejun

2015-01-01

When testing a large number of hypotheses, estimating the proportion of true nulls, denoted by \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\pi _0$\\end{document}, becomes increasingly important. This quantity has many applications in practice. For instance, a reliable estimate of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\pi _0$\\end{document} can eliminate the conservative bias of the Benjamini–Hochberg procedure on controlling the false discovery rate. It is known that most methods in the literature for estimating \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\pi _0$\\end{document} are conservative. Recently, some attempts have been paid to reduce such estimation bias. Nevertheless, they are either over bias corrected or suffering from an unacceptably large estimation variance. In this paper, we propose a new method for estimating \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\pi _0$\\end{document} that aims to reduce the bias and variance of the estimation simultaneously. To achieve this, we first utilize the probability density functions of false-null \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$p$\\end{document}-values and then propose a novel algorithm to estimate the quantity of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$\\pi _0$\\end{document}. The statistical behavior of the proposed estimator is also investigated. Finally, we carry out extensive simulation studies and several real data analysis to evaluate the performance of the proposed estimator. Both simulated and real data demonstrate that the proposed method may improve the existing literature significantly. PMID:24963010

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach.

PubMed

Kang, Le; Chen, Weijie; Petrick, Nicholas A; Gallas, Brandon D

2015-02-20

The area under the receiver operating characteristic curve is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of area under the receiver operating characteristic curve, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics-based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. Copyright © 2014 John Wiley & Sons, Ltd.

Near-Field Source Localization by Using Focusing Technique

NASA Astrophysics Data System (ADS)

He, Hongyang; Wang, Yide; Saillard, Joseph

2008-12-01

We discuss two fast algorithms to localize multiple sources in near field. The symmetry-based method proposed by Zhi and Chia (2007) is first improved by implementing a search-free procedure for the reduction of computation cost. We present then a focusing-based method which does not require symmetric array configuration. By using focusing technique, the near-field signal model is transformed into a model possessing the same structure as in the far-field situation, which allows the bearing estimation with the well-studied far-field methods. With the estimated bearing, the range estimation of each source is consequently obtained by using 1D MUSIC method without parameter pairing. The performance of the improved symmetry-based method and the proposed focusing-based method is compared by Monte Carlo simulations and with Crammer-Rao bound as well. Unlike other near-field algorithms, these two approaches require neither high-computation cost nor high-order statistics.

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.

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.

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Adaptive error covariances estimation methods for ensemble Kalman filters

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

Zhen, Yicun, E-mail: zhen@math.psu.edu; Harlim, John, E-mail: jharlim@psu.edu

2015-08-01

This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for usingmore » information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.« less

A Penalized Robust Method for Identifying Gene-Environment Interactions

PubMed Central

Shi, Xingjie; Liu, Jin; Huang, Jian; Zhou, Yong; Xie, Yang; Ma, Shuangge

2015-01-01

In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model mis-specification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications. PMID:24616063

A Non-Invasive Method for Estimating Cardiopulmonary Variables Using Breath-by-Breath Injection of Two Tracer Gases.

PubMed

Clifton, Lei; Clifton, David A; Hahn, Clive E W; Farmeryy, Andrew D

2013-01-01

Conventional methods for estimating cardiopulmonary variables usually require complex gas analyzers and the active co-operation of the patient. Therefore, they are not compatible with the crowded environment of the intensive care unit (ICU) or operating theatre, where patient co-operation is typically impossible. However, it is these patients that would benefit the most from accurate estimation of cardiopulmonary variables, because of their critical condition. This paper describes the results of a collaborative development between an anesthesiologists and biomedical engineers to create a compact and non-invasive system for the measurement of cardiopulmonary variables such as lung volume, airway dead space volume, and pulmonary blood flow. In contrast with conventional methods, the compact apparatus and non-invasive nature of the proposed method allow it to be used in the ICU, as well as in general clinical settings. We propose the use of a non-invasive method, in which tracer gases are injected into the patient's inspired breath, and the concentration of the tracer gases is subsequently measured. A novel breath-by-breath tidal ventilation model is then used to estimate the value of a patient's cardiopulmonary variables. Experimental results from an artificial lung demonstrate minimal error in the estimation of known parameters using the proposed method. Results from analysis of a cohort of 20 healthy volunteers (within the Oxford University Hospitals NHS Trust) show that the values of estimated cardiopulmonary variables from these subjects lies within the expected ranges. Advantages of this method are that it is non-invasive, compact, portable, and can perform analysis in real time with less than 1 min of acquired respiratory data.

Structural Estimation of Family Labor Supply with Taxes: Estimating a Continuous Hours Model Using a Direct Utility Specification

ERIC Educational Resources Information Center

Heim, Bradley T.

2009-01-01

This paper proposes a new method for estimating family labor supply in the presence of taxes. This method accounts for continuous hours choices, measurement error, unobserved heterogeneity in tastes for work, the nonlinear form of the tax code, and fixed costs of work in one comprehensive specification. Estimated on data from the 2001 PSID, the…

Fusion of visible and near-infrared images based on luminance estimation by weighted luminance algorithm

NASA Astrophysics Data System (ADS)

Wang, Zhun; Cheng, Feiyan; Shi, Junsheng; Huang, Xiaoqiao

2018-01-01

In a low-light scene, capturing color images needs to be at a high-gain setting or a long-exposure setting to avoid a visible flash. However, such these setting will lead to color images with serious noise or motion blur. Several methods have been proposed to improve a noise-color image through an invisible near infrared flash image. A novel method is that the luminance component and the chroma component of the improved color image are estimated from different image sources [1]. The luminance component is estimated mainly from the NIR image via a spectral estimation, and the chroma component is estimated from the noise-color image by denoising. However, it is challenging to estimate the luminance component. This novel method to estimate the luminance component needs to generate the learning data pairs, and the processes and algorithm are complex. It is difficult to achieve practical application. In order to reduce the complexity of the luminance estimation, an improved luminance estimation algorithm is presented in this paper, which is to weight the NIR image and the denoised-color image and the weighted coefficients are based on the mean value and standard deviation of both images. Experimental results show that the same fusion effect at aspect of color fidelity and texture quality is achieved, compared the proposed method with the novel method, however, the algorithm is more simple and practical.

Parameter identification for structural dynamics based on interval analysis algorithm

NASA Astrophysics Data System (ADS)

Yang, Chen; Lu, Zixing; Yang, Zhenyu; Liang, Ke

2018-04-01

A parameter identification method using interval analysis algorithm for structural dynamics is presented in this paper. The proposed uncertain identification method is investigated by using central difference method and ARMA system. With the help of the fixed memory least square method and matrix inverse lemma, a set-membership identification technology is applied to obtain the best estimation of the identified parameters in a tight and accurate region. To overcome the lack of insufficient statistical description of the uncertain parameters, this paper treats uncertainties as non-probabilistic intervals. As long as we know the bounds of uncertainties, this algorithm can obtain not only the center estimations of parameters, but also the bounds of errors. To improve the efficiency of the proposed method, a time-saving algorithm is presented by recursive formula. At last, to verify the accuracy of the proposed method, two numerical examples are applied and evaluated by three identification criteria respectively.

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.

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.

Digital Biomass Accumulation Using High-Throughput Plant Phenotype Data Analysis.

PubMed

Rahaman, Md Matiur; Ahsan, Md Asif; Gillani, Zeeshan; Chen, Ming

2017-09-01

Biomass is an important phenotypic trait in functional ecology and growth analysis. The typical methods for measuring biomass are destructive, and they require numerous individuals to be cultivated for repeated measurements. With the advent of image-based high-throughput plant phenotyping facilities, non-destructive biomass measuring methods have attempted to overcome this problem. Thus, the estimation of plant biomass of individual plants from their digital images is becoming more important. In this paper, we propose an approach to biomass estimation based on image derived phenotypic traits. Several image-based biomass studies state that the estimation of plant biomass is only a linear function of the projected plant area in images. However, we modeled the plant volume as a function of plant area, plant compactness, and plant age to generalize the linear biomass model. The obtained results confirm the proposed model and can explain most of the observed variance during image-derived biomass estimation. Moreover, a small difference was observed between actual and estimated digital biomass, which indicates that our proposed approach can be used to estimate digital biomass accurately.

Estimation of Qualitative and Quantitative Parameters of Air Cleaning by a Pulsed Corona Discharge Using Multicomponent Standard Mixtures

NASA Astrophysics Data System (ADS)

Filatov, I. E.; Uvarin, V. V.; Kuznetsov, D. L.

2018-05-01

The efficiency of removal of volatile organic impurities in air by a pulsed corona discharge is investigated using model mixtures. Based on the method of competing reactions, an approach to estimating the qualitative and quantitative parameters of the employed electrophysical technique is proposed. The concept of the "toluene coefficient" characterizing the relative reactivity of a component as compared to toluene is introduced. It is proposed that the energy efficiency of the electrophysical method be estimated using the concept of diversified yield of the removal process. Such an approach makes it possible to substantially intensify the determination of energy parameters of removal of impurities and can also serve as a criterion for estimating the effectiveness of various methods in which a nonequilibrium plasma is used for air cleaning from volatile impurities.

Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

NASA Astrophysics Data System (ADS)

Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

Fatigued monetary bills adversely affect the daily operation of automated teller machines (ATMs). In order to make the classification of fatigued bills more efficient, the development of an automatic fatigued monetary bill classification method is desirable. We propose a new method by which to estimate the fatigue level of monetary bills from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves the estimation accuracy of the fatigue level of monetary bills by adding frequency domain information to the acoustic energy pattern. The experimental results with real monetary bill samples reveal the effectiveness of the proposed method.

A method for analyzing clustered interval-censored data based on Cox's model.

PubMed

Kor, Chew-Teng; Cheng, Kuang-Fu; Chen, Yi-Hau

2013-02-28

Methods for analyzing interval-censored data are well established. Unfortunately, these methods are inappropriate for the studies with correlated data. In this paper, we focus on developing a method for analyzing clustered interval-censored data. Our method is based on Cox's proportional hazard model with piecewise-constant baseline hazard function. The correlation structure of the data can be modeled by using Clayton's copula or independence model with proper adjustment in the covariance estimation. We establish estimating equations for the regression parameters and baseline hazards (and a parameter in copula) simultaneously. Simulation results confirm that the point estimators follow a multivariate normal distribution, and our proposed variance estimations are reliable. In particular, we found that the approach with independence model worked well even when the true correlation model was derived from Clayton's copula. We applied our method to a family-based cohort study of pandemic H1N1 influenza in Taiwan during 2009-2010. Using the proposed method, we investigate the impact of vaccination and family contacts on the incidence of pH1N1 influenza. Copyright © 2012 John Wiley & Sons, Ltd.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

Registration using natural features for augmented reality systems.

PubMed

Yuan, M L; Ong, S K; Nee, A Y C

2006-01-01

Registration is one of the most difficult problems in augmented reality (AR) systems. In this paper, a simple registration method using natural features based on the projective reconstruction technique is proposed. This method consists of two steps: embedding and rendering. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In rendering, the Kanade-Lucas-Tomasi (KLT) feature tracker is used to track the natural feature correspondences in the live video. The natural features that have been tracked are used to estimate the corresponding projective matrix in the image sequence. Next, the projective reconstruction technique is used to transfer the four specified points to compute the registration matrix for augmentation. This paper also proposes a robust method for estimating the projective matrix, where the natural features that have been tracked are normalized (translation and scaling) and used as the input data. The estimated projective matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. The proposed registration method has three major advantages: 1) It is simple, as no predefined fiducials or markers are used for registration for either indoor and outdoor AR applications. 2) It is robust, because it remains effective as long as at least six natural features are tracked during the entire augmentation, and the existence of the corresponding projective matrices in the live video is guaranteed. Meanwhile, the robust method to estimate the projective matrix can obtain stable results even when there are some outliers during the tracking process. 3) Virtual objects can still be superimposed on the specified areas, even if some parts of the areas are occluded during the entire process. Some indoor and outdoor experiments have been conducted to validate the performance of this proposed method.

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

PubMed Central

Wang, Yikai; Kang, Jian; Kemmer, Phebe B.; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods. PMID:27242395

An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

PubMed

Wang, Yikai; Kang, Jian; Kemmer, Phebe B; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package "DensParcorr" can be downloaded from CRAN for implementing the proposed statistical methods.

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.

Comparison of least squares and exponential sine sweep methods for Parallel Hammerstein Models estimation

NASA Astrophysics Data System (ADS)

Rebillat, Marc; Schoukens, Maarten

2018-05-01

Linearity is a common assumption for many real-life systems, but in many cases the nonlinear behavior of systems cannot be ignored and must be modeled and estimated. Among the various existing classes of nonlinear models, Parallel Hammerstein Models (PHM) are interesting as they are at the same time easy to interpret as well as to estimate. One way to estimate PHM relies on the fact that the estimation problem is linear in the parameters and thus that classical least squares (LS) estimation algorithms can be used. In that area, this article introduces a regularized LS estimation algorithm inspired on some of the recently developed regularized impulse response estimation techniques. Another mean to estimate PHM consists in using parametric or non-parametric exponential sine sweeps (ESS) based methods. These methods (LS and ESS) are founded on radically different mathematical backgrounds but are expected to tackle the same issue. A methodology is proposed here to compare them with respect to (i) their accuracy, (ii) their computational cost, and (iii) their robustness to noise. Tests are performed on simulated systems for several values of methods respective parameters and of signal to noise ratio. Results show that, for a given set of data points, the ESS method is less demanding in computational resources than the LS method but that it is also less accurate. Furthermore, the LS method needs parameters to be set in advance whereas the ESS method is not subject to conditioning issues and can be fully non-parametric. In summary, for a given set of data points, ESS method can provide a first, automatic, and quick overview of a nonlinear system than can guide more computationally demanding and precise methods, such as the regularized LS one proposed here.

DETECTORS AND EXPERIMENTAL METHODS: Heuristic approach for peak regions estimation in gamma-ray spectra measured by a NaI detector

NASA Astrophysics Data System (ADS)

Zhu, Meng-Hua; Liu, Liang-Gang; You, Zhong; Xu, Ao-Ao

2009-03-01

In this paper, a heuristic approach based on Slavic's peak searching method has been employed to estimate the width of peak regions for background removing. Synthetic and experimental data are used to test this method. With the estimated peak regions using the proposed method in the whole spectrum, we find it is simple and effective enough to be used together with the Statistics-sensitive Nonlinear Iterative Peak-Clipping method.

Two-Dimensional DOA and Polarization Estimation for a Mixture of Uncorrelated and Coherent Sources with Sparsely-Distributed Vector Sensor Array

PubMed Central

Si, Weijian; Zhao, Pinjiao; Qu, Zhiyu

2016-01-01

This paper presents an L-shaped sparsely-distributed vector sensor (SD-VS) array with four different antenna compositions. With the proposed SD-VS array, a novel two-dimensional (2-D) direction of arrival (DOA) and polarization estimation method is proposed to handle the scenario where uncorrelated and coherent sources coexist. The uncorrelated and coherent sources are separated based on the moduli of the eigenvalues. For the uncorrelated sources, coarse estimates are acquired by extracting the DOA information embedded in the steering vectors from estimated array response matrix of the uncorrelated sources, and they serve as coarse references to disambiguate fine estimates with cyclical ambiguity obtained from the spatial phase factors. For the coherent sources, four Hankel matrices are constructed, with which the coherent sources are resolved in a similar way as for the uncorrelated sources. The proposed SD-VS array requires only two collocated antennas for each vector sensor, thus the mutual coupling effects across the collocated antennas are reduced greatly. Moreover, the inter-sensor spacings are allowed beyond a half-wavelength, which results in an extended array aperture. Simulation results demonstrate the effectiveness and favorable performance of the proposed method. PMID:27258271

A Luenberger observer for reaction-diffusion models with front position data

NASA Astrophysics Data System (ADS)

Collin, Annabelle; Chapelle, Dominique; Moireau, Philippe

2015-11-01

We propose a Luenberger observer for reaction-diffusion models with propagating front features, and for data associated with the location of the front over time. Such models are considered in various application fields, such as electrophysiology, wild-land fire propagation and tumor growth modeling. Drawing our inspiration from image processing methods, we start by proposing an observer for the eikonal-curvature equation that can be derived from the reaction-diffusion model by an asymptotic expansion. We then carry over this observer to the underlying reaction-diffusion equation by an ;inverse asymptotic analysis;, and we show that the associated correction in the dynamics has a stabilizing effect for the linearized estimation error. We also discuss the extension to joint state-parameter estimation by using the earlier-proposed ROUKF strategy. We then illustrate and assess our proposed observer method with test problems pertaining to electrophysiology modeling, including with a realistic model of cardiac atria. Our numerical trials show that state estimation is directly very effective with the proposed Luenberger observer, while specific strategies are needed to accurately perform parameter estimation - as is usual with Kalman filtering used in a nonlinear setting - and we demonstrate two such successful strategies.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling

PubMed Central

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A.

2012-01-01

This paper considers semiparametric estimation of the Cox proportional hazards model for right-censored and length-biased data arising from prevalent sampling. To exploit the special structure of length-biased sampling, we propose a maximum pseudo-profile likelihood estimator, which can handle time-dependent covariates and is consistent under covariate-dependent censoring. Simulation studies show that the proposed estimator is more efficient than its competitors. A data analysis illustrates the methods and theory. PMID:23843659

Doppler ultrasound-based measurement of tendon velocity and displacement for application toward detecting user-intended motion.

PubMed

Stegman, Kelly J; Park, Edward J; Dechev, Nikolai

2012-07-01

The motivation of this research is to non-invasively monitor the wrist tendon's displacement and velocity, for purposes of controlling a prosthetic device. This feasibility study aims to determine if the proposed technique using Doppler ultrasound is able to accurately estimate the tendon's instantaneous velocity and displacement. This study is conducted with a tendon mimicking experiment consisting of two different materials: a commercial ultrasound scanner, and a reference linear motion stage set-up. Audio-based output signals are acquired from the ultrasound scanner, and are processed with our proposed Fourier technique to obtain the tendon's velocity and displacement estimates. We then compare our estimates to an external reference system, and also to the ultrasound scanner's own estimates based on its proprietary software. The proposed tendon motion estimation method has been shown to be repeatable, effective and accurate in comparison to the external reference system, and is generally more accurate than the scanner's own estimates. After establishing this feasibility study, future testing will include cadaver-based studies to test the technique on the human arm tendon anatomy, and later on live human test subjects in order to further refine the proposed method for the novel purpose of detecting user-intended tendon motion for controlling wearable prosthetic devices.

MrBayes tgMC3++: A High Performance and Resource-Efficient GPU-Oriented Phylogenetic Analysis Method.

PubMed

Ling, Cheng; Hamada, Tsuyoshi; Gao, Jingyang; Zhao, Guoguang; Sun, Donghong; Shi, Weifeng

2016-01-01

MrBayes is a widespread phylogenetic inference tool harnessing empirical evolutionary models and Bayesian statistics. However, the computational cost on the likelihood estimation is very expensive, resulting in undesirably long execution time. Although a number of multi-threaded optimizations have been proposed to speed up MrBayes, there are bottlenecks that severely limit the GPU thread-level parallelism of likelihood estimations. This study proposes a high performance and resource-efficient method for GPU-oriented parallelization of likelihood estimations. Instead of having to rely on empirical programming, the proposed novel decomposition storage model implements high performance data transfers implicitly. In terms of performance improvement, a speedup factor of up to 178 can be achieved on the analysis of simulated datasets by four Tesla K40 cards. In comparison to the other publicly available GPU-oriented MrBayes, the tgMC 3 ++ method (proposed herein) outperforms the tgMC 3 (v1.0), nMC 3 (v2.1.1) and oMC 3 (v1.00) methods by speedup factors of up to 1.6, 1.9 and 2.9, respectively. Moreover, tgMC 3 ++ supports more evolutionary models and gamma categories, which previous GPU-oriented methods fail to take into analysis.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

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

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees ofmore » freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.« less

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Collaborative localization in wireless sensor networks via pattern recognition in radio irregularity using omnidirectional antennas.

PubMed

Jiang, Joe-Air; Chuang, Cheng-Long; Lin, Tzu-Shiang; Chen, Chia-Pang; Hung, Chih-Hung; Wang, Jiing-Yi; Liu, Chang-Wang; Lai, Tzu-Yun

2010-01-01

In recent years, various received signal strength (RSS)-based localization estimation approaches for wireless sensor networks (WSNs) have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments.

New agreement measures based on survival processes

PubMed Central

Guo, Ying; Li, Ruosha; Peng, Limin; Manatunga, Amita K.

2013-01-01

Summary The need to assess agreement arises in many scenarios in biomedical sciences when measurements were taken by different methods on the same subjects. When the endpoints are survival outcomes, the study of agreement becomes more challenging given the special characteristics of time-to-event data. In this paper, we propose a new framework for assessing agreement based on survival processes that can be viewed as a natural representation of time-to-event outcomes. Our new agreement measure is formulated as the chance-corrected concordance between survival processes. It provides a new perspective for studying the relationship between correlated survival outcomes and offers an appealing interpretation as the agreement between survival times on the absolute distance scale. We provide a multivariate extension of the proposed agreement measure for multiple methods. Furthermore, the new framework enables a natural extension to evaluate time-dependent agreement structure. We develop nonparametric estimation of the proposed new agreement measures. Our estimators are shown to be strongly consistent and asymptotically normal. We evaluate the performance of the proposed estimators through simulation studies and then illustrate the methods using a prostate cancer data example. PMID:23844617

Correction for FDG PET dose extravasations: Monte Carlo validation and quantitative evaluation of patient studies.

PubMed

Silva-Rodríguez, Jesús; Aguiar, Pablo; Sánchez, Manuel; Mosquera, Javier; Luna-Vega, Víctor; Cortés, Julia; Garrido, Miguel; Pombar, Miguel; Ruibal, Alvaro

2014-05-01

Current procedure guidelines for whole body [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET) state that studies with visible dose extravasations should be rejected for quantification protocols. Our work is focused on the development and validation of methods for estimating extravasated doses in order to correct standard uptake value (SUV) values for this effect in clinical routine. One thousand three hundred sixty-seven consecutive whole body FDG-PET studies were visually inspected looking for extravasation cases. Two methods for estimating the extravasated dose were proposed and validated in different scenarios using Monte Carlo simulations. All visible extravasations were retrospectively evaluated using a manual ROI based method. In addition, the 50 patients with higher extravasated doses were also evaluated using a threshold-based method. Simulation studies showed that the proposed methods for estimating extravasated doses allow us to compensate the impact of extravasations on SUV values with an error below 5%. The quantitative evaluation of patient studies revealed that paravenous injection is a relatively frequent effect (18%) with a small fraction of patients presenting considerable extravasations ranging from 1% to a maximum of 22% of the injected dose. A criterion based on the extravasated volume and maximum concentration was established in order to identify this fraction of patients that might be corrected for paravenous injection effect. The authors propose the use of a manual ROI based method for estimating the effectively administered FDG dose and then correct SUV quantification in those patients fulfilling the proposed criterion.

Performance Evaluation of EnKF-based Hydrogeological Site Characterization using Color Coherent Vectors

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.

2017-12-01

Complexity of hydrogeological systems arises from the multi-scale heterogeneity and insufficient measurements of their underlying parameters such as hydraulic conductivity and porosity. An inadequate characterization of hydrogeological properties can significantly decrease the trustworthiness of numerical models that predict groundwater flow and solute transport. Therefore, a variety of data assimilation methods have been proposed in order to estimate hydrogeological parameters from spatially scarce data by incorporating the governing physical models. In this work, we propose a novel framework for evaluating the performance of these estimation methods. We focus on the Ensemble Kalman Filter (EnKF) approach that is a widely used data assimilation technique. It reconciles multiple sources of measurements to sequentially estimate model parameters such as the hydraulic conductivity. Several methods have been used in the literature to quantify the accuracy of the estimations obtained by EnKF, including Rank Histograms, RMSE and Ensemble Spread. However, these commonly used methods do not regard the spatial information and variability of geological formations. This can cause hydraulic conductivity fields with very different spatial structures to have similar histograms or RMSE. We propose a vision-based approach that can quantify the accuracy of estimations by considering the spatial structure embedded in the estimated fields. Our new approach consists of adapting a new metric, Color Coherent Vectors (CCV), to evaluate the accuracy of estimated fields achieved by EnKF. CCV is a histogram-based technique for comparing images that incorporate spatial information. We represent estimated fields as digital three-channel images and use CCV to compare and quantify the accuracy of estimations. The sensitivity of CCV to spatial information makes it a suitable metric for assessing the performance of spatial data assimilation techniques. Under various factors of data assimilation methods such as number, layout, and type of measurements, we compare the performance of CCV with other metrics such as RMSE. By simulating hydrogeological processes using estimated and true fields, we observe that CCV outperforms other existing evaluation metrics.

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations

PubMed Central

Li, Laquan; Wang, Jian; Lu, Wei; Tan, Shan

2016-01-01

Accurate tumor segmentation from PET images is crucial in many radiation oncology applications. Among others, partial volume effect (PVE) is recognized as one of the most important factors degrading imaging quality and segmentation accuracy in PET. Taking into account that image restoration and tumor segmentation are tightly coupled and can promote each other, we proposed a variational method to solve both problems simultaneously in this study. The proposed method integrated total variation (TV) semi-blind de-convolution and Mumford-Shah segmentation with multiple regularizations. Unlike many existing energy minimization methods using either TV or L2 regularization, the proposed method employed TV regularization over tumor edges to preserve edge information, and L2 regularization inside tumor regions to preserve the smooth change of the metabolic uptake in a PET image. The blur kernel was modeled as anisotropic Gaussian to address the resolution difference in transverse and axial directions commonly seen in a clinic PET scanner. The energy functional was rephrased using the Γ-convergence approximation and was iteratively optimized using the alternating minimization (AM) algorithm. The performance of the proposed method was validated on a physical phantom and two clinic datasets with non-Hodgkin’s lymphoma and esophageal cancer, respectively. Experimental results demonstrated that the proposed method had high performance for simultaneous image restoration, tumor segmentation and scanner blur kernel estimation. Particularly, the recovery coefficients (RC) of the restored images of the proposed method in the phantom study were close to 1, indicating an efficient recovery of the original blurred images; for segmentation the proposed method achieved average dice similarity indexes (DSIs) of 0.79 and 0.80 for two clinic datasets, respectively; and the relative errors of the estimated blur kernel widths were less than 19% in the transversal direction and 7% in the axial direction. PMID:28603407

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.

Variational estimate method for solving autonomous ordinary differential equations

NASA Astrophysics Data System (ADS)

Mungkasi, Sudi

2018-04-01

In this paper, we propose a method for solving first-order autonomous ordinary differential equation problems using a variational estimate formulation. The variational estimate is constructed with a Lagrange multiplier which is chosen optimally, so that the formulation leads to an accurate solution to the problem. The variational estimate is an integral form, which can be computed using a computer software. As the variational estimate is an explicit formula, the solution is easy to compute. This is a great advantage of the variational estimate formulation.

Parameter estimation of an ARMA model for river flow forecasting using goal programming

NASA Astrophysics Data System (ADS)

Mohammadi, Kourosh; Eslami, H. R.; Kahawita, Rene

2006-11-01

SummaryRiver flow forecasting constitutes one of the most important applications in hydrology. Several methods have been developed for this purpose and one of the most famous techniques is the Auto regressive moving average (ARMA) model. In the research reported here, the goal was to minimize the error for a specific season of the year as well as for the complete series. Goal programming (GP) was used to estimate the ARMA model parameters. Shaloo Bridge station on the Karun River with 68 years of observed stream flow data was selected to evaluate the performance of the proposed method. The results when compared with the usual method of maximum likelihood estimation were favorable with respect to the new proposed algorithm.

Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

PubMed Central

Cortés, Camilo; Unzueta, Luis; de los Reyes-Guzmán, Ana; Ruiz, Oscar E.; Flórez, Julián

2016-01-01

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR. PMID:27403044

Systematic Error Modeling and Bias Estimation

PubMed Central

Zhang, Feihu; Knoll, Alois

2016-01-01

This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation. PMID:27213386

Polarization Smoothing Generalized MUSIC Algorithm with Polarization Sensitive Array for Low Angle Estimation.

PubMed

Tan, Jun; Nie, Zaiping

2018-05-12

Direction of Arrival (DOA) estimation of low-altitude targets is difficult due to the multipath coherent interference from the ground reflection image of the targets, especially for very high frequency (VHF) radars, which have antennae that are severely restricted in terms of aperture and height. The polarization smoothing generalized multiple signal classification (MUSIC) algorithm, which combines polarization smoothing and generalized MUSIC algorithm for polarization sensitive arrays (PSAs), was proposed to solve this problem in this paper. Firstly, the polarization smoothing pre-processing was exploited to eliminate the coherence between the direct and the specular signals. Secondly, we constructed the generalized MUSIC algorithm for low angle estimation. Finally, based on the geometry information of the symmetry multipath model, the proposed algorithm was introduced to convert the two-dimensional searching into one-dimensional searching, thus reducing the computational burden. Numerical results were provided to verify the effectiveness of the proposed method, showing that the proposed algorithm has significantly improved angle estimation performance in the low-angle area compared with the available methods, especially when the grazing angle is near zero.

Probing-error compensation using 5 degree of freedom force/moment sensor for coordinate measuring machine

NASA Astrophysics Data System (ADS)

Lee, Minho; Cho, Nahm-Gyoo

2013-09-01

A new probing and compensation method is proposed to improve the three-dimensional (3D) measuring accuracy of 3D shapes, including irregular surfaces. A new tactile coordinate measuring machine (CMM) probe with a five-degree of freedom (5-DOF) force/moment sensor using carbon fiber plates was developed. The proposed method efficiently removes the anisotropic sensitivity error and decreases the stylus deformation and the actual contact point estimation errors that are major error components of shape measurement using touch probes. The relationship between the measuring force and estimation accuracy of the actual contact point error and stylus deformation error are examined for practical use of the proposed method. The appropriate measuring force condition is presented for the precision measurement.

Conditional estimation using prior information in 2-stage group sequential designs assuming asymptotic normality when the trial terminated early.

PubMed

Shimura, Masashi; Maruo, Kazushi; Gosho, Masahiko

2018-04-23

Two-stage designs are widely used to determine whether a clinical trial should be terminated early. In such trials, a maximum likelihood estimate is often adopted to describe the difference in efficacy between the experimental and reference treatments; however, this method is known to display conditional bias. To reduce such bias, a conditional mean-adjusted estimator (CMAE) has been proposed, although the remaining bias may be nonnegligible when a trial is stopped for efficacy at the interim analysis. We propose a new estimator for adjusting the conditional bias of the treatment effect by extending the idea of the CMAE. This estimator is calculated by weighting the maximum likelihood estimate obtained at the interim analysis and the effect size prespecified when calculating the sample size. We evaluate the performance of the proposed estimator through analytical and simulation studies in various settings in which a trial is stopped for efficacy or futility at the interim analysis. We find that the conditional bias of the proposed estimator is smaller than that of the CMAE when the information time at the interim analysis is small. In addition, the mean-squared error of the proposed estimator is also smaller than that of the CMAE. In conclusion, we recommend the use of the proposed estimator for trials that are terminated early for efficacy or futility. Copyright © 2018 John Wiley & Sons, Ltd.

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.

Simple Form of MMSE Estimator for Super-Gaussian Prior Densities

NASA Astrophysics Data System (ADS)

Kittisuwan, Pichid

2015-04-01

The denoising method that become popular in recent years for additive white Gaussian noise (AWGN) are Bayesian estimation techniques e.g., maximum a posteriori (MAP) and minimum mean square error (MMSE). In super-Gaussian prior densities, it is well known that the MMSE estimator in such a case has a complicated form. In this work, we derive the MMSE estimation with Taylor series. We show that the proposed estimator also leads to a simple formula. An extension of this estimator to Pearson type VII prior density is also offered. The experimental result shows that the proposed estimator to the original MMSE nonlinearity is reasonably good.

Design and analysis of three-arm trials with negative binomially distributed endpoints.

PubMed

Mütze, Tobias; Munk, Axel; Friede, Tim

2016-02-20

A three-arm clinical trial design with an experimental treatment, an active control, and a placebo control, commonly referred to as the gold standard design, enables testing of non-inferiority or superiority of the experimental treatment compared with the active control. In this paper, we propose methods for designing and analyzing three-arm trials with negative binomially distributed endpoints. In particular, we develop a Wald-type test with a restricted maximum-likelihood variance estimator for testing non-inferiority or superiority. For this test, sample size and power formulas as well as optimal sample size allocations will be derived. The performance of the proposed test will be assessed in an extensive simulation study with regard to type I error rate, power, sample size, and sample size allocation. For the purpose of comparison, Wald-type statistics with a sample variance estimator and an unrestricted maximum-likelihood estimator are included in the simulation study. We found that the proposed Wald-type test with a restricted variance estimator performed well across the considered scenarios and is therefore recommended for application in clinical trials. The methods proposed are motivated and illustrated by a recent clinical trial in multiple sclerosis. The R package ThreeArmedTrials, which implements the methods discussed in this paper, is available on CRAN. Copyright © 2015 John Wiley & Sons, Ltd.

Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals.

PubMed

Martin, Emma C; Aarons, Leon; Yates, James W T

2016-07-01

Xenograft studies are commonly used to assess the efficacy of new compounds and characterise their dose-response relationship. Analysis often involves comparing the final tumour sizes across dose groups. This can cause bias, as often in xenograft studies a tumour burden limit (TBL) is imposed for ethical reasons, leading to the animals with the largest tumours being excluded from the final analysis. This means the average tumour size, particularly in the control group, is underestimated, leading to an underestimate of the treatment effect. Four methods to account for dropout due to the TBL are proposed, which use all the available data instead of only final observations: modelling, pattern mixture models, treating dropouts as censored using the M3 method and joint modelling of tumour growth and dropout. The methods were applied to both a simulated data set and a real example. All four proposed methods led to an improvement in the estimate of treatment effect in the simulated data. The joint modelling method performed most strongly, with the censoring method also providing a good estimate of the treatment effect, but with higher uncertainty. In the real data example, the dose-response estimated using the censoring and joint modelling methods was higher than the very flat curve estimated from average final measurements. Accounting for dropout using the proposed censoring or joint modelling methods allows the treatment effect to be recovered in studies where it may have been obscured due to dropout caused by the TBL.

A method for direct measurement of the first-order mass moments of human body segments.

PubMed

Fujii, Yusaku; Shimada, Kazuhito; Maru, Koichi; Ozawa, Junichi; Lu, Rong-Sheng

2010-01-01

We propose a simple and direct method for measuring the first-order mass moment of a human body segment. With the proposed method, the first-order mass moment of the body segment can be directly measured by using only one precision scale and one digital camera. In the dummy mass experiment, the relative standard uncertainty of a single set of measurements of the first-order mass moment is estimated to be 1.7%. The measured value will be useful as a reference for evaluating the uncertainty of the body segment inertial parameters (BSPs) estimated using an indirect method.

Estimating two indirect logging costs caused by accelerated erosion.

Treesearch

Glen O. Klock

1976-01-01

In forest areas where high soil erosion potential exists, a comparative yarding cost estimate, including the indirect costs determined by methods proposed here, shows that the total cost of using "advanced" logging methods may be less than that of "traditional" systems.

Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images †.

PubMed

Lee, Yeongjun; Choi, Jinwoo; Ko, Nak Yong; Choi, Hyun-Taek

2017-08-24

This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status-i.e., the existence and identity (or name)-of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods-particle filtering and Bayesian feature estimation-are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented.

Quantile Regression for Recurrent Gap Time Data

PubMed Central

Luo, Xianghua; Huang, Chiung-Yu; Wang, Lan

2014-01-01

Summary Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this article, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301–311), we extend the martingale-based estimating equation method considered by Peng and Huang (2008, Journal of the American Statistical Association 103, 637–649) for univariate survival data to analyze recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented to illustrate the methods developed in this article. PMID:23489055

Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments.

PubMed

Choi, Ji Yeh; Hwang, Heungsun; Timmerman, Marieke E

2018-03-01

Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with x- and y-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.

An Approach to Addressing Selection Bias in Survival Analysis

PubMed Central

Carlin, Caroline S.; Solid, Craig A.

2014-01-01

This work proposes a frailty model that accounts for non-random treatment assignment in survival analysis. Using Monte Carlo simulation, we found that estimated treatment parameters from our proposed endogenous selection survival model (esSurv) closely parallel the consistent two-stage residual inclusion (2SRI) results, while offering computational and interpretive advantages. The esSurv method greatly enhances computational speed relative to 2SRI by eliminating the need for bootstrapped standard errors, and generally results in smaller standard errors than those estimated by 2SRI. In addition, esSurv explicitly estimates the correlation of unobservable factors contributing to both treatment assignment and the outcome of interest, providing an interpretive advantage over the residual parameter estimate in the 2SRI method. Comparisons with commonly used propensity score methods and with a model that does not account for non-random treatment assignment show clear bias in these methods that is not mitigated by increased sample size. We illustrate using actual dialysis patient data comparing mortality of patients with mature arteriovenous grafts for venous access to mortality of patients with grafts placed but not yet ready for use at the initiation of dialysis. We find strong evidence of endogeneity (with estimate of correlation in unobserved factors ρ̂ = 0.55), and estimate a mature-graft hazard ratio of 0.197 in our proposed method, with a similar 0.173 hazard ratio using 2SRI. The 0.630 hazard ratio from a frailty model without a correction for the non-random nature of treatment assignment illustrates the importance of accounting for endogeneity. PMID:24845211

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

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.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

Simultaneous estimation of cross-validation errors in least squares collocation applied for statistical testing and evaluation of the noise variance components

NASA Astrophysics Data System (ADS)

Behnabian, Behzad; Mashhadi Hossainali, Masoud; Malekzadeh, Ahad

2018-02-01

The cross-validation technique is a popular method to assess and improve the quality of prediction by least squares collocation (LSC). We present a formula for direct estimation of the vector of cross-validation errors (CVEs) in LSC which is much faster than element-wise CVE computation. We show that a quadratic form of CVEs follows Chi-squared distribution. Furthermore, a posteriori noise variance factor is derived by the quadratic form of CVEs. In order to detect blunders in the observations, estimated standardized CVE is proposed as the test statistic which can be applied when noise variances are known or unknown. We use LSC together with the methods proposed in this research for interpolation of crustal subsidence in the northern coast of the Gulf of Mexico. The results show that after detection and removing outliers, the root mean square (RMS) of CVEs and estimated noise standard deviation are reduced about 51 and 59%, respectively. In addition, RMS of LSC prediction error at data points and RMS of estimated noise of observations are decreased by 39 and 67%, respectively. However, RMS of LSC prediction error on a regular grid of interpolation points covering the area is only reduced about 4% which is a consequence of sparse distribution of data points for this case study. The influence of gross errors on LSC prediction results is also investigated by lower cutoff CVEs. It is indicated that after elimination of outliers, RMS of this type of errors is also reduced by 19.5% for a 5 km radius of vicinity. We propose a method using standardized CVEs for classification of dataset into three groups with presumed different noise variances. The noise variance components for each of the groups are estimated using restricted maximum-likelihood method via Fisher scoring technique. Finally, LSC assessment measures were computed for the estimated heterogeneous noise variance model and compared with those of the homogeneous model. The advantage of the proposed method is the reduction in estimated noise levels for those groups with the fewer number of noisy data points.

Tuning the control system of a nonlinear inverted pendulum by means of the new method of Lyapunov exponents estimation

NASA Astrophysics Data System (ADS)

Balcerzak, Marek; Dąbrowski, Artur; Pikunov, Danylo

2018-01-01

This paper presents a practical application of a new, simplified method of Lyapunov exponents estimation. The method has been applied to optimization of a real, nonlinear inverted pendulum system. Authors presented how the algorithm of the Largest Lyapunov Exponent (LLE) estimation can be applied to evaluate control systems performance. The new LLE-based control performance index has been proposed. Equations of the inverted pendulum system of the fourth order have been found. The nonlinear friction of the regulation object has been identified by means of the nonlinear least squares method. Three different friction models have been tested: linear, cubic and Coulomb model. The Differential Evolution (DE) algorithm has been used to search for the best set of parameters of the general linear regulator. This work proves that proposed method is efficient and results in faster perturbation rejection, especially when disturbances are significant.

PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins

PubMed Central

Hussein, Hiba Abi; Borrel, Alexandre; Geneix, Colette; Petitjean, Michel; Regad, Leslie; Camproux, Anne-Claude

2015-01-01

Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr. PMID:25956651

PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins.

PubMed

Hussein, Hiba Abi; Borrel, Alexandre; Geneix, Colette; Petitjean, Michel; Regad, Leslie; Camproux, Anne-Claude

2015-07-01

Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose 'PockDrug-Server' to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Regression without truth with Markov chain Monte-Carlo

NASA Astrophysics Data System (ADS)

Madan, Hennadii; Pernuš, Franjo; Likar, Boštjan; Å piclin, Žiga

2017-03-01

Regression without truth (RWT) is a statistical technique for estimating error model parameters of each method in a group of methods used for measurement of a certain quantity. A very attractive aspect of RWT is that it does not rely on a reference method or "gold standard" data, which is otherwise difficult RWT was used for a reference-free performance comparison of several methods for measuring left ventricular ejection fraction (EF), i.e. a percentage of blood leaving the ventricle each time the heart contracts, and has since been applied for various other quantitative imaging biomarkerss (QIBs). Herein, we show how Markov chain Monte-Carlo (MCMC), a computational technique for drawing samples from a statistical distribution with probability density function known only up to a normalizing coefficient, can be used to augment RWT to gain a number of important benefits compared to the original approach based on iterative optimization. For instance, the proposed MCMC-based RWT enables the estimation of joint posterior distribution of the parameters of the error model, straightforward quantification of uncertainty of the estimates, estimation of true value of the measurand and corresponding credible intervals (CIs), does not require a finite support for prior distribution of the measureand generally has a much improved robustness against convergence to non-global maxima. The proposed approach is validated using synthetic data that emulate the EF data for 45 patients measured with 8 different methods. The obtained results show that 90% CI of the corresponding parameter estimates contain the true values of all error model parameters and the measurand. A potential real-world application is to take measurements of a certain QIB several different methods and then use the proposed framework to compute the estimates of the true values and their uncertainty, a vital information for diagnosis based on QIB.

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.

EEG Characteristic Extraction Method of Listening Music and Objective Estimation Method Based on Latency Structure Model in Individual Characteristics

NASA Astrophysics Data System (ADS)

Ito, Shin-Ichi; Mitsukura, Yasue; Nakamura Miyamura, Hiroko; Saito, Takafumi; Fukumi, Minoru

EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method.

Modal smoothing for analysis of room reflections measured with spherical microphone and loudspeaker arrays.

PubMed

Morgenstern, Hai; Rafaely, Boaz

2018-02-01

Spatial analysis of room acoustics is an ongoing research topic. Microphone arrays have been employed for spatial analyses with an important objective being the estimation of the direction-of-arrival (DOA) of direct sound and early room reflections using room impulse responses (RIRs). An optimal method for DOA estimation is the multiple signal classification algorithm. When RIRs are considered, this method typically fails due to the correlation of room reflections, which leads to rank deficiency of the cross-spectrum matrix. Preprocessing methods for rank restoration, which may involve averaging over frequency, for example, have been proposed exclusively for spherical arrays. However, these methods fail in the case of reflections with equal time delays, which may arise in practice and could be of interest. In this paper, a method is proposed for systems that combine a spherical microphone array and a spherical loudspeaker array, referred to as multiple-input multiple-output systems. This method, referred to as modal smoothing, exploits the additional spatial diversity for rank restoration and succeeds where previous methods fail, as demonstrated in a simulation study. Finally, combining modal smoothing with a preprocessing method is proposed in order to increase the number of DOAs that can be estimated using low-order spherical loudspeaker arrays.

State estimation with incomplete nonlinear constraint

NASA Astrophysics Data System (ADS)

Huang, Yuan; Wang, Xueying; An, Wei

2017-10-01

A problem of state estimation with a new constraints named incomplete nonlinear constraint is considered. The targets are often move in the curve road, if the width of road is neglected, the road can be considered as the constraint, and the position of sensors, e.g., radar, is known in advance, this info can be used to enhance the performance of the tracking filter. The problem of how to incorporate the priori knowledge is considered. In this paper, a second-order sate constraint is considered. A fitting algorithm of ellipse is adopted to incorporate the priori knowledge by estimating the radius of the trajectory. The fitting problem is transformed to the nonlinear estimation problem. The estimated ellipse function is used to approximate the nonlinear constraint. Then, the typical nonlinear constraint methods proposed in recent works can be used to constrain the target state. Monte-Carlo simulation results are presented to illustrate the effectiveness proposed method in state estimation with incomplete constraint.

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.

Method and system for diagnostics of apparatus

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry (Inventor)

2012-01-01

Proposed is a method, implemented in software, for estimating fault state of an apparatus outfitted with sensors. At each execution period the method processes sensor data from the apparatus to obtain a set of parity parameters, which are further used for estimating fault state. The estimation method formulates a convex optimization problem for each fault hypothesis and employs a convex solver to compute fault parameter estimates and fault likelihoods for each fault hypothesis. The highest likelihoods and corresponding parameter estimates are transmitted to a display device or an automated decision and control system. The obtained accurate estimate of fault state can be used to improve safety, performance, or maintenance processes for the apparatus.

A proposed standard methodology for estimating the wounding capacity of small calibre projectiles or other missiles.

PubMed

Berlin, R H; Janzon, B; Rybeck, B; Schantz, B; Seeman, T

1982-01-01

A standard methodology for estimating the energy transfer characteristics of small calibre bullets and other fast missiles is proposed, consisting of firings against targets made of soft soap. The target is evaluated by measuring the size of the permanent cavity remaining in it after the shot. The method is very simple to use and does not require access to any sophisticated measuring equipment. It can be applied under all circumstances, even under field conditions. Adequate methods of calibration to ensure good accuracy are suggested. The precision and limitations of the method are discussed.

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 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.

Robust PV Degradation Methodology and Application

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

Jordan, Dirk; Deline, Christopher A; Kurtz, Sarah

The degradation rate plays an important role in predicting and assessing the long-term energy generation of PV systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this manuscript, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year (YOY) rate calculation. We show the method to provide reliable degradation rate estimates even in the case ofmore » sensor drift, data shifts, and soiling. Compared with alternate methods, we demonstrate that the proposed method delivers the lowest uncertainty in degradation rate estimates for a fleet of 486 PV systems.« less

Robust PV Degradation Methodology and Application

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

Jordan, Dirk C.; Deline, Chris; Kurtz, Sarah R.

The degradation rate plays an important role in predicting and assessing the long-term energy generation of photovoltaics (PV) systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this paper, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year rate calculation. We show the method to provide reliable degradation rate estimates even in the case ofmore » sensor drift, data shifts, and soiling. Compared with alternate methods, we demonstrate that the proposed method delivers the lowest uncertainty in degradation rate estimates for a fleet of 486 PV systems.« less

Robust PV Degradation Methodology and Application

DOE PAGES

Jordan, Dirk C.; Deline, Chris; Kurtz, Sarah R.; ...

2017-12-21

The degradation rate plays an important role in predicting and assessing the long-term energy generation of photovoltaics (PV) systems. Many methods have been proposed for extracting the degradation rate from operational data of PV systems, but most of the published approaches are susceptible to bias due to inverter clipping, module soiling, temporary outages, seasonality, and sensor degradation. In this paper, we propose a methodology for determining PV degradation leveraging available modeled clear-sky irradiance data rather than site sensor data, and a robust year-over-year rate calculation. We show the method to provide reliable degradation rate estimates even in the case ofmore » sensor drift, data shifts, and soiling. Compared with alternate methods, we demonstrate that the proposed method delivers the lowest uncertainty in degradation rate estimates for a fleet of 486 PV systems.« less

Total generalized variation-regularized variational model for single image dehazing

NASA Astrophysics Data System (ADS)

Shu, Qiao-Ling; Wu, Chuan-Sheng; Zhong, Qiu-Xiang; Liu, Ryan Wen

2018-04-01

Imaging quality is often significantly degraded under hazy weather condition. The purpose of this paper is to recover the latent sharp image from its hazy version. It is well known that the accurate estimation of depth information could assist in improving dehazing performance. In this paper, a detail-preserving variational model was proposed to simultaneously estimate haze-free image and depth map. In particular, the total variation (TV) and total generalized variation (TGV) regularizers were introduced to restrain haze-free image and depth map, respectively. The resulting nonsmooth optimization problem was efficiently solved using the alternating direction method of multipliers (ADMM). Comprehensive experiments have been conducted on realistic datasets to compare our proposed method with several state-of-the-art dehazing methods. Results have illustrated the superior performance of the proposed method in terms of visual quality evaluation.

Three-Dimensional ISAR Imaging Method for High-Speed Targets in Short-Range Using Impulse Radar Based on SIMO Array.

PubMed

Zhou, Xinpeng; Wei, Guohua; Wu, Siliang; Wang, Dawei

2016-03-11

This paper proposes a three-dimensional inverse synthetic aperture radar (ISAR) imaging method for high-speed targets in short-range using an impulse radar. According to the requirements for high-speed target measurement in short-range, this paper establishes the single-input multiple-output (SIMO) antenna array, and further proposes a missile motion parameter estimation method based on impulse radar. By analyzing the motion geometry relationship of the warhead scattering center after translational compensation, this paper derives the receiving antenna position and the time delay after translational compensation, and thus overcomes the shortcomings of conventional translational compensation methods. By analyzing the motion characteristics of the missile, this paper estimates the missile's rotation angle and the rotation matrix by establishing a new coordinate system. Simulation results validate the performance of the proposed algorithm.

Fast Image Restoration for Spatially Varying Defocus Blur of Imaging Sensor

PubMed Central

Cheong, Hejin; Chae, Eunjung; Lee, Eunsung; Jo, Gwanghyun; Paik, Joonki

2015-01-01

This paper presents a fast adaptive image restoration method for removing spatially varying out-of-focus blur of a general imaging sensor. After estimating the parameters of space-variant point-spread-function (PSF) using the derivative in each uniformly blurred region, the proposed method performs spatially adaptive image restoration by selecting the optimal restoration filter according to the estimated blur parameters. Each restoration filter is implemented in the form of a combination of multiple FIR filters, which guarantees the fast image restoration without the need of iterative or recursive processing. Experimental results show that the proposed method outperforms existing space-invariant restoration methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed to a wide area of image restoration applications, such as mobile imaging devices, robot vision, and satellite image processing. PMID:25569760

An improved parameter estimation scheme for image modification detection based on DCT coefficient analysis.

PubMed

Yu, Liyang; Han, Qi; Niu, Xiamu; Yiu, S M; Fang, Junbin; Zhang, Ye

2016-02-01

Most of the existing image modification detection methods which are based on DCT coefficient analysis model the distribution of DCT coefficients as a mixture of a modified and an unchanged component. To separate the two components, two parameters, which are the primary quantization step, Q1, and the portion of the modified region, α, have to be estimated, and more accurate estimations of α and Q1 lead to better detection and localization results. Existing methods estimate α and Q1 in a completely blind manner, without considering the characteristics of the mixture model and the constraints to which α should conform. In this paper, we propose a more effective scheme for estimating α and Q1, based on the observations that, the curves on the surface of the likelihood function corresponding to the mixture model is largely smooth, and α can take values only in a discrete set. We conduct extensive experiments to evaluate the proposed method, and the experimental results confirm the efficacy of our method. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Estimation of uncertainty in tracer gas measurement of air change rates.

PubMed

Iizuka, Atsushi; Okuizumi, Yumiko; Yanagisawa, Yukio

2010-12-01

Simple and economical measurement of air change rates can be achieved with a passive-type tracer gas doser and sampler. However, this is made more complex by the fact many buildings are not a single fully mixed zone. This means many measurements are required to obtain information on ventilation conditions. In this study, we evaluated the uncertainty of tracer gas measurement of air change rate in n completely mixed zones. A single measurement with one tracer gas could be used to simply estimate the air change rate when n = 2. Accurate air change rates could not be obtained for n ≥ 2 due to a lack of information. However, the proposed method can be used to estimate an air change rate with an accuracy of <33%. Using this method, overestimation of air change rate can be avoided. The proposed estimation method will be useful in practical ventilation measurements.

Using State Estimation Residuals to Detect Abnormal SCADA Data

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

Ma, Jian; Chen, Yousu; Huang, Zhenyu

2010-04-30

Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm ismore » applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.« less

Estimating effect of environmental contaminants on women's subfecundity for the MoBa study data with an outcome-dependent sampling scheme

PubMed Central

Ding, Jieli; Zhou, Haibo; Liu, Yanyan; Cai, Jianwen; Longnecker, Matthew P.

2014-01-01

Motivated by the need from our on-going environmental study in the Norwegian Mother and Child Cohort (MoBa) study, we consider an outcome-dependent sampling (ODS) scheme for failure-time data with censoring. Like the case-cohort design, the ODS design enriches the observed sample by selectively including certain failure subjects. We present an estimated maximum semiparametric empirical likelihood estimation (EMSELE) under the proportional hazards model framework. The asymptotic properties of the proposed estimator were derived. Simulation studies were conducted to evaluate the small-sample performance of our proposed method. Our analyses show that the proposed estimator and design is more efficient than the current default approach and other competing approaches. Applying the proposed approach with the data set from the MoBa study, we found a significant effect of an environmental contaminant on fecundability. PMID:24812419

A general probabilistic model for group independent component analysis and its estimation methods

PubMed Central

Guo, Ying

2012-01-01

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods. PMID:21517789

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.

Estimation of Subjective Mental Work Load Level with Heart Rate Variability by Tolerance to Driver's Mental Load

NASA Astrophysics Data System (ADS)

Yokoi, Toshiyuki; Itoh, Michimasa; Oguri, Koji

Most of the traffic accidents have been caused by inappropriate driver's mental state. Therefore, driver monitoring is one of the most important challenges to prevent traffic accidents. Some studies for evaluating the driver's mental state while driving have been reported; however driver's mental state should be estimated in real-time in the future. This paper proposes a way to estimate quantitatively driver's mental workload using heart rate variability. It is assumed that the tolerance to driver's mental workload is different depending on the individual. Therefore, we classify people based on their individual tolerance to mental workload. Our estimation method is multiple linear regression analysis, and we compare it to NASA-TLX which is used as the evaluation method of subjective mental workload. As a result, the coefficient of correlation improved from 0.83 to 0.91, and the standard deviation of error also improved. Therefore, our proposed method demonstrated the possibility to estimate mental workload.

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions.

PubMed

Drouard, Vincent; Horaud, Radu; Deleforge, Antoine; Ba, Sileye; Evangelidis, Georgios

2017-03-01

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.