An algorithm to estimate aircraft cruise black carbon emissions for use in developing a cruise emissions inventory.

PubMed

Peck, Jay; Oluwole, Oluwayemisi O; Wong, Hsi-Wu; Miake-Lye, Richard C

2013-03-01

To provide accurate input parameters to the large-scale global climate simulation models, an algorithm was developed to estimate the black carbon (BC) mass emission index for engines in the commercial fleet at cruise. Using a high-dimensional model representation (HDMR) global sensitivity analysis, relevant engine specification/operation parameters were ranked, and the most important parameters were selected. Simple algebraic formulas were then constructed based on those important parameters. The algorithm takes the cruise power (alternatively, fuel flow rate), altitude, and Mach number as inputs, and calculates BC emission index for a given engine/airframe combination using the engine property parameters, such as the smoke number, available in the International Civil Aviation Organization (ICAO) engine certification databank. The algorithm can be interfaced with state-of-the-art aircraft emissions inventory development tools, and will greatly improve the global climate simulations that currently use a single fleet average value for all airplanes. An algorithm to estimate the cruise condition black carbon emission index for commercial aircraft engines was developed. Using the ICAO certification data, the algorithm can evaluate the black carbon emission at given cruise altitude and speed.

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

EPA Science Inventory

Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...

A fast, robust algorithm for power line interference cancellation in neural recording.

PubMed

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.

A fast, robust algorithm for power line interference cancellation in neural recording

NASA Astrophysics Data System (ADS)

Keshtkaran, Mohammad Reza; Yang, Zhi

2014-04-01

Objective. Power line interference may severely corrupt neural recordings at 50/60 Hz and harmonic frequencies. The interference is usually non-stationary and can vary in frequency, amplitude and phase. To retrieve the gamma-band oscillations at the contaminated frequencies, it is desired to remove the interference without compromising the actual neural signals at the interference frequency bands. In this paper, we present a robust and computationally efficient algorithm for removing power line interference from neural recordings. Approach. The algorithm includes four steps. First, an adaptive notch filter is used to estimate the fundamental frequency of the interference. Subsequently, based on the estimated frequency, harmonics are generated by using discrete-time oscillators, and then the amplitude and phase of each harmonic are estimated by using a modified recursive least squares algorithm. Finally, the estimated interference is subtracted from the recorded data. Main results. The algorithm does not require any reference signal, and can track the frequency, phase and amplitude of each harmonic. When benchmarked with other popular approaches, our algorithm performs better in terms of noise immunity, convergence speed and output signal-to-noise ratio (SNR). While minimally affecting the signal bands of interest, the algorithm consistently yields fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) in different conditions of interference strengths (input SNR from -30 to 30 dB), power line frequencies (45-65 Hz) and phase and amplitude drifts. In addition, the algorithm features a straightforward parameter adjustment since the parameters are independent of the input SNR, input signal power and the sampling rate. A hardware prototype was fabricated in a 65 nm CMOS process and tested. Software implementation of the algorithm has been made available for open access at https://github.com/mrezak/removePLI. Significance. The proposed algorithm features a highly robust operation, fast adaptation to interference variations, significant SNR improvement, low computational complexity and memory requirement and straightforward parameter adjustment. These features render the algorithm suitable for wearable and implantable sensor applications, where reliable and real-time cancellation of the interference is desired.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system

NASA Technical Reports Server (NTRS)

Choudhary, Alok Nidhi; Leung, Mun K.; Huang, Thomas S.; Patel, Janak H.

1989-01-01

Computer vision systems employ a sequence of vision algorithms in which the output of an algorithm is the input of the next algorithm in the sequence. Algorithms that constitute such systems exhibit vastly different computational characteristics, and therefore, require different data decomposition techniques and efficient load balancing techniques for parallel implementation. However, since the input data for a task is produced as the output data of the previous task, this information can be exploited to perform knowledge based data decomposition and load balancing. Presented here are algorithms for a motion estimation system. The motion estimation is based on the point correspondence between the involved images which are a sequence of stereo image pairs. Researchers propose algorithms to obtain point correspondences by matching feature points among stereo image pairs at any two consecutive time instants. Furthermore, the proposed algorithms employ non-iterative procedures, which results in saving considerable amounts of computation time. The system consists of the following steps: (1) extraction of features; (2) stereo match of images in one time instant; (3) time match of images from consecutive time instants; (4) stereo match to compute final unambiguous points; and (5) computation of motion parameters.

NOSS altimeter algorithm specifications

NASA Technical Reports Server (NTRS)

Hancock, D. W.; Forsythe, R. G.; Mcmillan, J. D.

1982-01-01

A description of all algorithms required for altimeter processing is given. Each description includes title, description, inputs/outputs, general algebraic sequences and data volume. All required input/output data files are described and the computer resources required for the entire altimeter processing system were estimated. The majority of the data processing requirements for any radar altimeter of the Seasat-1 type are scoped. Additions and deletions could be made for the specific altimeter products required by other projects.

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

Zawisza, I; Yan, H; Yin, F

Purpose: To assure that tumor motion is within the radiation field during high-dose and high-precision radiosurgery, real-time imaging and surrogate monitoring are employed. These methods are useful in providing real-time tumor/surrogate motion but no future information is available. In order to anticipate future tumor/surrogate motion and track target location precisely, an algorithm is developed and investigated for estimating surrogate motion multiple-steps ahead. Methods: The study utilized a one-dimensional surrogate motion signal divided into three components: (a) training component containing the primary data including the first frame to the beginning of the input subsequence; (b) input subsequence component of the surrogatemore » signal used as input to the prediction algorithm: (c) output subsequence component is the remaining signal used as the known output of the prediction algorithm for validation. The prediction algorithm consists of three major steps: (1) extracting subsequences from training component which best-match the input subsequence according to given criterion; (2) calculating weighting factors from these best-matched subsequence; (3) collecting the proceeding parts of the subsequences and combining them together with assigned weighting factors to form output. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation between prediction and known output. Results: Respiratory motion data was collected for 20 patients using the RPM system. The output subsequence is the last 50 samples (∼2 seconds) of a surrogate signal, and the input subsequence was 100 (∼3 seconds) frames prior to the output subsequence. Based on the analysis of correlation coefficient between predicted and known output subsequence, the average correlation is 0.9644±0.0394 and 0.9789±0.0239 for equal-weighting and relative-weighting strategies, respectively. Conclusion: Preliminary results indicate that the prediction algorithm is effective in estimating surrogate motion multiple-steps in advance. Relative-weighting method shows better prediction accuracy than equal-weighting method. More parameters of this algorithm are under investigation.« less

Predicting Loss-of-Control Boundaries Toward a Piloting Aid

NASA Technical Reports Server (NTRS)

Barlow, Jonathan; Stepanyan, Vahram; Krishnakumar, Kalmanje

2012-01-01

This work presents an approach to predicting loss-of-control with the goal of providing the pilot a decision aid focused on maintaining the pilot's control action within predicted loss-of-control boundaries. The predictive architecture combines quantitative loss-of-control boundaries, a data-based predictive control boundary estimation algorithm and an adaptive prediction method to estimate Markov model parameters in real-time. The data-based loss-of-control boundary estimation algorithm estimates the boundary of a safe set of control inputs that will keep the aircraft within the loss-of-control boundaries for a specified time horizon. The adaptive prediction model generates estimates of the system Markov Parameters, which are used by the data-based loss-of-control boundary estimation algorithm. The combined algorithm is applied to a nonlinear generic transport aircraft to illustrate the features of the architecture.

An Error-Entropy Minimization Algorithm for Tracking Control of Nonlinear Stochastic Systems with Non-Gaussian Variables

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

Liu, Yunlong; Wang, Aiping; Guo, Lei

This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.

A digital combining-weight estimation algorithm for broadband sources with the array feed compensation system

NASA Technical Reports Server (NTRS)

Vilnrotter, V. A.; Rodemich, E. R.

1994-01-01

An algorithm for estimating the optimum combining weights for the Ka-band (33.7-GHz) array feed compensation system was developed and analyzed. The input signal is assumed to be broadband radiation of thermal origin, generated by a distant radio source. Currently, seven video converters operating in conjunction with the real-time correlator are used to obtain these weight estimates. The algorithm described here requires only simple operations that can be implemented on a PC-based combining system, greatly reducing the amount of hardware. Therefore, system reliability and portability will be improved.

Application of neural based estimation algorithm for gait phases of above knee prosthesis.

PubMed

Tileylioğlu, E; Yilmaz, A

2015-01-01

In this study, two gait phase estimation methods which utilize a rule based quantization and an artificial neural network model respectively are developed and applied for the microcontroller based semi-active knee prosthesis in order to respond user demands and adapt environmental conditions. In this context, an experimental environment in which gait data collected synchronously from both inertial and image based measurement systems has been set up. The inertial measurement system that incorporates MEM accelerometers and gyroscopes is used to perform direct motion measurement through the microcontroller, while the image based measurement system is employed for producing the verification data and assessing the success of the prosthesis. Embedded algorithms dynamically normalize the input data prior to gait phase estimation. The real time analyses of two methods revealed that embedded ANN based approach performs slightly better in comparison with the rule based algorithm and has advantage of being easily-scalable, thus able to accommodate additional input parameters considering the microcontroller constraints.

Ocean Primary Production Estimates from Terra MODIS and Their Dependency on Satellite Chlorophyll Alpha Algorithms

NASA Technical Reports Server (NTRS)

Essias, Wayne E.; Abbott, Mark; Carder, Kendall; Campbell, Janet; Clark, Dennis; Evans, Robert; Brown, Otis; Kearns, Ed; Kilpatrick, Kay; Balch, W.

2003-01-01

Simplistic models relating global satellite ocean color, temperature, and light to ocean net primary production (ONPP) are sensitive to the accuracy and limitations of the satellite estimate of chlorophyll and other input fields, as well as the primary productivity model. The standard MODIS ONPP product uses the new semi-analytic chlorophyll algorithm as its input for two ONPP indexes. The three primary MODIS chlorophyll Q estimates from MODIS, as well as the SeaWiFS 4 chlorophyll product, were used to assess global and regional performance in estimating ONPP for the full mission, but concentrating on 2001. The two standard ONPP algorithms were examined with 8-day and 39 kilometer resolution to quantify chlorophyll algorithm dependency of ONPP. Ancillary data (MLD from FNMOC, MODIS SSTD1, and PAR from the GSFC DAO) were identical. The standard MODIS ONPP estimates for annual production in 2001 was 59 and 58 GT C for the two ONPP algorithms. Differences in ONPP using alternate chlorophylls were on the order of 10% for global annual ONPP, but ranged to 100% regionally. On all scales the differences in ONPP were smaller between MODIS and SeaWiFS than between ONPP models, or among chlorophyll algorithms within MODIS. Largest regional ONPP differences were found in the Southern Ocean (SO). In the SO, application of the semi-analytic chlorophyll resulted in not only a magnitude difference in ONPP (2x), but also a temporal shift in the time of maximum production compared to empirical algorithms when summed over standard oceanic areas. The resulting increase in global ONPP (6-7 GT) is supported by better performance of the semi-analytic chlorophyll in the SO and other high chlorophyll regions. The differences are significant in terms of understanding regional differences and dynamics of ocean carbon transformations.

Vehicle longitudinal velocity estimation during the braking process using unknown input Kalman filter

NASA Astrophysics Data System (ADS)

Moaveni, Bijan; Khosravi Roqaye Abad, Mahdi; Nasiri, Sayyad

2015-10-01

In this paper, vehicle longitudinal velocity during the braking process is estimated by measuring the wheels speed. Here, a new algorithm based on the unknown input Kalman filter is developed to estimate the vehicle longitudinal velocity with a minimum mean square error and without using the value of braking torque in the estimation procedure. The stability and convergence of the filter are analysed and proved. Effectiveness of the method is shown by designing a real experiment and comparing the estimation result with actual longitudinal velocity computing from a three-axis accelerometer output.

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

Na, Man Gyun; Oh, Seungrohk

A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce themore » time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.« less

Variable is better than invariable: sparse VSS-NLMS algorithms with application to adaptive MIMO channel estimation.

PubMed

Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

2014-01-01

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.

Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation

PubMed Central

Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

2014-01-01

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286

A digital prediction algorithm for a single-phase boost PFC

NASA Astrophysics Data System (ADS)

Qing, Wang; Ning, Chen; Weifeng, Sun; Shengli, Lu; Longxing, Shi

2012-12-01

A novel digital control algorithm for digital control power factor correction is presented, which is called the prediction algorithm and has a feature of a higher PF (power factor) with lower total harmonic distortion, and a faster dynamic response with the change of the input voltage or load current. For a certain system, based on the current system state parameters, the prediction algorithm can estimate the track of the output voltage and the inductor current at the next switching cycle and get a set of optimized control sequences to perfectly track the trajectory of input voltage. The proposed prediction algorithm is verified at different conditions, and computer simulation and experimental results under multi-situations confirm the effectiveness of the prediction algorithm. Under the circumstances that the input voltage is in the range of 90-265 V and the load current in the range of 20%-100%, the PF value is larger than 0.998. The startup and the recovery times respectively are about 0.1 s and 0.02 s without overshoot. The experimental results also verify the validity of the proposed method.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Estimation of parameters in Shot-Noise-Driven Doubly Stochastic Poisson processes using the EM algorithm--modeling of pre- and postsynaptic spike trains.

PubMed

Mino, H

2007-01-01

To estimate the parameters, the impulse response (IR) functions of some linear time-invariant systems generating intensity processes, in Shot-Noise-Driven Doubly Stochastic Poisson Process (SND-DSPP) in which multivariate presynaptic spike trains and postsynaptic spike trains can be assumed to be modeled by the SND-DSPPs. An explicit formula for estimating the IR functions from observations of multivariate input processes of the linear systems and the corresponding counting process (output process) is derived utilizing the expectation maximization (EM) algorithm. The validity of the estimation formula was verified through Monte Carlo simulations in which two presynaptic spike trains and one postsynaptic spike train were assumed to be observable. The IR functions estimated on the basis of the proposed identification method were close to the true IR functions. The proposed method will play an important role in identifying the input-output relationship of pre- and postsynaptic neural spike trains in practical situations.

Computationally efficient algorithms for real-time attitude estimation

NASA Technical Reports Server (NTRS)

Pringle, Steven R.

1993-01-01

For many practical spacecraft applications, algorithms for determining spacecraft attitude must combine inputs from diverse sensors and provide redundancy in the event of sensor failure. A Kalman filter is suitable for this task, however, it may impose a computational burden which may be avoided by sub optimal methods. A suboptimal estimator is presented which was implemented successfully on the Delta Star spacecraft which performed a 9 month SDI flight experiment in 1989. This design sought to minimize algorithm complexity to accommodate the limitations of an 8K guidance computer. The algorithm used is interpreted in the framework of Kalman filtering and a derivation is given for the computation.

Technical Report Series on Global Modeling and Data Assimilation. Volume 12; Comparison of Satellite Global Rainfall Algorithms

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Chang, Alfred T. C.; Chiu, Long S.

1997-01-01

Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang, Kummerow, Prabhakara, Huffman, Spencer, Susskind, and Wu) were analyzed to examine the uncertainty of satellite-derived rainfall estimates. The variability among algorithms, measured as the standard deviation computed from the ensemble of algorithms, shows regions of high algorithm variability tend to coincide with regions of high rain rates. Histograms of pattern correlation (PC) between algorithms suggest a bimodal distribution, with separation at a PC-value of about 0.85. Applying this threshold as a criteria for similarity, our analyses show that algorithms using the same sensor or satellite input tend to be similar, suggesting the dominance of sampling errors in these satellite estimates.

Multi-Target Angle Tracking Algorithm for Bistatic Multiple-Input Multiple-Output (MIMO) Radar Based on the Elements of the Covariance Matrix.

PubMed

Zhang, Zhengyan; Zhang, Jianyun; Zhou, Qingsong; Li, Xiaobo

2018-03-07

In this paper, we consider the problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output (MIMO) radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD) algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

Effective channel estimation and efficient symbol detection for multi-input multi-output underwater acoustic communications

NASA Astrophysics Data System (ADS)

Ling, Jun

Achieving reliable underwater acoustic communications (UAC) has long been recognized as a challenging problem owing to the scarce bandwidth available and the reverberant spread in both time and frequency domains. To pursue high data rates, we consider a multi-input multi-output (MIMO) UAC system, and our focus is placed on two main issues regarding a MIMO UAC system: (1) channel estimation, which involves the design of the training sequences and the development of a reliable channel estimation algorithm, and (2) symbol detection, which requires interference cancelation schemes due to simultaneous transmission from multiple transducers. To enhance channel estimation performance, we present a cyclic approach for designing training sequences with good auto- and cross-correlation properties, and a channel estimation algorithm called the iterative adaptive approach (IAA). Sparse channel estimates can be obtained by combining IAA with the Bayesian information criterion (BIC). Moreover, we present sparse learning via iterative minimization (SLIM) and demonstrate that SLIM gives similar performance to IAA but at a much lower computational cost. Furthermore, an extension of the SLIM algorithm is introduced to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). Regarding symbol detection, a linear minimum mean-squared error based detection scheme, called RELAX-BLAST, which is a combination of vertical Bell Labs layered space-time (V-BLAST) algorithm and the cyclic principle of the RELAX algorithm, is presented and it is shown that RELAX-BLAST outperforms V-BLAST. We show that RELAX-BLAST can be implemented efficiently by making use of the conjugate gradient method and diagonalization properties of circulant matrices. This fast implementation approach requires only simple fast Fourier transform operations and facilitates parallel implementations. The effectiveness of the proposed MIMO schemes is verified by both computer simulations and experimental results obtained by analyzing the measurements acquired in multiple in-water experiments.

Quantification of regional myocardial blood flow estimation with three-dimensional dynamic rubidium-82 PET and modified spillover correction model.

PubMed

Katoh, Chietsugu; Yoshinaga, Keiichiro; Klein, Ran; Kasai, Katsuhiko; Tomiyama, Yuuki; Manabe, Osamu; Naya, Masanao; Sakakibara, Mamoru; Tsutsui, Hiroyuki; deKemp, Robert A; Tamaki, Nagara

2012-08-01

Myocardial blood flow (MBF) estimation with (82)Rubidium ((82)Rb) positron emission tomography (PET) is technically difficult because of the high spillover between regions of interest, especially due to the long positron range. We sought to develop a new algorithm to reduce the spillover in image-derived blood activity curves, using non-uniform weighted least-squares fitting. Fourteen volunteers underwent imaging with both 3-dimensional (3D) (82)Rb and (15)O-water PET at rest and during pharmacological stress. Whole left ventricular (LV) (82)Rb MBF was estimated using a one-compartment model, including a myocardium-to-blood spillover correction to estimate the corresponding blood input function Ca(t)(whole). Regional K1 values were calculated using this uniform global input function, which simplifies equations and enables robust estimation of MBF. To assess the robustness of the modified algorithm, inter-operator repeatability of 3D (82)Rb MBF was compared with a previously established method. Whole LV correlation of (82)Rb MBF with (15)O-water MBF was better (P < .01) with the modified spillover correction method (r = 0.92 vs r = 0.60). The modified method also yielded significantly improved inter-operator repeatability of regional MBF quantification (r = 0.89) versus the established method (r = 0.82) (P < .01). A uniform global input function can suppress LV spillover into the image-derived blood input function, resulting in improved precision for MBF quantification with 3D (82)Rb PET.

Flight data identification of six degree-of-freedom stability and control derivatives of a large crane type helicopter

NASA Technical Reports Server (NTRS)

Tomaine, R. L.

1976-01-01

Flight test data from a large 'crane' type helicopter were collected and processed for the purpose of identifying vehicle rigid body stability and control derivatives. The process consisted of using digital and Kalman filtering techniques for state estimation and Extended Kalman filtering for parameter identification, utilizing a least squares algorithm for initial derivative and variance estimates. Data were processed for indicated airspeeds from 0 m/sec to 152 m/sec. Pulse, doublet and step control inputs were investigated. Digital filter frequency did not have a major effect on the identification process, while the initial derivative estimates and the estimated variances had an appreciable effect on many derivative estimates. The major derivatives identified agreed fairly well with analytical predictions and engineering experience. Doublet control inputs provided better results than pulse or step inputs.

An open tool for input function estimation and quantification of dynamic PET FDG brain scans.

PubMed

Bertrán, Martín; Martínez, Natalia; Carbajal, Guillermo; Fernández, Alicia; Gómez, Álvaro

2016-08-01

Positron emission tomography (PET) analysis of clinical studies is mostly restricted to qualitative evaluation. Quantitative analysis of PET studies is highly desirable to be able to compute an objective measurement of the process of interest in order to evaluate treatment response and/or compare patient data. But implementation of quantitative analysis generally requires the determination of the input function: the arterial blood or plasma activity which indicates how much tracer is available for uptake in the brain. The purpose of our work was to share with the community an open software tool that can assist in the estimation of this input function, and the derivation of a quantitative map from the dynamic PET study. Arterial blood sampling during the PET study is the gold standard method to get the input function, but is uncomfortable and risky for the patient so it is rarely used in routine studies. To overcome the lack of a direct input function, different alternatives have been devised and are available in the literature. These alternatives derive the input function from the PET image itself (image-derived input function) or from data gathered from previous similar studies (population-based input function). In this article, we present ongoing work that includes the development of a software tool that integrates several methods with novel strategies for the segmentation of blood pools and parameter estimation. The tool is available as an extension to the 3D Slicer software. Tests on phantoms were conducted in order to validate the implemented methods. We evaluated the segmentation algorithms over a range of acquisition conditions and vasculature size. Input function estimation algorithms were evaluated against ground truth of the phantoms, as well as on their impact over the final quantification map. End-to-end use of the tool yields quantification maps with [Formula: see text] relative error in the estimated influx versus ground truth on phantoms. The main contribution of this article is the development of an open-source, free to use tool that encapsulates several well-known methods for the estimation of the input function and the quantification of dynamic PET FDG studies. Some alternative strategies are also proposed and implemented in the tool for the segmentation of blood pools and parameter estimation. The tool was tested on phantoms with encouraging results that suggest that even bloodless estimators could provide a viable alternative to blood sampling for quantification using graphical analysis. The open tool is a promising opportunity for collaboration among investigators and further validation on real studies.

A parameter estimation subroutine package

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Nead, M. W.

1978-01-01

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. In this report we document a library of FORTRAN subroutines that have been developed to facilitate analyses of a variety of estimation problems. Our purpose is to present an easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage. Subroutine inputs, outputs, usage and listings are given along with examples of how these routines can be used. The following outline indicates the scope of this report: Section (1) introduction with reference to background material; Section (2) examples and applications; Section (3) subroutine directory summary; Section (4) the subroutine directory user description with input, output, and usage explained; and Section (5) subroutine FORTRAN listings. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses.

Output-only modal dynamic identification of frames by a refined FDD algorithm at seismic input and high damping

NASA Astrophysics Data System (ADS)

Pioldi, Fabio; Ferrari, Rosalba; Rizzi, Egidio

2016-02-01

The present paper deals with the seismic modal dynamic identification of frame structures by a refined Frequency Domain Decomposition (rFDD) algorithm, autonomously formulated and implemented within MATLAB. First, the output-only identification technique is outlined analytically and then employed to characterize all modal properties. Synthetic response signals generated prior to the dynamic identification are adopted as input channels, in view of assessing a necessary condition for the procedure's efficiency. Initially, the algorithm is verified on canonical input from random excitation. Then, modal identification has been attempted successfully at given seismic input, taken as base excitation, including both strong motion data and single and multiple input ground motions. Rather than different attempts investigating the role of seismic response signals in the Time Domain, this paper considers the identification analysis in the Frequency Domain. Results turn-out very much consistent with the target values, with quite limited errors in the modal estimates, including for the damping ratios, ranging from values in the order of 1% to 10%. Either seismic excitation and high values of damping, resulting critical also in case of well-spaced modes, shall not fulfill traditional FFD assumptions: this shows the consistency of the developed algorithm. Through original strategies and arrangements, the paper shows that a comprehensive rFDD modal dynamic identification of frames at seismic input is feasible, also at concomitant high damping.

A modified PATH algorithm rapidly generates transition states comparable to those found by other well established algorithms

PubMed Central

Chandrasekaran, Srinivas Niranj; Das, Jhuma; Dokholyan, Nikolay V.; Carter, Charles W.

2016-01-01

PATH rapidly computes a path and a transition state between crystal structures by minimizing the Onsager-Machlup action. It requires input parameters whose range of values can generate different transition-state structures that cannot be uniquely compared with those generated by other methods. We outline modifications to estimate these input parameters to circumvent these difficulties and validate the PATH transition states by showing consistency between transition-states derived by different algorithms for unrelated protein systems. Although functional protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH. PMID:26958584

Early Examples from the Integrated Multi-Satellite Retrievals for GPM (IMERG)

NASA Astrophysics Data System (ADS)

Huffman, George; Bolvin, David; Braithwaite, Daniel; Hsu, Kuolin; Joyce, Robert; Kidd, Christopher; Sorooshian, Soroosh; Xie, Pingping

2014-05-01

The U.S. GPM Science Team's Day-1 algorithm for computing combined precipitation estimates as part of GPM is the Integrated Multi-satellitE Retrievals for GPM (IMERG). The goal is to compute the best time series of (nearly) global precipitation from "all" precipitation-relevant satellites and global surface precipitation gauge analyses. IMERG is being developed as a unified U.S. algorithm drawing on strengths in the three contributing groups, whose previous work includes: 1) the TRMM Multi-satellite Precipitation Analysis (TMPA); 2) the CPC Morphing algorithm with Kalman Filtering (K-CMORPH); and 3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS). We review the IMERG design and development, plans for testing, and current status. Some of the lessons learned in running and reprocessing the previous data sets include the importance of quality-controlling input data sets, strategies for coping with transitions in the various input data sets, and practical approaches to retrospective analysis of multiple output products (namely the real- and post-real-time data streams). IMERG output will be illustrated using early test data, including the variety of supporting fields, such as the merged-microwave and infrared estimates, and the precipitation type. We end by considering recent changes in input data specifications, the transition from TRMM-based calibration to GPM-based, and further "Day 2" development.

Tele-Autonomous control involving contact. Final Report Thesis; [object localization

NASA Technical Reports Server (NTRS)

Shao, Lejun; Volz, Richard A.; Conway, Lynn; Walker, Michael W.

1990-01-01

Object localization and its application in tele-autonomous systems are studied. Two object localization algorithms are presented together with the methods of extracting several important types of object features. The first algorithm is based on line-segment to line-segment matching. Line range sensors are used to extract line-segment features from an object. The extracted features are matched to corresponding model features to compute the location of the object. The inputs of the second algorithm are not limited only to the line features. Featured points (point to point matching) and featured unit direction vectors (vector to vector matching) can also be used as the inputs of the algorithm, and there is no upper limit on the number of the features inputed. The algorithm will allow the use of redundant features to find a better solution. The algorithm uses dual number quaternions to represent the position and orientation of an object and uses the least squares optimization method to find an optimal solution for the object's location. The advantage of using this representation is that the method solves for the location estimation by minimizing a single cost function associated with the sum of the orientation and position errors and thus has a better performance on the estimation, both in accuracy and speed, than that of other similar algorithms. The difficulties when the operator is controlling a remote robot to perform manipulation tasks are also discussed. The main problems facing the operator are time delays on the signal transmission and the uncertainties of the remote environment. How object localization techniques can be used together with other techniques such as predictor display and time desynchronization to help to overcome these difficulties are then discussed.

Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

NASA Astrophysics Data System (ADS)

Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-01-01

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

On the role of dimensionality and sample size for unstructured and structured covariance matrix estimation

NASA Technical Reports Server (NTRS)

Morgera, S. D.; Cooper, D. B.

1976-01-01

The experimental observation that a surprisingly small sample size vis-a-vis dimension is needed to achieve good signal-to-interference ratio (SIR) performance with an adaptive predetection filter is explained. The adaptive filter requires estimates as obtained by a recursive stochastic algorithm of the inverse of the filter input data covariance matrix. The SIR performance with sample size is compared for the situations where the covariance matrix estimates are of unstructured (generalized) form and of structured (finite Toeplitz) form; the latter case is consistent with weak stationarity of the input data stochastic process.

Ares I-X Best Estimated Trajectory Analysis and Results

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Beck, Roger E.; Starr, Brett R.; Derry, Stephen D.; Brandon, Jay; Olds, Aaron D.

2011-01-01

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air-data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions.

Ares I-X Best Estimated Trajectory and Comparison with Pre-Flight Predictions

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Beck, Roger E.; Derry, Stephen D.; Brandon, Jay M.; Starr, Brett R.; Tartabini, Paul V.; Olds, Aaron D.

2011-01-01

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air- data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions.

Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density

NASA Astrophysics Data System (ADS)

Closas, Pau; Guillamon, Antoni

2017-12-01

This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain's connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes.

A Stream Tilling Approach to Surface Area Estimation for Large Scale Spatial Data in a Shared Memory System

NASA Astrophysics Data System (ADS)

Liu, Jiping; Kang, Xiaochen; Dong, Chun; Xu, Shenghua

2017-12-01

Surface area estimation is a widely used tool for resource evaluation in the physical world. When processing large scale spatial data, the input/output (I/O) can easily become the bottleneck in parallelizing the algorithm due to the limited physical memory resources and the very slow disk transfer rate. In this paper, we proposed a stream tilling approach to surface area estimation that first decomposed a spatial data set into tiles with topological expansions. With these tiles, the one-to-one mapping relationship between the input and the computing process was broken. Then, we realized a streaming framework towards the scheduling of the I/O processes and computing units. Herein, each computing unit encapsulated a same copy of the estimation algorithm, and multiple asynchronous computing units could work individually in parallel. Finally, the performed experiment demonstrated that our stream tilling estimation can efficiently alleviate the heavy pressures from the I/O-bound work, and the measured speedup after being optimized have greatly outperformed the directly parallel versions in shared memory systems with multi-core processors.

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

A matlab framework for estimation of NLME models using stochastic differential equations: applications for estimation of insulin secretion rates.

PubMed

Mortensen, Stig B; Klim, Søren; Dammann, Bernd; Kristensen, Niels R; Madsen, Henrik; Overgaard, Rune V

2007-10-01

The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.

Non-intrusive parameter identification procedure user's guide

NASA Technical Reports Server (NTRS)

Hanson, G. D.; Jewell, W. F.

1983-01-01

Written in standard FORTRAN, NAS is capable of identifying linear as well as nonlinear relations between input and output parameters; the only restriction is that the input/output relation be linear with respect to the unknown coefficients of the estimation equations. The output of the identification algorithm can be specified to be in either the time domain (i.e., the estimation equation coefficients) or in the frequency domain (i.e., a frequency response of the estimation equation). The frame length ("window") over which the identification procedure is to take place can be specified to be any portion of the input time history, thereby allowing the freedom to start and stop the identification procedure within a time history. There also is an option which allows a sliding window, which gives a moving average over the time history. The NAS software also includes the ability to identify several assumed solutions simultaneously for the same or different input data.

A parameter estimation subroutine package

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Nead, M. W.

1978-01-01

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. A library of FORTRAN subroutines were developed to facilitate analyses of a variety of estimation problems. An easy to use, multi-purpose set of algorithms that are reasonably efficient and which use a minimal amount of computer storage are presented. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used. The routines are compact and efficient and are far superior to the normal equation and Kalman filter data processing algorithms that are often used for least squares analyses.

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.

Performances estimation of a rotary traveling wave ultrasonic motor based on two-dimension analytical model.

PubMed

Ming, Y; Peiwen, Q

2001-03-01

The understanding of ultrasonic motor performances as a function of input parameters, such as the voltage amplitude, driving frequency, the preload on the rotor, is a key to many applications and control of ultrasonic motor. This paper presents performances estimation of the piezoelectric rotary traveling wave ultrasonic motor as a function of input voltage amplitude and driving frequency and preload. The Love equation is used to derive the traveling wave amplitude on the stator surface. With the contact model of the distributed spring-rigid body between the stator and rotor, a two-dimension analytical model of the rotary traveling wave ultrasonic motor is constructed. Then the performances of stead rotation speed and stall torque are deduced. With MATLAB computational language and iteration algorithm, we estimate the performances of rotation speed and stall torque versus input parameters respectively. The same experiments are completed with the optoelectronic tachometer and stand weight. Both estimation and experiment results reveal the pattern of performance variation as a function of its input parameters.

Validating precision estimates in horizontal wind measurements from a Doppler lidar

DOE PAGES

Newsom, Rob K.; Brewer, W. Alan; Wilczak, James M.; ...

2017-03-30

Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating themore » precision in the radial velocity measurements. Here, the resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.« less

Classification of simple vegetation types using POLSAR image data

NASA Technical Reports Server (NTRS)

Freeman, A.

1993-01-01

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Space-variant restoration of images degraded by camera motion blur.

PubMed

Sorel, Michal; Flusser, Jan

2008-02-01

We examine the problem of restoration from multiple images degraded by camera motion blur. We consider scenes with significant depth variations resulting in space-variant blur. The proposed algorithm can be applied if the camera moves along an arbitrary curve parallel to the image plane, without any rotations. The knowledge of camera trajectory and camera parameters is not necessary. At the input, the user selects a region where depth variations are negligible. The algorithm belongs to the group of variational methods that estimate simultaneously a sharp image and a depth map, based on the minimization of a cost functional. To initialize the minimization, it uses an auxiliary window-based depth estimation algorithm. Feasibility of the algorithm is demonstrated by three experiments with real images.

A complex symbol signal-to-noise ratio estimator and its performance

NASA Technical Reports Server (NTRS)

Feria, Y.

1994-01-01

This article presents an algorithm for estimating the signal-to-noise ratio (SNR) of signals that contain data on a downconverted suppressed carrier or the first harmonic of a square-wave subcarrier. This algorithm can be used to determine the performance of the full-spectrum combiner for the Galileo S-band (2.2- to 2.3-GHz) mission by measuring the input and output symbol SNR. A performance analysis of the algorithm shows that the estimator can estimate the complex symbol SNR using 10,000 symbols at a true symbol SNR of -5 dB with a mean of -4.9985 dB and a standard deviation of 0.2454 dB, and these analytical results are checked by simulations of 100 runs with a mean of -5.06 dB and a standard deviation of 0.2506 dB.

Multishaker modal testing

NASA Technical Reports Server (NTRS)

Craig, Roy R., Jr.

1987-01-01

The major accomplishments of this research are: (1) the refinement and documentation of a multi-input, multi-output modal parameter estimation algorithm which is applicable to general linear, time-invariant dynamic systems; (2) the development and testing of an unsymmetric block-Lanzcos algorithm for reduced-order modeling of linear systems with arbitrary damping; and (3) the development of a control-structure-interaction (CSI) test facility.

Digital adaptive controllers for VTOL vehicles. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Hartmann, G. L.; Stein, G.; Pratt, S. G.

1979-01-01

The VTOL approach and landing test (VALT) adaptive software is documented. Two self-adaptive algorithms, one based on an implicit model reference design and the other on an explicit parameter estimation technique were evaluated. The organization of the software, user options, and a nominal set of input data are presented along with a flow chart and program listing of each algorithm.

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

NASA Astrophysics Data System (ADS)

Stein, Manuel S.; Bar, Shahar; Nossek, Josef A.; Tabrikian, Joseph

2018-05-01

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by ${2}/{\\pi}$ ($-1.96$ dB) when comparing to an ideal $\\infty$-bit converter. Due to hardware imperfections, low-complexity $1$-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

Engineering description of the ascent/descent bet product

NASA Technical Reports Server (NTRS)

Seacord, A. W., II

1986-01-01

The Ascent/Descent output product is produced in the OPIP routine from three files which constitute its input. One of these, OPIP.IN, contains mission specific parameters. Meteorological data, such as atmospheric wind velocities, temperatures, and density, are obtained from the second file, the Corrected Meteorological Data File (METDATA). The third file is the TRJATTDATA file which contains the time-tagged state vectors that combine trajectory information from the Best Estimate of Trajectory (BET) filter, LBRET5, and Best Estimate of Attitude (BEA) derived from IMU telemetry. Each term in the two output data files (BETDATA and the Navigation Block, or NAVBLK) are defined. The description of the BETDATA file includes an outline of the algorithm used to calculate each term. To facilitate describing the algorithms, a nomenclature is defined. The description of the nomenclature includes a definition of the coordinate systems used. The NAVBLK file contains navigation input parameters. Each term in NAVBLK is defined and its source is listed. The production of NAVBLK requires only two computational algorithms. These two algorithms, which compute the terms DELTA and RSUBO, are described. Finally, the distribution of data in the NAVBLK records is listed.

A channel estimation scheme for MIMO-OFDM systems

NASA Astrophysics Data System (ADS)

He, Chunlong; Tian, Chu; Li, Xingquan; Zhang, Ce; Zhang, Shiqi; Liu, Chaowen

2017-08-01

In view of the contradiction of the time-domain least squares (LS) channel estimation performance and the practical realization complexity, a reduced complexity channel estimation method for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) based on pilot is obtained. This approach can transform the complexity of MIMO-OFDM channel estimation problem into a simple single input single output-orthogonal frequency division multiplexing (SISO-OFDM) channel estimation problem and therefore there is no need for large matrix pseudo-inverse, which greatly reduces the complexity of algorithms. Simulation results show that the bit error rate (BER) performance of the obtained method with time orthogonal training sequences and linear minimum mean square error (LMMSE) criteria is better than that of time-domain LS estimator and nearly optimal performance.

An analysis of a candidate control algorithm for a ride quality augmentation system

NASA Technical Reports Server (NTRS)

Suikat, Reiner; Donaldson, Kent; Downing, David R.

1987-01-01

This paper presents a detailed analysis of a candidate algorithm for a ride quality augmentation system. The algorithm consists of a full-state feedback control law based on optimal control output weighting, estimators for angle of attack and sideslip, and a maneuvering algorithm. The control law is shown to perform well by both frequency and time domain analysis. The rms vertical acceleration is reduced by about 40 percent over the whole mission flight envelope. The estimators for the angle of attack and sideslip avoid the often inaccurate or costly direct measurement of those angles. The maneuvering algorithm will allow the augmented airplane to respond to pilot inputs. The design characteristics and performance are documented by the closed-loop eigenvalues; rms levels of vertical, lateral, and longitudinal acceleration; and representative time histories and frequency response.

Army ants algorithm for rare event sampling of delocalized nonadiabatic transitions by trajectory surface hopping and the estimation of sampling errors by the bootstrap method.

PubMed

Nangia, Shikha; Jasper, Ahren W; Miller, Thomas F; Truhlar, Donald G

2004-02-22

The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of coupling. The army ants algorithm is a form of rare event sampling whose efficiency is controlled by an input parameter. By choosing a suitable value of the input parameter the army ants algorithm can be reduced to the anteater algorithm (which is efficient for strongly coupled cases), and by optimizing the parameter the army ants algorithm may be efficiently applied to systems with low-probability events. To demonstrate the efficiency of the army ants algorithm, we performed atom-diatom scattering calculations on a model system involving weakly coupled electronic states. Fully converged quantum mechanical calculations were performed, and the probabilities for nonadiabatic reaction and nonreactive deexcitation (quenching) were found to be on the order of 10(-8). For such low-probability events the anteater sampling scheme requires a large number of trajectories ( approximately 10(10)) to obtain good statistics and converged semiclassical results. In contrast by using the new army ants algorithm converged results were obtained by running 10(5) trajectories. Furthermore, the results were found to be in excellent agreement with the quantum mechanical results. Sampling errors were estimated using the bootstrap method, which is validated for use with the army ants algorithm. (c) 2004 American Institute of Physics.

A Fuzzy Technique for Performing Lateral-Axis Formation Flight Navigation Using Wingtip Vortices

NASA Technical Reports Server (NTRS)

Hanson, Curtis E.

2003-01-01

Close formation flight involving aerodynamic coupling through wingtip vortices shows significant promise to improve the efficiency of cooperative aircraft operations. Impediments to the application of this technology include internship communication required to establish precise relative positioning. This report proposes a method for estimating the lateral relative position between two aircraft in close formation flight through real-time estimates of the aerodynamic effects imparted by the leading airplane on the trailing airplane. A fuzzy algorithm is developed to map combinations of vortex-induced drag and roll effects to relative lateral spacing. The algorithm is refined using self-tuning techniques to provide lateral relative position estimates accurate to 14 in., well within the requirement to maintain significant levels of drag reduction. The fuzzy navigation algorithm is integrated with a leader-follower formation flight autopilot in a two-ship F/A-18 simulation with no intership communication modeled. It is shown that in the absence of measurements from the leading airplane the algorithm provides sufficient estimation of lateral formation spacing for the autopilot to maintain stable formation flight within the vortex. Formation autopilot trim commands are used to estimate vortex effects for the algorithm. The fuzzy algorithm is shown to operate satisfactorily with anticipated levels of input uncertainties.

DS02R1: Improvements to Atomic Bomb Survivors' Input Data and Implementation of Dosimetry System 2002 (DS02) and Resulting Changes in Estimated Doses.

PubMed

Cullings, H M; Grant, E J; Egbert, S D; Watanabe, T; Oda, T; Nakamura, F; Yamashita, T; Fuchi, H; Funamoto, S; Marumo, K; Sakata, R; Kodama, Y; Ozasa, K; Kodama, K

2017-01-01

Individual dose estimates calculated by Dosimetry System 2002 (DS02) for the Life Span Study (LSS) of atomic bomb survivors are based on input data that specify location and shielding at the time of the bombing (ATB). A multi-year effort to improve information on survivors' locations ATB has recently been completed, along with comprehensive improvements in their terrain shielding input data and several improvements to computational algorithms used in combination with DS02 at RERF. Improvements began with a thorough review and prioritization of original questionnaire data on location and shielding that were taken from survivors or their proxies in the period 1949-1963. Related source documents varied in level of detail, from relatively simple lists to carefully-constructed technical drawings of structural and other shielding and surrounding neighborhoods. Systematic errors were reduced in this work by restoring the original precision of map coordinates that had been truncated due to limitations in early data processing equipment and by correcting distortions in the old (WWII-era) maps originally used to specify survivors' positions, among other improvements. Distortion errors were corrected by aligning the old maps and neighborhood drawings to orthophotographic mosaics of the cities that were newly constructed from pre-bombing aerial photographs. Random errors that were reduced included simple transcription errors and mistakes in identifying survivors' locations on the old maps. Terrain shielding input data that had been originally estimated for limited groups of survivors using older methods and data sources were completely re-estimated for all survivors using new digital terrain elevation data. Improvements to algorithms included a fix to an error in the DS02 code for coupling house and terrain shielding, a correction for elevation at the survivor's location in calculating angles to the horizon used for terrain shielding input, an improved method for truncating high dose estimates to 4 Gy to reduce the effect of dose error, and improved methods for calculating averaged shielding transmission factors that are used to calculate doses for survivors without detailed shielding input data. Input data changes are summarized and described here in some detail, along with the resulting changes in dose estimates and a simple description of changes in risk estimates for solid cancer mortality. This and future RERF publications will refer to the new dose estimates described herein as "DS02R1 doses."

Approximate, computationally efficient online learning in Bayesian spiking neurons.

PubMed

Kuhlmann, Levin; Hauser-Raspe, Michael; Manton, Jonathan H; Grayden, David B; Tapson, Jonathan; van Schaik, André

2014-03-01

Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

Large signal-to-noise ratio quantification in MLE for ARARMAX models

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input-output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed.

Fast instantaneous center of rotation estimation algorithm for a skied-steered robot

NASA Astrophysics Data System (ADS)

Kniaz, V. V.

2015-05-01

Skid-steered robots are widely used as mobile platforms for machine vision systems. However it is hard to achieve a stable motion of such robots along desired trajectory due to an unpredictable wheel slip. It is possible to compensate the unpredictable wheel slip and stabilize the motion of the robot using visual odometry. This paper presents a fast optical flow based algorithm for estimation of instantaneous center of rotation, angular and longitudinal speed of the robot. The proposed algorithm is based on Horn-Schunck variational optical flow estimation method. The instantaneous center of rotation and motion of the robot is estimated by back projection of optical flow field to the ground surface. The developed algorithm was tested using skid-steered mobile robot. The robot is based on a mobile platform that includes two pairs of differential driven motors and a motor controller. Monocular visual odometry system consisting of a singleboard computer and a low cost webcam is mounted on the mobile platform. A state-space model of the robot was derived using standard black-box system identification. The input (commands) and the output (motion) were recorded using a dedicated external motion capture system. The obtained model was used to control the robot without visual odometry data. The paper is concluded with the algorithm quality estimation by comparison of the trajectories estimated by the algorithm with the data from motion capture system.

Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model

NASA Astrophysics Data System (ADS)

Touhidul Mustafa, Syed Md.; Nossent, Jiri; Ghysels, Gert; Huysmans, Marijke

2017-04-01

Transient numerical groundwater flow models have been used to understand and forecast groundwater flow systems under anthropogenic and climatic effects, but the reliability of the predictions is strongly influenced by different sources of uncertainty. Hence, researchers in hydrological sciences are developing and applying methods for uncertainty quantification. Nevertheless, spatially distributed flow models pose significant challenges for parameter and spatially distributed input estimation and uncertainty quantification. In this study, we present a general and flexible approach for input and parameter estimation and uncertainty analysis of groundwater models. The proposed approach combines a fully distributed groundwater flow model (MODFLOW) with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. To avoid over-parameterization, the uncertainty of the spatially distributed model input has been represented by multipliers. The posterior distributions of these multipliers and the regular model parameters were estimated using DREAM. The proposed methodology has been applied in an overexploited aquifer in Bangladesh where groundwater pumping and recharge data are highly uncertain. The results confirm that input uncertainty does have a considerable effect on the model predictions and parameter distributions. Additionally, our approach also provides a new way to optimize the spatially distributed recharge and pumping data along with the parameter values under uncertain input conditions. It can be concluded from our approach that considering model input uncertainty along with parameter uncertainty is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds.

Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

NASA Astrophysics Data System (ADS)

Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

2017-10-01

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

SCI model structure determination program (OSR) user's guide. [optimal subset regression

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program, OSR (Optimal Subset Regression) which estimates models for rotorcraft body and rotor force and moment coefficients is described. The technique used is based on the subset regression algorithm. Given time histories of aerodynamic coefficients, aerodynamic variables, and control inputs, the program computes correlation between various time histories. The model structure determination is based on these correlations. Inputs and outputs of the program are given.

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

PubMed Central

Vahabi, Zahra; Kermani, Saeed

2012-01-01

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

Dynamic modeling and parameter estimation of a radial and loop type distribution system network

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

Jun Qui; Heng Chen; Girgis, A.A.

1993-05-01

This paper presents a new identification approach to three-phase power system modeling and model reduction taking power system network as multi-input, multi-output (MIMO) processes. The model estimate can be obtained in discrete-time input-output form, discrete- or continuous-time state-space variable form, or frequency-domain impedance transfer function matrix form. An algorithm for determining the model structure of this MIMO process is described. The effect of measurement noise on the approach is also discussed. This approach has been applied on a sample system and simulation results are also presented in this paper.

Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

PubMed

Kowalski, Piotr A; Kusy, Maciej

2018-05-01

In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function. Therefore, the smoothing parameter is separately calculated for each dimension by means of the plug-in method. The classification qualities of the reduced and full structure PNN are compared. Furthermore, we evaluate the performance of PNN, for which global sensitivity analysis (GSA) and the common reduction methods are applied, both in the input layer and the pattern layer. The models are tested on the classification problems of eight repository data sets. A 10-fold cross validation procedure is used to determine the prediction ability of the networks. Based on the obtained results, it is shown that the LSA can be used as an alternative PNN reduction approach.

A parameter estimation subroutine package

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Nead, W. M.

1977-01-01

Linear least squares estimation and regression analyses continue to play a major role in orbit determination and related areas. FORTRAN subroutines have been developed to facilitate analyses of a variety of parameter estimation problems. Easy to use multipurpose sets of algorithms are reported that are reasonably efficient and which use a minimal amount of computer storage. Subroutine inputs, outputs, usage and listings are given, along with examples of how these routines can be used.

Evaluation of the MV (CAPON) Coherent Doppler Lidar Velocity Estimator

NASA Technical Reports Server (NTRS)

Lottman, B.; Frehlich, R.

1997-01-01

The performance of the CAPON velocity estimator for coherent Doppler lidar is determined for typical space-based and ground-based parameter regimes. Optimal input parameters for the algorithm were determined for each regime. For weak signals, performance is described by the standard deviation of the good estimates and the fraction of outliers. For strong signals, the fraction of outliers is zero. Numerical effort was also determined.

Distributed estimation for adaptive sensor selection in wireless sensor networks

NASA Astrophysics Data System (ADS)

Mahmoud, Magdi S.; Hassan Hamid, Matasm M.

2014-05-01

Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.

Analysis of blind identification methods for estimation of kinetic parameters in dynamic medical imaging

NASA Astrophysics Data System (ADS)

Riabkov, Dmitri

Compartment modeling of dynamic medical image data implies that the concentration of the tracer over time in a particular region of the organ of interest is well-modeled as a convolution of the tissue response with the tracer concentration in the blood stream. The tissue response is different for different tissues while the blood input is assumed to be the same for different tissues. The kinetic parameters characterizing the tissue responses can be estimated by blind identification methods. These algorithms use the simultaneous measurements of concentration in separate regions of the organ; if the regions have different responses, the measurement of the blood input function may not be required. In this work it is shown that the blind identification problem has a unique solution for two-compartment model tissue response. For two-compartment model tissue responses in dynamic cardiac MRI imaging conditions with gadolinium-DTPA contrast agent, three blind identification algorithms are analyzed here to assess their utility: Eigenvector-based Algorithm for Multichannel Blind Deconvolution (EVAM), Cross Relations (CR), and Iterative Quadratic Maximum Likelihood (IQML). Comparisons of accuracy with conventional (not blind) identification techniques where the blood input is known are made as well. The statistical accuracies of estimation for the three methods are evaluated and compared for multiple parameter sets. The results show that the IQML method gives more accurate estimates than the other two blind identification methods. A proof is presented here that three-compartment model blind identification is not unique in the case of only two regions. It is shown that it is likely unique for the case of more than two regions, but this has not been proved analytically. For the three-compartment model the tissue responses in dynamic FDG PET imaging conditions are analyzed with the blind identification algorithms EVAM and Separable variables Least Squares (SLS). A method of identification that assumes that FDG blood input in the brain can be modeled as a function of time and several parameters (IFM) is analyzed also. Nonuniform sampling SLS (NSLS) is developed due to the rapid change of the FDG concentration in the blood during the early postinjection stage. Comparisons of accuracy of EVAM, SLS, NSLS and IFM identification techniques are made.

Optimized System Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Longman, Richard W.

1999-01-01

In system identification, one usually cares most about finding a model whose outputs are as close as possible to the true system outputs when the same input is applied to both. However, most system identification algorithms do not minimize this output error. Often they minimize model equation error instead, as in typical least-squares fits using a finite-difference model, and it is seen here that this distinction is significant. Here, we develop a set of system identification algorithms that minimize output error for multi-input/multi-output and multi-input/single-output systems. This is done with sequential quadratic programming iterations on the nonlinear least-squares problems, with an eigendecomposition to handle indefinite second partials. This optimization minimizes a nonlinear function of many variables, and hence can converge to local minima. To handle this problem, we start the iterations from the OKID (Observer/Kalman Identification) algorithm result. Not only has OKID proved very effective in practice, it minimizes an output error of an observer which has the property that as the data set gets large, it converges to minimizing the criterion of interest here. Hence, it is a particularly good starting point for the nonlinear iterations here. Examples show that the methods developed here eliminate the bias that is often observed using any system identification methods of either over-estimating or under-estimating the damping of vibration modes in lightly damped structures.

Polarimeter Blind Deconvolution Using Image Diversity

DTIC Science & Technology

2007-09-01

significant presence when imaging through turbulence and its ease of production in the labora- tory. An innovative algorithm for detection and estimation...1.2.2.2 Atmospheric Turbulence . Atmospheric turbulence spatially distorts the wavefront as light passes through it and causes blurring of images in an...intensity image . Various values of β are used in the experiments. The optimal β value varied with the input and the algorithm . The hybrid seemed to

A PC-based magnetometer-only attitude and rate determination system for gyroless spacecraft

NASA Technical Reports Server (NTRS)

Challa, M.; Natanson, G.; Deutschmann, J.; Galal, K.

1995-01-01

This paper describes a prototype PC-based system that uses measurements from a three-axis magnetometer (TAM) to estimate the state (three-axis attitude and rates) of a spacecraft given no a priori information other than the mass properties. The system uses two algorithms that estimate the spacecraft's state - a deterministic magnetic-field only algorithm and a Kalman filter for gyroless spacecraft. The algorithms are combined by invoking the deterministic algorithm to generate the spacecraft state at epoch using a small batch of data and then using this deterministic epoch solution as the initial condition for the Kalman filter during the production run. System input comprises processed data that includes TAM and reference magnetic field data. Additional information, such as control system data and measurements from line-of-sight sensors, can be input to the system if available. Test results are presented using in-flight data from two three-axis stabilized spacecraft: Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) (gyroless, Sun-pointing) and Earth Radiation Budget Satellite (ERBS) (gyro-based, Earth-pointing). The results show that, using as little as 700 s of data, the system is capable of accuracies of 1.5 deg in attitude and 0.01 deg/s in rates; i.e., within SAMPEX mission requirements.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

Generalized expectation-maximization segmentation of brain MR images

NASA Astrophysics Data System (ADS)

Devalkeneer, Arnaud A.; Robe, Pierre A.; Verly, Jacques G.; Phillips, Christophe L. M.

2006-03-01

Manual segmentation of medical images is unpractical because it is time consuming, not reproducible, and prone to human error. It is also very difficult to take into account the 3D nature of the images. Thus, semi- or fully-automatic methods are of great interest. Current segmentation algorithms based on an Expectation- Maximization (EM) procedure present some limitations. The algorithm by Ashburner et al., 2005, does not allow multichannel inputs, e.g. two MR images of different contrast, and does not use spatial constraints between adjacent voxels, e.g. Markov random field (MRF) constraints. The solution of Van Leemput et al., 1999, employs a simplified model (mixture coefficients are not estimated and only one Gaussian is used by tissue class, with three for the image background). We have thus implemented an algorithm that combines the features of these two approaches: multichannel inputs, intensity bias correction, multi-Gaussian histogram model, and Markov random field (MRF) constraints. Our proposed method classifies tissues in three iterative main stages by way of a Generalized-EM (GEM) algorithm: (1) estimation of the Gaussian parameters modeling the histogram of the images, (2) correction of image intensity non-uniformity, and (3) modification of prior classification knowledge by MRF techniques. The goal of the GEM algorithm is to maximize the log-likelihood across the classes and voxels. Our segmentation algorithm was validated on synthetic data (with the Dice metric criterion) and real data (by a neurosurgeon) and compared to the original algorithms by Ashburner et al. and Van Leemput et al. Our combined approach leads to more robust and accurate segmentation.

Efficient Screening of Climate Model Sensitivity to a Large Number of Perturbed Input Parameters [plus supporting information

DOE PAGES

Covey, Curt; Lucas, Donald D.; Tannahill, John; ...

2013-07-01

Modern climate models contain numerous input parameters, each with a range of possible values. Since the volume of parameter space increases exponentially with the number of parameters N, it is generally impossible to directly evaluate a model throughout this space even if just 2-3 values are chosen for each parameter. Sensitivity screening algorithms, however, can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination.This can aid both model development and the uncertainty quantification (UQ) process. Here we report results from a parameter sensitivity screening algorithm hitherto untested in climate modeling,more » the Morris one-at-a-time (MOAT) method. This algorithm drastically reduces the computational cost of estimating sensitivities in a high dimensional parameter space because the sample size grows linearly rather than exponentially with N. It nevertheless samples over much of the N-dimensional volume and allows assessment of parameter interactions, unlike traditional elementary one-at-a-time (EOAT) parameter variation. We applied both EOAT and MOAT to the Community Atmosphere Model (CAM), assessing CAM’s behavior as a function of 27 uncertain input parameters related to the boundary layer, clouds, and other subgrid scale processes. For radiation balance at the top of the atmosphere, EOAT and MOAT rank most input parameters similarly, but MOAT identifies a sensitivity that EOAT underplays for two convection parameters that operate nonlinearly in the model. MOAT’s ranking of input parameters is robust to modest algorithmic variations, and it is qualitatively consistent with model development experience. Supporting information is also provided at the end of the full text of the article.« less

Face pose tracking using the four-point algorithm

NASA Astrophysics Data System (ADS)

Fung, Ho Yin; Wong, Kin Hong; Yu, Ying Kin; Tsui, Kwan Pang; Kam, Ho Chuen

2017-06-01

In this paper, we have developed an algorithm to track the pose of a human face robustly and efficiently. Face pose estimation is very useful in many applications such as building virtual reality systems and creating an alternative input method for the disabled. Firstly, we have modified a face detection toolbox called DLib for the detection of a face in front of a camera. The detected face features are passed to a pose estimation method, known as the four-point algorithm, for pose computation. The theory applied and the technical problems encountered during system development are discussed in the paper. It is demonstrated that the system is able to track the pose of a face in real time using a consumer grade laptop computer.

A tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel independent brake from moderate driving to limit handling

NASA Astrophysics Data System (ADS)

Joa, Eunhyek; Park, Kwanwoo; Koh, Youngil; Yi, Kyongsu; Kim, Kilsoo

2018-04-01

This paper presents a tyre slip-based integrated chassis control of front/rear traction distribution and four-wheel braking for enhanced performance from moderate driving to limit handling. The proposed algorithm adopted hierarchical structure: supervisor - desired motion tracking controller - optimisation-based control allocation. In the supervisor, by considering transient cornering characteristics, desired vehicle motion is calculated. In the desired motion tracking controller, in order to track desired vehicle motion, virtual control input is determined in the manner of sliding mode control. In the control allocation, virtual control input is allocated to minimise cost function. The cost function consists of two major parts. First part is a slip-based tyre friction utilisation quantification, which does not need a tyre force estimation. Second part is an allocation guideline, which guides optimally allocated inputs to predefined solution. The proposed algorithm has been investigated via simulation from moderate driving to limit handling scenario. Compared to Base and direct yaw moment control system, the proposed algorithm can effectively reduce tyre dissipation energy in the moderate driving situation. Moreover, the proposed algorithm enhances limit handling performance compared to Base and direct yaw moment control system. In addition to comparison with Base and direct yaw moment control, comparison the proposed algorithm with the control algorithm based on the known tyre force information has been conducted. The results show that the performance of the proposed algorithm is similar with that of the control algorithm with the known tyre force information.

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

NASA Technical Reports Server (NTRS)

Smith, Mark S.; Moes, Timothy R.; Morelli, Eugene A.

2003-01-01

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

A Comprehensive Estimation of the Economic Effects of Meteorological Services Based on the Input-Output Method

PubMed Central

Wu, Xianhua; Yang, Lingjuan; Guo, Ji; Lu, Huaguo; Chen, Yunfeng; Sun, Jian

2014-01-01

Concentrating on consuming coefficient, partition coefficient, and Leontief inverse matrix, relevant concepts and algorithms are developed for estimating the impact of meteorological services including the associated (indirect, complete) economic effect. Subsequently, quantitative estimations are particularly obtained for the meteorological services in Jiangxi province by utilizing the input-output method. It is found that the economic effects are noticeably rescued by the preventive strategies developed from both the meteorological information and internal relevance (interdependency) in the industrial economic system. Another finding is that the ratio range of input in the complete economic effect on meteorological services is about 1 : 108.27–1 : 183.06, remarkably different from a previous estimation based on the Delphi method (1 : 30–1 : 51). Particularly, economic effects of meteorological services are higher for nontraditional users of manufacturing, wholesale and retail trades, services sector, tourism and culture, and art and lower for traditional users of agriculture, forestry, livestock, fishery, and construction industries. PMID:24578666

A comprehensive estimation of the economic effects of meteorological services based on the input-output method.

PubMed

Wu, Xianhua; Wei, Guo; Yang, Lingjuan; Guo, Ji; Lu, Huaguo; Chen, Yunfeng; Sun, Jian

2014-01-01

Concentrating on consuming coefficient, partition coefficient, and Leontief inverse matrix, relevant concepts and algorithms are developed for estimating the impact of meteorological services including the associated (indirect, complete) economic effect. Subsequently, quantitative estimations are particularly obtained for the meteorological services in Jiangxi province by utilizing the input-output method. It is found that the economic effects are noticeably rescued by the preventive strategies developed from both the meteorological information and internal relevance (interdependency) in the industrial economic system. Another finding is that the ratio range of input in the complete economic effect on meteorological services is about 1 : 108.27-1 : 183.06, remarkably different from a previous estimation based on the Delphi method (1 : 30-1 : 51). Particularly, economic effects of meteorological services are higher for nontraditional users of manufacturing, wholesale and retail trades, services sector, tourism and culture, and art and lower for traditional users of agriculture, forestry, livestock, fishery, and construction industries.

Egomotion estimation with optic flow and air velocity sensors.

PubMed

Rutkowski, Adam J; Miller, Mikel M; Quinn, Roger D; Willis, Mark A

2011-06-01

We develop a method that allows a flyer to estimate its own motion (egomotion), the wind velocity, ground slope, and flight height using only inputs from onboard optic flow and air velocity sensors. Our artificial algorithm demonstrates how it could be possible for flying insects to determine their absolute egomotion using their available sensors, namely their eyes and wind sensitive hairs and antennae. Although many behaviors can be performed by only knowing the direction of travel, behavioral experiments indicate that odor tracking insects are able to estimate the wind direction and control their absolute egomotion (i.e., groundspeed). The egomotion estimation method that we have developed, which we call the opto-aeronautic algorithm, is tested in a variety of wind and ground slope conditions using a video recorded flight of a moth tracking a pheromone plume. Over all test cases that we examined, the algorithm achieved a mean absolute error in height of 7% or less. Furthermore, our algorithm is suitable for the navigation of aerial vehicles in environments where signals from the Global Positioning System are unavailable.

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.

Prediction of S-wave velocity using complete ensemble empirical mode decomposition and neural networks

NASA Astrophysics Data System (ADS)

Gaci, Said; Hachay, Olga; Zaourar, Naima

2017-04-01

One of the key elements in hydrocarbon reservoirs characterization is the S-wave velocity (Vs). Since the traditional estimating methods often fail to accurately predict this physical parameter, a new approach that takes into account its non-stationary and non-linear properties is needed. In this view, a prediction model based on complete ensemble empirical mode decomposition (CEEMD) and a multiple layer perceptron artificial neural network (MLP ANN) is suggested to compute Vs from P-wave velocity (Vp). Using a fine-to-coarse reconstruction algorithm based on CEEMD, the Vp log data is decomposed into a high frequency (HF) component, a low frequency (LF) component and a trend component. Then, different combinations of these components are used as inputs of the MLP ANN algorithm for estimating Vs log. Applications on well logs taken from different geological settings illustrate that the predicted Vs values using MLP ANN with the combinations of HF, LF and trend in inputs are more accurate than those obtained with the traditional estimating methods. Keywords: S-wave velocity, CEEMD, multilayer perceptron neural networks.

Two cloud-based cues for estimating scene structure and camera calibration.

PubMed

Jacobs, Nathan; Abrams, Austin; Pless, Robert

2013-10-01

We describe algorithms that use cloud shadows as a form of stochastically structured light to support 3D scene geometry estimation. Taking video captured from a static outdoor camera as input, we use the relationship of the time series of intensity values between pairs of pixels as the primary input to our algorithms. We describe two cues that relate the 3D distance between a pair of points to the pair of intensity time series. The first cue results from the fact that two pixels that are nearby in the world are more likely to be under a cloud at the same time than two distant points. We describe methods for using this cue to estimate focal length and scene structure. The second cue is based on the motion of cloud shadows across the scene; this cue results in a set of linear constraints on scene structure. These constraints have an inherent ambiguity, which we show how to overcome by combining the cloud motion cue with the spatial cue. We evaluate our method on several time lapses of real outdoor scenes.

Multidimensional density shaping by sigmoids.

PubMed

Roth, Z; Baram, Y

1996-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the output entropy of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's optimization method, applied to the estimated density, yields a recursive estimator for a random variable or a random sequence. A constrained connectivity structure yields a linear estimator, which is particularly suitable for "real time" prediction. A Gaussian nonlinearity yields a closed-form solution for the network's parameters, which may also be used for initializing the optimization algorithm when other nonlinearities are employed. A triangular connectivity between the neurons and the input, which is naturally suggested by the statistical setting, reduces the number of parameters. Applications to classification and forecasting problems are demonstrated.

A Network Flow Approach to the Initial Skills Training Scheduling Problem

DTIC Science & Technology

2007-12-01

include (but are not limited to) queuing theory, stochastic analysis and simulation. After the demand schedule has been estimated, it can be ...software package has already been purchased and is in use by AFPC, AFPC has requested that the new algorithm be programmed in this language as well ...the discussed outputs from those schedules. Required Inputs A single input file details the students to be scheduled as well as the courses

AU-FREDI - AUTONOMOUS FREQUENCY DOMAIN IDENTIFICATION

NASA Technical Reports Server (NTRS)

Yam, Y.

1994-01-01

The Autonomous Frequency Domain Identification program, AU-FREDI, is a system of methods, algorithms and software that was developed for the identification of structural dynamic parameters and system transfer function characterization for control of large space platforms and flexible spacecraft. It was validated in the CALTECH/Jet Propulsion Laboratory's Large Spacecraft Control Laboratory. Due to the unique characteristics of this laboratory environment, and the environment-specific nature of many of the software's routines, AU-FREDI should be considered to be a collection of routines which can be modified and reassembled to suit system identification and control experiments on large flexible structures. The AU-FREDI software was originally designed to command plant excitation and handle subsequent input/output data transfer, and to conduct system identification based on the I/O data. Key features of the AU-FREDI methodology are as follows: 1. AU-FREDI has on-line digital filter design to support on-orbit optimal input design and data composition. 2. Data composition of experimental data in overlapping frequency bands overcomes finite actuator power constraints. 3. Recursive least squares sine-dwell estimation accurately handles digitized sinusoids and low frequency modes. 4. The system also includes automated estimation of model order using a product moment matrix. 5. A sample-data transfer function parametrization supports digital control design. 6. Minimum variance estimation is assured with a curve fitting algorithm with iterative reweighting. 7. Robust root solvers accurately factorize high order polynomials to determine frequency and damping estimates. 8. Output error characterization of model additive uncertainty supports robustness analysis. The research objectives associated with AU-FREDI were particularly useful in focusing the identification methodology for realistic on-orbit testing conditions. Rather than estimating the entire structure, as is typically done in ground structural testing, AU-FREDI identifies only the key transfer function parameters and uncertainty bounds that are necessary for on-line design and tuning of robust controllers. AU-FREDI's system identification algorithms are independent of the JPL-LSCL environment, and can easily be extracted and modified for use with input/output data files. The basic approach of AU-FREDI's system identification algorithms is to non-parametrically identify the sampled data in the frequency domain using either stochastic or sine-dwell input, and then to obtain a parametric model of the transfer function by curve-fitting techniques. A cross-spectral analysis of the output error is used to determine the additive uncertainty in the estimated transfer function. The nominal transfer function estimate and the estimate of the associated additive uncertainty can be used for robust control analysis and design. AU-FREDI's I/O data transfer routines are tailored to the environment of the CALTECH/ JPL-LSCL which included a special operating system to interface with the testbed. Input commands for a particular experiment (wideband, narrowband, or sine-dwell) were computed on-line and then issued to respective actuators by the operating system. The operating system also took measurements through displacement sensors and passed them back to the software for storage and off-line processing. In order to make use of AU-FREDI's I/O data transfer routines, a user would need to provide an operating system capable of overseeing such functions between the software and the experimental setup at hand. The program documentation contains information designed to support users in either providing such an operating system or modifying the system identification algorithms for use with input/output data files. It provides a history of the theoretical, algorithmic and software development efforts including operating system requirements and listings of some of the various special purpose subroutines which were developed and optimized for Lahey FORTRAN compilers on IBM PC-AT computers before the subroutines were integrated into the system software. Potential purchasers are encouraged to purchase and review the documentation before purchasing the AU-FREDI software. AU-FREDI is distributed in DEC VAX BACKUP format on a 1600 BPI 9-track magnetic tape (standard media) or a TK50 tape cartridge. AU-FREDI was developed in 1989 and is a copyrighted work with all copyright vested in NASA.

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia

PubMed Central

de Oliveira, Gabriel; Brunsell, Nathaniel A.; Moraes, Elisabete C.; Bertani, Gabriel; dos Santos, Thiago V.; Shimabukuro, Yosio E.; Aragão, Luiz E. O. C.

2016-01-01

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001–December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance. PMID:27347957

Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia.

PubMed

de Oliveira, Gabriel; Brunsell, Nathaniel A; Moraes, Elisabete C; Bertani, Gabriel; Dos Santos, Thiago V; Shimabukuro, Yosio E; Aragão, Luiz E O C

2016-06-24

In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.

Satellite estimation of surface spectral ultraviolet irradiance using OMI data in East Asia

NASA Astrophysics Data System (ADS)

Lee, H.; Kim, J.; Jeong, U.

2017-12-01

Due to a strong influence to the human health and ecosystem environment, continuous monitoring of the surface ultraviolet (UV) irradiance is important nowadays. The amount of UVA (320-400 nm) and UVB (290-320 nm) radiation at the Earth surface depends on the extent of Rayleigh scattering by atmospheric gas molecules, the radiative absorption by ozone, radiative scattering by clouds, and both absorption and scattering by airborne aerosols. Thus advanced consideration of these factors is the essential part to establish the process of UV irradiance estimation. Also UV index (UVI) is a simple parameter to show the strength of surface UV irradiance, therefore UVI has been widely utilized for the purpose of UV monitoring. In this study, we estimate surface UV irradiance at East Asia using realistic input based on OMI Total Ozone and reflectivity, and then validate this estimated comparing to UV irradiance from World Ozone and Ultraviolet Radiation Data Centre (WOUDC) data. In this work, we also try to develop our own retrieval algorithm for better estimation of surface irradiance. We use the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model version 2.6 for our UV irradiance calculation. The input to the VLIDORT radiative transfer calculations are the total ozone column (TOMS V7 climatology), the surface albedo (Herman and Celarier, 1997) and the cloud optical depth. Based on these, the UV irradiance is calculated based on look-up table (LUT) approach. To correct absorbing aerosol, UV irradiance algorithm added climatological aerosol information (Arola et al., 2009). The further study, we analyze the comprehensive uncertainty analysis based on LUT and all input parameters.

Efficient sequential and parallel algorithms for finding edit distance based motifs.

PubMed

Pal, Soumitra; Xiao, Peng; Rajasekaran, Sanguthevar

2016-08-18

Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion. One popular technique to solve the problem is to explore for each input string the set of all possible l-mers that belong to the d-neighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multi-core shared memory setting uses arrays for storing and a novel modification of radix-sort for sorting the candidate motifs. The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads. Our algorithms have pushed up the state-of-the-art of EMS solvers and we believe that the techniques introduced in this paper are also applicable to other motif search problems such as Planted Motif Search (PMS) and Simple Motif Search (SMS).

Acoustic Source Bearing Estimation (ASBE) computer program development

NASA Technical Reports Server (NTRS)

Wiese, Michael R.

1987-01-01

A new bearing estimation algorithm (Acoustic Source Analysis Technique - ASAT) and an acoustic analysis computer program (Acoustic Source Bearing Estimation - ASBE) are described, which were developed by Computer Sciences Corporation for NASA Langley Research Center. The ASBE program is used by the Acoustics Division/Applied Acoustics Branch and the Instrument Research Division/Electro-Mechanical Instrumentation Branch to analyze acoustic data and estimate the azimuths from which the source signals radiated. Included are the input and output from a benchmark test case.

Fast computation of derivative based sensitivities of PSHA models via algorithmic differentiation

NASA Astrophysics Data System (ADS)

Leövey, Hernan; Molkenthin, Christian; Scherbaum, Frank; Griewank, Andreas; Kuehn, Nicolas; Stafford, Peter

2015-04-01

Probabilistic seismic hazard analysis (PSHA) is the preferred tool for estimation of potential ground-shaking hazard due to future earthquakes at a site of interest. A modern PSHA represents a complex framework which combines different models with possible many inputs. Sensitivity analysis is a valuable tool for quantifying changes of a model output as inputs are perturbed, identifying critical input parameters and obtaining insight in the model behavior. Differential sensitivity analysis relies on calculating first-order partial derivatives of the model output with respect to its inputs. Moreover, derivative based global sensitivity measures (Sobol' & Kucherenko '09) can be practically used to detect non-essential inputs of the models, thus restricting the focus of attention to a possible much smaller set of inputs. Nevertheless, obtaining first-order partial derivatives of complex models with traditional approaches can be very challenging, and usually increases the computation complexity linearly with the number of inputs appearing in the models. In this study we show how Algorithmic Differentiation (AD) tools can be used in a complex framework such as PSHA to successfully estimate derivative based sensitivities, as is the case in various other domains such as meteorology or aerodynamics, without no significant increase in the computation complexity required for the original computations. First we demonstrate the feasibility of the AD methodology by comparing AD derived sensitivities to analytically derived sensitivities for a basic case of PSHA using a simple ground-motion prediction equation. In a second step, we derive sensitivities via AD for a more complex PSHA study using a ground motion attenuation relation based on a stochastic method to simulate strong motion. The presented approach is general enough to accommodate more advanced PSHA studies of higher complexity.

Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

NASA Astrophysics Data System (ADS)

Azarpour, Masoumeh; Enzner, Gerald

2017-12-01

Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome reveals that the signals processed by the blocking-based algorithms are significantly preferred over the noisy signal in terms of instantaneous noise attenuation. Furthermore, the listening test data analysis confirms the conclusions drawn based on the objective evaluation.

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

PubMed

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

2017-09-19

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

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

PubMed Central

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

2017-01-01

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

A Horizontal Tilt Correction Method for Ship License Numbers Recognition

NASA Astrophysics Data System (ADS)

Liu, Baolong; Zhang, Sanyuan; Hong, Zhenjie; Ye, Xiuzi

2018-02-01

An automatic ship license numbers (SLNs) recognition system plays a significant role in intelligent waterway transportation systems since it can be used to identify ships by recognizing the characters in SLNs. Tilt occurs frequently in many SLNs because the monitors and the ships usually have great vertical or horizontal angles, which decreases the accuracy and robustness of a SLNs recognition system significantly. In this paper, we present a horizontal tilt correction method for SLNs. For an input tilt SLN image, the proposed method accomplishes the correction task through three main steps. First, a MSER-based characters’ center-points computation algorithm is designed to compute the accurate center-points of the characters contained in the input SLN image. Second, a L 1- L 2 distance-based straight line is fitted to the computed center-points using M-estimator algorithm. The tilt angle is estimated at this stage. Finally, based on the computed tilt angle, an affine transformation rotation is conducted to rotate and to correct the input SLN horizontally. At last, the proposed method is tested on 200 tilt SLN images, the proposed method is proved to be effective with a tilt correction rate of 80.5%.

Adaptive model reduction for continuous systems via recursive rational interpolation

NASA Technical Reports Server (NTRS)

Lilly, John H.

1994-01-01

A method for adaptive identification of reduced-order models for continuous stable SISO and MIMO plants is presented. The method recursively finds a model whose transfer function (matrix) matches that of the plant on a set of frequencies chosen by the designer. The algorithm utilizes the Moving Discrete Fourier Transform (MDFT) to continuously monitor the frequency-domain profile of the system input and output signals. The MDFT is an efficient method of monitoring discrete points in the frequency domain of an evolving function of time. The model parameters are estimated from MDFT data using standard recursive parameter estimation techniques. The algorithm has been shown in simulations to be quite robust to additive noise in the inputs and outputs. A significant advantage of the method is that it enables a type of on-line model validation. This is accomplished by simultaneously identifying a number of models and comparing each with the plant in the frequency domain. Simulations of the method applied to an 8th-order SISO plant and a 10-state 2-input 2-output plant are presented. An example of on-line model validation applied to the SISO plant is also presented.

Aerodynamic parameter estimation via Fourier modulating function techniques

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1995-01-01

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Parameter identification for nonlinear aerodynamic systems

NASA Technical Reports Server (NTRS)

Pearson, Allan E.

1990-01-01

Parameter identification for nonlinear aerodynamic systems is examined. It is presumed that the underlying model can be arranged into an input/output (I/O) differential operator equation of a generic form. The algorithm estimation is especially efficient since the equation error can be integrated exactly given any I/O pair to obtain an algebraic function of the parameters. The algorithm for parameter identification was extended to the order determination problem for linear differential system. The degeneracy in a least squares estimate caused by feedback was addressed. A method of frequency analysis for determining the transfer function G(j omega) from transient I/O data was formulated using complex valued Fourier based modulating functions in contrast with the trigonometric modulating functions for the parameter estimation problem. A simulation result of applying the algorithm is given under noise-free conditions for a system with a low pass transfer function.

Nonlinear unbiased minimum-variance filter for Mars entry autonomous navigation under large uncertainties and unknown measurement bias.

PubMed

Xiao, Mengli; Zhang, Yongbo; Fu, Huimin; Wang, Zhihua

2018-05-01

High-precision navigation algorithm is essential for the future Mars pinpoint landing mission. The unknown inputs caused by large uncertainties of atmospheric density and aerodynamic coefficients as well as unknown measurement biases may cause large estimation errors of conventional Kalman filters. This paper proposes a derivative-free version of nonlinear unbiased minimum variance filter for Mars entry navigation. This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation. IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

GLACiAR: GaLAxy survey Completeness AlgoRithm

NASA Astrophysics Data System (ADS)

Carrasco, Daniela; Trenti, Michele; Mutch, Simon; Oesch, Pascal

2018-05-01

GLACiAR (GaLAxy survey Completeness AlgoRithm) estimates the completeness and selection functions in galaxy surveys. Tailored for multiband imaging surveys aimed at searching for high-redshift galaxies through the Lyman Break technique, the code can nevertheless be applied broadly. GLACiAR generates artificial galaxies that follow Sérsic profiles with different indexes and with customizable size, redshift and spectral energy distribution properties, adds them to input images, and measures the recovery rate.

A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice

PubMed Central

Wang, Yong; Ma, Xiaolei; Liu, Yong; Gong, Ke; Henricakson, Kristian C.; Xu, Maozeng; Wang, Yinhai

2016-01-01

This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior. PMID:26761209

Degree-of-Freedom Strengthened Cascade Array for DOD-DOA Estimation in MIMO Array Systems.

PubMed

Yao, Bobin; Dong, Zhi; Zhang, Weile; Wang, Wei; Wu, Qisheng

2018-05-14

In spatial spectrum estimation, difference co-array can provide extra degrees-of-freedom (DOFs) for promoting parameter identifiability and parameter estimation accuracy. For the sake of acquiring as more DOFs as possible with a given number of physical sensors, we herein design a novel sensor array geometry named cascade array. This structure is generated by systematically connecting a uniform linear array (ULA) and a non-uniform linear array, and can provide more DOFs than some exist array structures but less than the upper-bound indicated by minimum redundant array (MRA). We further apply this cascade array into multiple input multiple output (MIMO) array systems, and propose a novel joint direction of departure (DOD) and direction of arrival (DOA) estimation algorithm, which is based on a reduced-dimensional weighted subspace fitting technique. The algorithm is angle auto-paired and computationally efficient. Theoretical analysis and numerical simulations prove the advantages and effectiveness of the proposed array structure and the related algorithm.

Operational Retrievals of Evapotranspiration: Are we there yet?

NASA Astrophysics Data System (ADS)

Neale, C. M. U.; Anderson, M. C.; Hain, C.; Schull, M.; Isidro, C., Sr.; Goncalves, I. Z.

2017-12-01

Remote sensing based retrievals of evapotranspiration (ET) have progressed significantly over the last two decades with the improvement of methods and algorithms and the availability of multiple satellite sensors with shortwave and thermal infrared bands on polar orbiting platforms. The modeling approaches include simpler vegetation index (VI) based methods such as the reflectance-based crop coefficient approach coupled with surface reference evapotranspiration estimates to derive actual evapotranspiration of crops or, direct inputs to the Penman-Monteith equation through VI relationships with certain input variables. Methods that are more complex include one-layer or two-layer energy balance approaches that make use of both shortwave and longwave spectral band information to estimate different inputs to the energy balance equation. These models mostly differ in the estimation of sensible heat fluxes. For continental and global scale applications, other satellite-based products such as solar radiation, vegetation leaf area and cover are used as inputs, along with gridded re-analysis weather information. This presentation will review the state-of-the-art in satellite-based evapotranspiration estimation, giving examples of existing efforts to obtain operational ET retrievals over continental and global scales and discussing difficulties and challenges.

Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

PubMed Central

Wan, Liangtian; Han, Guangjie; Wang, Hao; Shu, Lei; Feng, Nanxing; Peng, Bao

2016-01-01

In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm. PMID:26985896

Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems.

PubMed

Wan, Liangtian; Han, Guangjie; Wang, Hao; Shu, Lei; Feng, Nanxing; Peng, Bao

2016-03-12

In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm.

An Uncertainty Quantification Framework for Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Hobbs, J.

2017-12-01

Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.

Machine learning with neural networks - a case study of estimating thermal conductivity with ancient well-log data

NASA Astrophysics Data System (ADS)

Harrison, Benjamin; Sandiford, Mike; McLaren, Sandra

2016-04-01

Supervised machine learning algorithms attempt to build a predictive model using empirical data. Their aim is to take a known set of input data along with known responses to the data, and adaptively train a model to generate predictions for new data inputs. A key attraction to their use is the ability to perform as function approximators where the definition of an explicit relationship between variables is infeasible. We present a novel means of estimating thermal conductivity using a supervised self-organising map algorithm, trained on about 150 thermal conductivity measurements, and using a suite of five electric logs common to 14 boreholes. A key motivation of the study was to supplement the small number of direct measurements of thermal conductivity with the decades of borehole data acquired in the Gippsland Basin to produce more confident calculations of surface heat flow. A previous attempt to generate estimates from well-log data in the Gippsland Basin using classic petrophysical log interpretation methods was able to produce reasonable synthetic thermal conductivity logs for only four boreholes. The current study has extended this to a further ten boreholes. Interesting outcomes from the study are: the method appears stable at very low sample sizes (< ~100); the SOM permits quantitative analysis of essentially qualitative uncalibrated well-log data; and the method's moderate success at prediction with minimal effort tuning the algorithm's parameters.

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.

DC servomechanism parameter identification: a Closed Loop Input Error approach.

PubMed

Garrido, Ruben; Miranda, Roger

2012-01-01

This paper presents a Closed Loop Input Error (CLIE) approach for on-line parametric estimation of a continuous-time model of a DC servomechanism functioning in closed loop. A standard Proportional Derivative (PD) position controller stabilizes the loop without requiring knowledge on the servomechanism parameters. The analysis of the identification algorithm takes into account the control law employed for closing the loop. The model contains four parameters that depend on the servo inertia, viscous, and Coulomb friction as well as on a constant disturbance. Lyapunov stability theory permits assessing boundedness of the signals associated to the identification algorithm. Experiments on a laboratory prototype allows evaluating the performance of the approach. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Tmax Determined Using a Bayesian Estimation Deconvolution Algorithm Applied to Bolus Tracking Perfusion Imaging: A Digital Phantom Validation Study.

PubMed

Uwano, Ikuko; Sasaki, Makoto; Kudo, Kohsuke; Boutelier, Timothé; Kameda, Hiroyuki; Mori, Futoshi; Yamashita, Fumio

2017-01-10

The Bayesian estimation algorithm improves the precision of bolus tracking perfusion imaging. However, this algorithm cannot directly calculate Tmax, the time scale widely used to identify ischemic penumbra, because Tmax is a non-physiological, artificial index that reflects the tracer arrival delay (TD) and other parameters. We calculated Tmax from the TD and mean transit time (MTT) obtained by the Bayesian algorithm and determined its accuracy in comparison with Tmax obtained by singular value decomposition (SVD) algorithms. The TD and MTT maps were generated by the Bayesian algorithm applied to digital phantoms with time-concentration curves that reflected a range of values for various perfusion metrics using a global arterial input function. Tmax was calculated from the TD and MTT using constants obtained by a linear least-squares fit to Tmax obtained from the two SVD algorithms that showed the best benchmarks in a previous study. Correlations between the Tmax values obtained by the Bayesian and SVD methods were examined. The Bayesian algorithm yielded accurate TD and MTT values relative to the true values of the digital phantom. Tmax calculated from the TD and MTT values with the least-squares fit constants showed excellent correlation (Pearson's correlation coefficient = 0.99) and agreement (intraclass correlation coefficient = 0.99) with Tmax obtained from SVD algorithms. Quantitative analyses of Tmax values calculated from Bayesian-estimation algorithm-derived TD and MTT from a digital phantom correlated and agreed well with Tmax values determined using SVD algorithms.

Efficient Strategies for Predictive Cell-Level Control of Lithium-Ion Batteries

NASA Astrophysics Data System (ADS)

Xavier, Marcelo A.

This dissertation introduces a set of state-space based model predictive control (MPC) algorithms tailored to a non-zero feedthrough term to account for the ohmic resistance that is inherent to the battery dynamics. MPC is herein applied to the problem of regulating cell-level measures of performance for lithium-ion batteries; the control methodologies are used first to compute a fast charging profile that respects input, output, and state constraints, i.e., input current, terminal voltage, and state of charge for an equivalent circuit model of the battery cell, and extended later to a linearized physics-based reduced-order model. The novelty of this work can summarized as follows: (1) the MPC variants are employed to a physics based reduce-order model in order to make use of the available set of internal electrochemical variables and mitigate internal mechanisms of cell degradation. (e.g., lithium plating); (2) we developed a dual-mode MPC closed-loop paradigm that suits the battery control problem with the objective of reducing computational effort by solving simpler optimization routines and guaranteeing stability; and finally (3) we developed a completely new approach of the use of a predictive control strategy where MPC is employed as a "smart sensor" for power estimation. Results are presented that show the comparative performance of the MPC algorithms for both EMC and PBROM These results highlight that dual-mode MPC can deliver optimal input current profiles by using a shorter horizon while still guaranteeing stability. Additionally, rigorous mathematical developments are presented for the development of the MPC algorithms. The use of MPC as a "smart sensor" presents it self as an appealing method for power estimation, since MPC permits a fully dynamic input profile that is able to achieve performance right at the proper constraint boundaries. Therefore, MPC is expected to produce accurate power limits for each computed sample time when compared to the Bisection method [1] which assumes constant input values over the prediction interval.

An Improved Method of Pose Estimation for Lighthouse Base Station Extension.

PubMed

Yang, Yi; Weng, Dongdong; Li, Dong; Xun, Hang

2017-10-22

In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal.

An Improved Method of Pose Estimation for Lighthouse Base Station Extension

PubMed Central

Yang, Yi; Weng, Dongdong; Li, Dong; Xun, Hang

2017-01-01

In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal. PMID:29065509

Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

NASA Technical Reports Server (NTRS)

Matthews, Bryan L.; Srivastava, Ashok N.

2010-01-01

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

Satellite Angular Rate Estimation From Vector Measurements

NASA Technical Reports Server (NTRS)

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

1996-01-01

This paper presents an algorithm for estimating the angular rate vector of a satellite which is based on the time derivatives of vector measurements expressed in a reference and body coordinate. The computed derivatives are fed into a spacial Kalman filter which yields an estimate of the spacecraft angular velocity. The filter, named Extended Interlaced Kalman Filter (EIKF), is an extension of the Kalman filter which, although being linear, estimates the state of a nonlinear dynamic system. It consists of two or three parallel Kalman filters whose individual estimates are fed to one another and are considered as known inputs by the other parallel filter(s). The nonlinear dynamics stem from the nonlinear differential equation that describes the rotation of a three dimensional body. Initial results, using simulated data, and real Rossi X ray Timing Explorer (RXTE) data indicate that the algorithm is efficient and robust.

An evaluation of open set recognition for FLIR images

NASA Astrophysics Data System (ADS)

Scherreik, Matthew; Rigling, Brian

2015-05-01

Typical supervised classification algorithms label inputs according to what was learned in a training phase. Thus, test inputs that were not seen in training are always given incorrect labels. Open set recognition algorithms address this issue by accounting for inputs that are not present in training and providing the classifier with an option to reject" unknown samples. A number of such techniques have been developed in the literature, many of which are based on support vector machines (SVMs). One approach, the 1-vs-set machine, constructs a slab" in feature space using the SVM hyperplane. Inputs falling on one side of the slab or within the slab belong to a training class, while inputs falling on the far side of the slab are rejected. We note that rejection of unknown inputs can be achieved by thresholding class posterior probabilities. Another recently developed approach, the Probabilistic Open Set SVM (POS-SVM), empirically determines good probability thresholds. We apply the 1-vs-set machine, POS-SVM, and closed set SVMs to FLIR images taken from the Comanche SIG dataset. Vehicles in the dataset are divided into three general classes: wheeled, armored personnel carrier (APC), and tank. For each class, a coarse pose estimate (front, rear, left, right) is taken. In a closed set sense, we analyze these algorithms for prediction of vehicle class and pose. To test open set performance, one or more vehicle classes are held out from training. By considering closed and open set performance separately, we may closely analyze both inter-class discrimination and threshold effectiveness.

Rotorcraft system identification techniques for handling qualities and stability and control evaluation

NASA Technical Reports Server (NTRS)

Hall, W. E., Jr.; Gupta, N. K.; Hansen, R. S.

1978-01-01

An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.

Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Li, Yong; Conte, Joel P.

2017-09-01

A methodology is proposed to update mechanics-based nonlinear finite element (FE) models of civil structures subjected to unknown input excitation. The approach allows to jointly estimate unknown time-invariant model parameters of a nonlinear FE model of the structure and the unknown time histories of input excitations using spatially-sparse output response measurements recorded during an earthquake event. The unscented Kalman filter, which circumvents the computation of FE response sensitivities with respect to the unknown model parameters and unknown input excitations by using a deterministic sampling approach, is employed as the estimation tool. The use of measurement data obtained from arrays of heterogeneous sensors, including accelerometers, displacement sensors, and strain gauges is investigated. Based on the estimated FE model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess damage in the structure. Numerically simulated response data of a three-dimensional 4-story 2-by-1 bay steel frame structure with six unknown model parameters subjected to unknown bi-directional horizontal seismic excitation, and a three-dimensional 5-story 2-by-1 bay reinforced concrete frame structure with nine unknown model parameters subjected to unknown bi-directional horizontal seismic excitation are used to illustrate and validate the proposed methodology. The results of the validation studies show the excellent performance and robustness of the proposed algorithm to jointly estimate unknown FE model parameters and unknown input excitations.

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.

Analysis of sensorless control of brushless DC motor using unknown input observer with different gains

NASA Astrophysics Data System (ADS)

Astik, Mitesh B.; Bhatt, Praghnesh; Bhalja, Bhavesh R.

2017-03-01

A sensorless control scheme based on an unknown input observer is presented in this paper in which back EMF of the Brushless DC Motor (BLDC) is continuously estimated from available line voltages and currents. During negative rotation of motor, actual and estimated speed fail to track the reference speed and if the corrective action is not taken by the observer, the motor goes into saturation. To overcome this problem, the speed estimation algorithm has been implemented in this paper to control the dynamic behavior of the motor during negative rotation. The Ackermans method was used to calculate the gains of an unknown input observer which is based on the appropriate choice of the eigenvalues in advance. The criteria to choose eigenvalue is to obtain a balance between faster convergence rate and the least noise level. Simulations have been carried out for different disturbances such as step changes in motor reference speed and load torque. The comparative simulation results clearly depict that the disturbance effects in actual and estimated responses minimizes as observer gain setting increases.

Evaluation of flash-flood discharge forecasts in complex terrain using precipitation

USGS Publications Warehouse

Yates, D.; Warner, T.T.; Brandes, E.A.; Leavesley, G.H.; Sun, Jielun; Mueller, C.K.

2001-01-01

Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar-Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface-hydrologic models employed for flood warning. Precipitation data provided by these various techniques at short time scales and at fine spatial resolutions are employed as detailed input to a distributed-parameter hydrologic model for flash-flood prediction and analysis. With the radar-based precipitation estimates employed as input, the simulated flood discharge was similar to that observed. The dynamic-model precipitation forecast showed the most promise in providing a significant discharge-forecast lead time. The algorithmic system's precipitation forecast did not demonstrate as much skill, but the associated discharge forecast would still have been sufficient to have provided an alert of impending flood danger.

Comparison of subpixel image registration algorithms

NASA Astrophysics Data System (ADS)

Boye, R. R.; Nelson, C. L.

2009-02-01

Research into the use of multiframe superresolution has led to the development of algorithms for providing images with enhanced resolution using several lower resolution copies. An integral component of these algorithms is the determination of the registration of each of the low resolution images to a reference image. Without this information, no resolution enhancement can be attained. We have endeavored to find a suitable method for registering severely undersampled images by comparing several approaches. To test the algorithms, an ideal image is input to a simulated image formation program, creating several undersampled images with known geometric transformations. The registration algorithms are then applied to the set of low resolution images and the estimated registration parameters compared to the actual values. This investigation is limited to monochromatic images (extension to color images is not difficult) and only considers global geometric transformations. Each registration approach will be reviewed and evaluated with respect to the accuracy of the estimated registration parameters as well as the computational complexity required. In addition, the effects of image content, specifically spatial frequency content, as well as the immunity of the registration algorithms to noise will be discussed.

Evaluating total inorganic nitrogen in coastal waters through fusion of multi-temporal RADARSAT-2 and optical imagery using random forest algorithm

NASA Astrophysics Data System (ADS)

Liu, Meiling; Liu, Xiangnan; Li, Jin; Ding, Chao; Jiang, Jiale

2014-12-01

Satellites routinely provide frequent, large-scale, near-surface views of many oceanographic variables pertinent to plankton ecology. However, the nutrient fertility of water can be challenging to detect accurately using remote sensing technology. This research has explored an approach to estimate the nutrient fertility in coastal waters through the fusion of synthetic aperture radar (SAR) images and optical images using the random forest (RF) algorithm. The estimation of total inorganic nitrogen (TIN) in the Hong Kong Sea, China, was used as a case study. In March of 2009 and May and August of 2010, a sequence of multi-temporal in situ data and CCD images from China's HJ-1 satellite and RADARSAT-2 images were acquired. Four sensitive parameters were selected as input variables to evaluate TIN: single-band reflectance, a normalized difference spectral index (NDSI) and HV and VH polarizations. The RF algorithm was used to merge the different input variables from the SAR and optical imagery to generate a new dataset (i.e., the TIN outputs). The results showed the temporal-spatial distribution of TIN. The TIN values decreased from coastal waters to the open water areas, and TIN values in the northeast area were higher than those found in the southwest region of the study area. The maximum TIN values occurred in May. Additionally, the estimation accuracy for estimating TIN was significantly improved when the SAR and optical data were used in combination rather than a single data type alone. This study suggests that this method of estimating nutrient fertility in coastal waters by effectively fusing data from multiple sensors is very promising.

State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

NASA Astrophysics Data System (ADS)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Qiu, Bensheng; Zhang, Xu

2017-08-01

Objective. To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. Approach. Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. Main results. Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. Significance. The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Impact of input data (in)accuracy on overestimation of visible area in digital viewshed models

PubMed Central

Klouček, Tomáš; Šímová, Petra

2018-01-01

Viewshed analysis is a GIS tool in standard use for more than two decades to perform numerous scientific and practical tasks. The reliability of the resulting viewshed model depends on the computational algorithm and the quality of the input digital surface model (DSM). Although many studies have dealt with improving viewshed algorithms, only a few studies have focused on the effect of the spatial accuracy of input data. Here, we compare simple binary viewshed models based on DSMs having varying levels of detail with viewshed models created using LiDAR DSM. The compared DSMs were calculated as the sums of digital terrain models (DTMs) and layers of forests and buildings with expertly assigned heights. Both elevation data and the visibility obstacle layers were prepared using digital vector maps differing in scale (1:5,000, 1:25,000, and 1:500,000) as well as using a combination of a LiDAR DTM with objects vectorized on an orthophotomap. All analyses were performed for 104 sample locations of 5 km2, covering areas from lowlands to mountains and including farmlands as well as afforested landscapes. We worked with two observer point heights, the first (1.8 m) simulating observation by a person standing on the ground and the second (80 m) as observation from high structures such as wind turbines, and with five estimates of forest heights (15, 20, 25, 30, and 35 m). At all height estimations, all of the vector-based DSMs used resulted in overestimations of visible areas considerably greater than those from the LiDAR DSM. In comparison to the effect from input data scale, the effect from object height estimation was shown to be secondary. PMID:29844982

Impact of input data (in)accuracy on overestimation of visible area in digital viewshed models.

PubMed

Lagner, Ondřej; Klouček, Tomáš; Šímová, Petra

2018-01-01

Viewshed analysis is a GIS tool in standard use for more than two decades to perform numerous scientific and practical tasks. The reliability of the resulting viewshed model depends on the computational algorithm and the quality of the input digital surface model (DSM). Although many studies have dealt with improving viewshed algorithms, only a few studies have focused on the effect of the spatial accuracy of input data. Here, we compare simple binary viewshed models based on DSMs having varying levels of detail with viewshed models created using LiDAR DSM. The compared DSMs were calculated as the sums of digital terrain models (DTMs) and layers of forests and buildings with expertly assigned heights. Both elevation data and the visibility obstacle layers were prepared using digital vector maps differing in scale (1:5,000, 1:25,000, and 1:500,000) as well as using a combination of a LiDAR DTM with objects vectorized on an orthophotomap. All analyses were performed for 104 sample locations of 5 km 2 , covering areas from lowlands to mountains and including farmlands as well as afforested landscapes. We worked with two observer point heights, the first (1.8 m) simulating observation by a person standing on the ground and the second (80 m) as observation from high structures such as wind turbines, and with five estimates of forest heights (15, 20, 25, 30, and 35 m). At all height estimations, all of the vector-based DSMs used resulted in overestimations of visible areas considerably greater than those from the LiDAR DSM. In comparison to the effect from input data scale, the effect from object height estimation was shown to be secondary.

Estimation of electric fields and current from ground-based magnetometer data

NASA Technical Reports Server (NTRS)

Kamide, Y.; Richmond, A. D.

1984-01-01

Recent advances in numerical algorithms for estimating ionospheric electric fields and currents from groundbased magnetometer data are reviewed and evaluated. Tests of the adequacy of one such algorithm in reproducing large-scale patterns of electrodynamic parameters in the high-latitude ionosphere have yielded generally positive results, at least for some simple cases. Some encouraging advances in producing realistic conductivity models, which are a critical input, are pointed out. When the algorithms are applied to extensive data sets, such as the ones from meridian chain magnetometer networks during the IMS, together with refined conductivity models, unique information on instantaneous electric field and current patterns can be obtained. Examples of electric potentials, ionospheric currents, field-aligned currents, and Joule heating distributions derived from ground magnetic data are presented. Possible directions for future improvements are also pointed out.

State Estimation of International Space Station Centrifuge Rotor With Incomplete Knowledge of Disturbance Inputs

NASA Technical Reports Server (NTRS)

Sullivan, Michael J.

2005-01-01

This thesis develops a state estimation algorithm for the Centrifuge Rotor (CR) system where only relative measurements are available with limited knowledge of both rotor imbalance disturbances and International Space Station (ISS) thruster disturbances. A Kalman filter is applied to a plant model augmented with sinusoidal disturbance states used to model both the effect of the rotor imbalance and the 155 thrusters on the CR relative motion measurement. The sinusoidal disturbance states compensate for the lack of the availability of plant inputs for use in the Kalman filter. Testing confirms that complete disturbance modeling is necessary to ensure reliable estimation. Further testing goes on to show that increased estimator operational bandwidth can be achieved through the expansion of the disturbance model within the filter dynamics. In addition, Monte Carlo analysis shows the varying levels of robustness against defined plant/filter uncertainty variations.

Robust input design for nonlinear dynamic modeling of AUV.

PubMed

Nouri, Nowrouz Mohammad; Valadi, Mehrdad

2017-09-01

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system's inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Monte Carlo Solution to Find Input Parameters in Systems Design Problems

NASA Astrophysics Data System (ADS)

Arsham, Hossein

2013-06-01

Most engineering system designs, such as product, process, and service design, involve a framework for arriving at a target value for a set of experiments. This paper considers a stochastic approximation algorithm for estimating the controllable input parameter within a desired accuracy, given a target value for the performance function. Two different problems, what-if and goal-seeking problems, are explained and defined in an auxiliary simulation model, which represents a local response surface model in terms of a polynomial. A method of constructing this polynomial by a single run simulation is explained. An algorithm is given to select the design parameter for the local response surface model. Finally, the mean time to failure (MTTF) of a reliability subsystem is computed and compared with its known analytical MTTF value for validation purposes.

Simulation of a Multidimensional Input Quantum Perceptron

NASA Astrophysics Data System (ADS)

Yamamoto, Alexandre Y.; Sundqvist, Kyle M.; Li, Peng; Harris, H. Rusty

2018-06-01

In this work, we demonstrate the improved data separation capabilities of the Multidimensional Input Quantum Perceptron (MDIQP), a fundamental cell for the construction of more complex Quantum Artificial Neural Networks (QANNs). This is done by using input controlled alterations of ancillary qubits in combination with phase estimation and learning algorithms. The MDIQP is capable of processing quantum information and classifying multidimensional data that may not be linearly separable, extending the capabilities of the classical perceptron. With this powerful component, we get much closer to the achievement of a feedforward multilayer QANN, which would be able to represent and classify arbitrary sets of data (both quantum and classical).

Full-order optimal compensators for flow control: the multiple inputs case

NASA Astrophysics Data System (ADS)

Semeraro, Onofrio; Pralits, Jan O.

2018-03-01

Flow control has been the subject of numerous experimental and theoretical works. We analyze full-order, optimal controllers for large dynamical systems in the presence of multiple actuators and sensors. The full-order controllers do not require any preliminary model reduction or low-order approximation: this feature allows us to assess the optimal performance of an actuated flow without relying on any estimation process or further hypothesis on the disturbances. We start from the original technique proposed by Bewley et al. (Meccanica 51(12):2997-3014, 2016. https://doi.org/10.1007/s11012-016-0547-3), the adjoint of the direct-adjoint (ADA) algorithm. The algorithm is iterative and allows bypassing the solution of the algebraic Riccati equation associated with the optimal control problem, typically infeasible for large systems. In this numerical work, we extend the ADA iteration into a more general framework that includes the design of controllers with multiple, coupled inputs and robust controllers (H_{∞} methods). First, we demonstrate our results by showing the analytical equivalence between the full Riccati solutions and the ADA approximations in the multiple inputs case. In the second part of the article, we analyze the performance of the algorithm in terms of convergence of the solution, by comparing it with analogous techniques. We find an excellent scalability with the number of inputs (actuators), making the method a viable way for full-order control design in complex settings. Finally, the applicability of the algorithm to fluid mechanics problems is shown using the linearized Kuramoto-Sivashinsky equation and the Kármán vortex street past a two-dimensional cylinder.

Self-tuning multivariable pole placement control of a multizone crystal growth furnace

NASA Technical Reports Server (NTRS)

Batur, C.; Sharpless, R. B.; Duval, W. M. B.; Rosenthal, B. N.

1992-01-01

This paper presents the design and implementation of a multivariable self-tuning temperature controller for the control of lead bromide crystal growth. The crystal grows inside a multizone transparent furnace. There are eight interacting heating zones shaping the axial temperature distribution inside the furnace. A multi-input, multi-output furnace model is identified on-line by a recursive least squares estimation algorithm. A multivariable pole placement controller based on this model is derived and implemented. Comparison between single-input, single-output and multi-input, multi-output self-tuning controllers demonstrates that the zone-to-zone interactions can be minimized better by a multi-input, multi-output controller design. This directly affects the quality of crystal grown.

Automatic creation of object hierarchies for ray tracing

NASA Technical Reports Server (NTRS)

Goldsmith, Jeffrey; Salmon, John

1987-01-01

Various methods for evaluating generated trees are proposed. The use of the hierarchical extent method of Rubin and Whitted (1980) to find the objects that will be hit by a ray is examined. This method employs tree searching; the construction of a tree of bounding volumes in order to determine the number of objects that will be hit by a ray is discussed. A tree generation algorithm, which uses a heuristic tree search strategy, is described. The effects of shuffling and sorting on the input data are investigated. The cost of inserting an object into the hierarchy during the construction of a tree algorithm is estimated. The steps involved in estimating the number of intersection calculations are presented.

Texas two-step: a framework for optimal multi-input single-output deconvolution.

PubMed

Neelamani, Ramesh; Deffenbaugh, Max; Baraniuk, Richard G

2007-11-01

Multi-input single-output deconvolution (MISO-D) aims to extract a deblurred estimate of a target signal from several blurred and noisy observations. This paper develops a new two step framework--Texas Two-Step--to solve MISO-D problems with known blurs. Texas Two-Step first reduces the MISO-D problem to a related single-input single-output deconvolution (SISO-D) problem by invoking the concept of sufficient statistics (SSs) and then solves the simpler SISO-D problem using an appropriate technique. The two-step framework enables new MISO-D techniques (both optimal and suboptimal) based on the rich suite of existing SISO-D techniques. In fact, the properties of SSs imply that a MISO-D algorithm is mean-squared-error optimal if and only if it can be rearranged to conform to the Texas Two-Step framework. Using this insight, we construct new wavelet- and curvelet-based MISO-D algorithms with asymptotically optimal performance. Simulated and real data experiments verify that the framework is indeed effective.

Circular Regression in a Dual-Phase Lock-In Amplifier for Coherent Detection of Weak Signal

PubMed Central

Wang, Gaoxuan; Reboul, Serge; Fertein, Eric

2017-01-01

Lock-in amplification (LIA) is an effective approach for recovery of weak signal buried in noise. Determination of the input signal amplitude in a classical dual-phase LIA is based on incoherent detection which leads to a biased estimation at low signal-to-noise ratio. This article presents, for the first time to our knowledge, a new architecture of LIA involving phase estimation with a linear-circular regression for coherent detection. The proposed phase delay estimate, between the input signal and a reference, is defined as the maximum-likelihood of a set of observations distributed according to a von Mises distribution. In our implementation this maximum is obtained with a Newton Raphson algorithm. We show that the proposed LIA architecture provides an unbiased estimate of the input signal amplitude. Theoretical simulations with synthetic data demonstrate that the classical LIA estimates are biased for SNR of the input signal lower than −20 dB, while the proposed LIA is able to accurately recover the weak signal amplitude. The novel approach is applied to an optical sensor for accurate measurement of NO2 concentrations at the sub-ppbv level in the atmosphere. Side-by-side intercomparison measurements with a commercial LIA (SR830, Stanford Research Inc., Sunnyvale, CA, USA ) demonstrate that the proposed LIA has an identical performance in terms of measurement accuracy and precision but with simplified hardware architecture. PMID:29135951

Identification of piecewise affine systems based on fuzzy PCA-guided robust clustering technique

NASA Astrophysics Data System (ADS)

Khanmirza, Esmaeel; Nazarahari, Milad; Mousavi, Alireza

2016-12-01

Hybrid systems are a class of dynamical systems whose behaviors are based on the interaction between discrete and continuous dynamical behaviors. Since a general method for the analysis of hybrid systems is not available, some researchers have focused on specific types of hybrid systems. Piecewise affine (PWA) systems are one of the subsets of hybrid systems. The identification of PWA systems includes the estimation of the parameters of affine subsystems and the coefficients of the hyperplanes defining the partition of the state-input domain. In this paper, we have proposed a PWA identification approach based on a modified clustering technique. By using a fuzzy PCA-guided robust k-means clustering algorithm along with neighborhood outlier detection, the two main drawbacks of the well-known clustering algorithms, i.e., the poor initialization and the presence of outliers, are eliminated. Furthermore, this modified clustering technique enables us to determine the number of subsystems without any prior knowledge about system. In addition, applying the structure of the state-input domain, that is, considering the time sequence of input-output pairs, provides a more efficient clustering algorithm, which is the other novelty of this work. Finally, the proposed algorithm has been evaluated by parameter identification of an IGV servo actuator. Simulation together with experiment analysis has proved the effectiveness of the proposed method.

Space-time light field rendering.

PubMed

Wang, Huamin; Sun, Mingxuan; Yang, Ruigang

2007-01-01

In this paper, we propose a novel framework called space-time light field rendering, which allows continuous exploration of a dynamic scene in both space and time. Compared to existing light field capture/rendering systems, it offers the capability of using unsynchronized video inputs and the added freedom of controlling the visualization in the temporal domain, such as smooth slow motion and temporal integration. In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate these software-synchronized images in the spatial domain to synthesize the final view. In addition, we introduce a very accurate and robust algorithm to estimate subframe temporal offsets among input video sequences. Experimental results from unsynchronized videos with or without time stamps show that our approach is capable of maintaining photorealistic quality from a variety of real scenes.

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

Estimation of the full-field dynamic response of a floating bridge using Kalman-type filtering algorithms

NASA Astrophysics Data System (ADS)

Petersen, Ø. W.; Øiseth, O.; Nord, T. S.; Lourens, E.

2018-07-01

Numerical predictions of the dynamic response of complex structures are often uncertain due to uncertainties inherited from the assumed load effects. Inverse methods can estimate the true dynamic response of a structure through system inversion, combining measured acceleration data with a system model. This article presents a case study of full-field dynamic response estimation of a long-span floating bridge: the Bergøysund Bridge in Norway. This bridge is instrumented with a network of 14 triaxial accelerometers. The system model consists of 27 vibration modes with natural frequencies below 2 Hz, obtained from a tuned finite element model that takes the fluid-structure interaction with the surrounding water into account. Two methods, a joint input-state estimation algorithm and a dual Kalman filter, are applied to estimate the full-field response of the bridge. The results demonstrate that the displacements and the accelerations can be estimated at unmeasured locations with reasonable accuracy when the wave loads are the dominant source of excitation.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1997-03-01

The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

[Formula: see text]-regularized recursive total least squares based sparse system identification for the error-in-variables.

PubMed

Lim, Jun-Seok; Pang, Hee-Suk

2016-01-01

In this paper an [Formula: see text]-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed [Formula: see text]-RTLS algorithm is an RLS like iteration using the [Formula: see text] regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed [Formula: see text]-regularized RTLS for the sparse system identification setting.

Dictionary learning based noisy image super-resolution via distance penalty weight model

PubMed Central

Han, Yulan; Zhao, Yongping; Wang, Qisong

2017-01-01

In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of our method, LR example images are noise-free. For different input LR images, even if the noise variance varies, the dictionary pair does not need to be retrained. For the input LR image patch, the corresponding high resolution (HR) image patch is reconstructed through weighted average of similar HR example patches. To reduce computational cost, we use the atoms of learned sparse dictionary as the examples instead of original example patches. We proposed a distance penalty model for calculating the weight, which can complete a second selection on similar atoms at the same time. Moreover, LR example patches removed mean pixel value are also used to learn dictionary rather than just their gradient features. Based on this, we can reconstruct initial estimated HR image and denoised LR image. Combined with iterative back projection, the two reconstructed images are applied to obtain final estimated HR image. We validate our algorithm on natural images and compared with the previously reported algorithms. Experimental results show that our proposed method performs better noise robustness. PMID:28759633

An improved non-uniformity correction algorithm and its GPU parallel implementation

NASA Astrophysics Data System (ADS)

Cheng, Kuanhong; Zhou, Huixin; Qin, Hanlin; Zhao, Dong; Qian, Kun; Rong, Shenghui

2018-05-01

The performance of SLP-THP based non-uniformity correction algorithm is seriously affected by the result of SLP filter, which always leads to image blurring and ghosting artifacts. To address this problem, an improved SLP-THP based non-uniformity correction method with curvature constraint was proposed. Here we put forward a new way to estimate spatial low frequency component. First, the details and contours of input image were obtained respectively by minimizing local Gaussian curvature and mean curvature of image surface. Then, the guided filter was utilized to combine these two parts together to get the estimate of spatial low frequency component. Finally, we brought this SLP component into SLP-THP method to achieve non-uniformity correction. The performance of proposed algorithm was verified by several real and simulated infrared image sequences. The experimental results indicated that the proposed algorithm can reduce the non-uniformity without detail losing. After that, a GPU based parallel implementation that runs 150 times faster than CPU was presented, which showed the proposed algorithm has great potential for real time application.

An Overview of the Naval Research Laboratory Ocean Surface Flux (NFLUX) System

NASA Astrophysics Data System (ADS)

May, J. C.; Rowley, C. D.; Barron, C. N.

2016-02-01

The Naval Research Laboratory (NRL) ocean surface flux (NFLUX) system is an end-to-end data processing and assimilation system used to provide near-real time satellite-based surface heat flux fields over the global ocean. Swath-level air temperature (TA), specific humidity (QA), and wind speed (WS) estimates are produced using multiple polynomial regression algorithms with inputs from satellite sensor data records from the Special Sensor Microwave Imager/Sounder, the Advanced Microwave Sounding Unit-A, the Advanced Technology Microwave Sounder, and the Advanced Microwave Scanning Radiometer-2 sensors. Swath-level WS estimates are also retrieved from satellite environmental data records from WindSat, the MetOp scatterometers, and the Oceansat scatterometer. Swath-level solar and longwave radiative flux estimates are produced utilizing the Rapid Radiative Transfer Model for Global Circulation Models (RRTMG). Primary inputs to the RRTMG include temperature and moisture profiles and cloud liquid and ice water paths from the Microwave Integrated Retrieval System. All swath-level satellite estimates undergo an automated quality control process and are then assimilated with atmospheric model forecasts to produce 3-hourly gridded analysis fields. The turbulent heat flux fields, latent and sensible heat flux, are determined from the Coupled Ocean-Atmosphere Response Experiment (COARE) 3.0 bulk algorithms using inputs of TA, QA, WS, and a sea surface temperature model field. Quality-controlled in situ observations over a one-year time period from May 2013 through April 2014 form the reference for validating ocean surface state parameter and heat flux fields. The NFLUX fields are evaluated alongside the Navy's operational global atmospheric model, the Navy Global Environmental Model (NAVGEM). NFLUX is shown to have smaller biases and lower or similar root mean square errors compared to NAVGEM.

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region.

PubMed

Wei, Xiangqin; Gu, Xingfa; Meng, Qingyan; Yu, Tao; Zhou, Xiang; Wei, Zheng; Jia, Kun; Wang, Chunmei

2017-07-08

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R² = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.

Multiple estimation channel decoupling and optimization method based on inverse system

NASA Astrophysics Data System (ADS)

Wu, Peng; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

This paper addressed the intelligent autonomous navigation request of intelligent deformation missile, based on the intelligent deformation missile dynamics and kinematics modeling, navigation subsystem solution method and error modeling, and then focuses on the corresponding data fusion and decision fusion technology, decouples the sensitive channel of the filter input through the inverse system of design dynamics to reduce the influence of sudden change of the measurement information on the filter input. Then carrying out a series of simulation experiments, which verified the feasibility of the inverse system decoupling algorithm effectiveness.

Evaluation and design of a rain gauge network using a statistical optimization method in a severe hydro-geological hazard prone area

NASA Astrophysics Data System (ADS)

Fattoruso, Grazia; Longobardi, Antonia; Pizzuti, Alfredo; Molinara, Mario; Marocco, Claudio; De Vito, Saverio; Tortorella, Francesco; Di Francia, Girolamo

2017-06-01

Rainfall data collection gathered in continuous by a distributed rain gauge network is instrumental to more effective hydro-geological risk forecasting and management services though the input estimated rainfall fields suffer from prediction uncertainty. Optimal rain gauge networks can generate accurate estimated rainfall fields. In this research work, a methodology has been investigated for evaluating an optimal rain gauges network aimed at robust hydrogeological hazard investigations. The rain gauges of the Sarno River basin (Southern Italy) has been evaluated by optimizing a two-objective function that maximizes the estimated accuracy and minimizes the total metering cost through the variance reduction algorithm along with the climatological variogram (time-invariant). This problem has been solved by using an enumerative search algorithm, evaluating the exact Pareto-front by an efficient computational time.

An algorithm for the estimation of the signal-to-noise ratio in surface myoelectric signals generated during cyclic movements.

PubMed

Agostini, Valentina; Knaflitz, Marco

2012-01-01

In many applications requiring the study of the surface myoelectric signal (SMES) acquired in dynamic conditions, it is essential to have a quantitative evaluation of the quality of the collected signals. When the activation pattern of a muscle has to be obtained by means of single- or double-threshold statistical detectors, the background noise level e (noise) of the signal is a necessary input parameter. Moreover, the detection strategy of double-threshold detectors may be properly tuned when the SNR and the duty cycle (DC) of the signal are known. The aim of this paper is to present an algorithm for the estimation of e (noise), SNR, and DC of an SMES collected during cyclic movements. The algorithm is validated on synthetic signals with statistical properties similar to those of SMES, as well as on more than 100 real signals. © 2011 IEEE

Modeling uncertainty and correlation in soil properties using Restricted Pairing and implications for ensemble-based hillslope-scale soil moisture and temperature estimation

NASA Astrophysics Data System (ADS)

Flores, A. N.; Entekhabi, D.; Bras, R. L.

2007-12-01

Soil hydraulic and thermal properties (SHTPs) affect both the rate of moisture redistribution in the soil column and the volumetric soil water capacity. Adequately constraining these properties through field and lab analysis to parameterize spatially-distributed hydrology models is often prohibitively expensive. Because SHTPs vary significantly at small spatial scales individual soil samples are also only reliably indicative of local conditions, and these properties remain a significant source of uncertainty in soil moisture and temperature estimation. In ensemble-based soil moisture data assimilation, uncertainty in the model-produced prior estimate due to associated uncertainty in SHTPs must be taken into account to avoid under-dispersive ensembles. To treat SHTP uncertainty for purposes of supplying inputs to a distributed watershed model we use the restricted pairing (RP) algorithm, an extension of Latin Hypercube (LH) sampling. The RP algorithm generates an arbitrary number of SHTP combinations by sampling the appropriate marginal distributions of the individual soil properties using the LH approach, while imposing a target rank correlation among the properties. A previously-published meta- database of 1309 soils representing 12 textural classes is used to fit appropriate marginal distributions to the properties and compute the target rank correlation structure, conditioned on soil texture. Given categorical soil textures, our implementation of the RP algorithm generates an arbitrarily-sized ensemble of realizations of the SHTPs required as input to the TIN-based Realtime Integrated Basin Simulator with vegetation dynamics (tRIBS+VEGGIE) distributed parameter ecohydrology model. Soil moisture ensembles simulated with RP- generated SHTPs exhibit less variance than ensembles simulated with SHTPs generated by a scheme that neglects correlation among properties. Neglecting correlation among SHTPs can lead to physically unrealistic combinations of parameters that exhibit implausible hydrologic behavior when input to the tRIBS+VEGGIE model.

Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

PubMed Central

Commowick, Olivier; Akhondi-Asl, Alireza; Warfield, Simon K.

2012-01-01

We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new Maximum A Posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the- art algorithms. PMID:22562727

Parameterization of typhoon-induced ocean cooling using temperature equation and machine learning algorithms: an example of typhoon Soulik (2013)

NASA Astrophysics Data System (ADS)

Wei, Jun; Jiang, Guo-Qing; Liu, Xin

2017-09-01

This study proposed three algorithms that can potentially be used to provide sea surface temperature (SST) conditions for typhoon prediction models. Different from traditional data assimilation approaches, which provide prescribed initial/boundary conditions, our proposed algorithms aim to resolve a flow-dependent SST feedback between growing typhoons and oceans in the future time. Two of these algorithms are based on linear temperature equations (TE-based), and the other is based on an innovative technique involving machine learning (ML-based). The algorithms are then implemented into a Weather Research and Forecasting model for the simulation of typhoon to assess their effectiveness, and the results show significant improvement in simulated storm intensities by including ocean cooling feedback. The TE-based algorithm I considers wind-induced ocean vertical mixing and upwelling processes only, and thus obtained a synoptic and relatively smooth sea surface temperature cooling. The TE-based algorithm II incorporates not only typhoon winds but also ocean information, and thus resolves more cooling features. The ML-based algorithm is based on a neural network, consisting of multiple layers of input variables and neurons, and produces the best estimate of the cooling structure, in terms of its amplitude and position. Sensitivity analysis indicated that the typhoon-induced ocean cooling is a nonlinear process involving interactions of multiple atmospheric and oceanic variables. Therefore, with an appropriate selection of input variables and neuron sizes, the ML-based algorithm appears to be more efficient in prognosing the typhoon-induced ocean cooling and in predicting typhoon intensity than those algorithms based on linear regression methods.

Development of a new time domain-based algorithm for train detection and axle counting

NASA Astrophysics Data System (ADS)

Allotta, B.; D'Adamio, P.; Meli, E.; Pugi, L.

2015-12-01

This paper presents an innovative train detection algorithm, able to perform the train localisation and, at the same time, to estimate its speed, the crossing times on a fixed point of the track and the axle number. The proposed solution uses the same approach to evaluate all these quantities, starting from the knowledge of generic track inputs directly measured on the track (for example, the vertical forces on the sleepers, the rail deformation and the rail stress). More particularly, all the inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. This approach has the advantage to be simple and less invasive than the standard ones (it requires less equipment) and represents a more reliable and robust solution against numerical noise because it exploits the whole shape of the input signal and not only the peak values. A suitable and accurate multibody model of railway vehicle and flexible track has also been developed by the authors to test the algorithm when experimental data are not available and in general, under any operating conditions (fundamental to verify the algorithm accuracy and robustness). The railway vehicle chosen as benchmark is the Manchester Wagon, modelled in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed to verify the performance and the robustness of the proposed algorithm, and the results are quite promising. The research has been carried out in cooperation with Ansaldo STS and ECM Spa.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Robust Blind Learning Algorithm for Nonlinear Equalization Using Input Decision Information.

PubMed

Xu, Lu; Huang, Defeng David; Guo, Yingjie Jay

2015-12-01

In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.

A Monte Carlo comparison of the recovery of winds near upwind and downwind from the SASS-1 model function by means of the sum of squares algorithm and a maximum likelihood estimator

NASA Technical Reports Server (NTRS)

Pierson, W. J., Jr.

1984-01-01

Backscatter measurements at upwind and crosswind are simulated for five incidence angles by means of the SASS-1 model function. The effects of communication noise and attitude errors are simulated by Monte Carlo methods, and the winds are recovered by both the Sum of Square (SOS) algorithm and a Maximum Likelihood Estimater (MLE). The SOS algorithm is shown to fail for light enough winds at all incidence angles and to fail to show areas of calm because backscatter estimates that were negative or that produced incorrect values of K sub p greater than one were discarded. The MLE performs well for all input backscatter estimates and returns calm when both are negative. The use of the SOS algorithm is shown to have introduced errors in the SASS-1 model function that, in part, cancel out the errors that result from using it, but that also cause disagreement with other data sources such as the AAFE circle flight data at light winds. Implications for future scatterometer systems are given.

Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

NASA Astrophysics Data System (ADS)

Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman

2016-09-01

The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.

A Height Estimation Approach for Terrain Following Flights from Monocular Vision.

PubMed

Campos, Igor S G; Nascimento, Erickson R; Freitas, Gustavo M; Chaimowicz, Luiz

2016-12-06

In this paper, we present a monocular vision-based height estimation algorithm for terrain following flights. The impressive growth of Unmanned Aerial Vehicle (UAV) usage, notably in mapping applications, will soon require the creation of new technologies to enable these systems to better perceive their surroundings. Specifically, we chose to tackle the terrain following problem, as it is still unresolved for consumer available systems. Virtually every mapping aircraft carries a camera; therefore, we chose to exploit this in order to use presently available hardware to extract the height information toward performing terrain following flights. The proposed methodology consists of using optical flow to track features from videos obtained by the UAV, as well as its motion information to estimate the flying height. To determine if the height estimation is reliable, we trained a decision tree that takes the optical flow information as input and classifies whether the output is trustworthy or not. The classifier achieved accuracies of 80 % for positives and 90 % for negatives, while the height estimation algorithm presented good accuracy.

Automatic energy expenditure measurement for health science.

PubMed

Catal, Cagatay; Akbulut, Akhan

2018-04-01

It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results. Copyright © 2018 Elsevier B.V. All rights reserved.

Simultaneous Estimation of Electromechanical Modes and Forced Oscillations

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

Follum, Jim; Pierre, John W.; Martin, Russell

Over the past several years, great strides have been made in the effort to monitor the small-signal stability of power systems. These efforts focus on estimating electromechanical modes, which are a property of the system that dictate how generators in different parts of the system exchange energy. Though the algorithms designed for this task are powerful and important for reliable operation of the power system, they are susceptible to severe bias when forced oscillations are present in the system. Forced oscillations are fundamentally different from electromechanical oscillations in that they are the result of a rogue input to the system,more » rather than a property of the system itself. To address the presence of forced oscillations, the frequently used AutoRegressive Moving Average (ARMA) model is adapted to include sinusoidal inputs, resulting in the AutoRegressive Moving Average plus Sinusoid (ARMA+S) model. From this model, a new Two-Stage Least Squares algorithm is derived to incorporate the forced oscillations, thereby enabling the simultaneous estimation of the electromechanical modes and the amplitude and phase of the forced oscillations. The method is validated using simulated power system data as well as data obtained from the western North American power system (wNAPS) and Eastern Interconnection (EI).« less

An Estimation of Turbulent Kinetic Energy and Energy Dissipation Rate Based on Atmospheric Boundary Layer Similarity Theory

NASA Technical Reports Server (NTRS)

Han, Jongil; Arya, S. Pal; Shaohua, Shen; Lin, Yuh-Lang; Proctor, Fred H. (Technical Monitor)

2000-01-01

Algorithms are developed to extract atmospheric boundary layer profiles for turbulence kinetic energy (TKE) and energy dissipation rate (EDR), with data from a meteorological tower as input. The profiles are based on similarity theory and scalings for the atmospheric boundary layer. The calculated profiles of EDR and TKE are required to match the observed values at 5 and 40 m. The algorithms are coded for operational use and yield plausible profiles over the diurnal variation of the atmospheric boundary layer.

Efficient stochastic approaches for sensitivity studies of an Eulerian large-scale air pollution model

NASA Astrophysics Data System (ADS)

Dimov, I.; Georgieva, R.; Todorov, V.; Ostromsky, Tz.

2017-10-01

Reliability of large-scale mathematical models is an important issue when such models are used to support decision makers. Sensitivity analysis of model outputs to variation or natural uncertainties of model inputs is crucial for improving the reliability of mathematical models. A comprehensive experimental study of Monte Carlo algorithms based on Sobol sequences for multidimensional numerical integration has been done. A comparison with Latin hypercube sampling and a particular quasi-Monte Carlo lattice rule based on generalized Fibonacci numbers has been presented. The algorithms have been successfully applied to compute global Sobol sensitivity measures corresponding to the influence of several input parameters (six chemical reactions rates and four different groups of pollutants) on the concentrations of important air pollutants. The concentration values have been generated by the Unified Danish Eulerian Model. The sensitivity study has been done for the areas of several European cities with different geographical locations. The numerical tests show that the stochastic algorithms under consideration are efficient for multidimensional integration and especially for computing small by value sensitivity indices. It is a crucial element since even small indices may be important to be estimated in order to achieve a more accurate distribution of inputs influence and a more reliable interpretation of the mathematical model results.

Efficient FPT Algorithms for (Strict) Compatibility of Unrooted Phylogenetic Trees.

PubMed

Baste, Julien; Paul, Christophe; Sau, Ignasi; Scornavacca, Celine

2017-04-01

In phylogenetics, a central problem is to infer the evolutionary relationships between a set of species X; these relationships are often depicted via a phylogenetic tree-a tree having its leaves labeled bijectively by elements of X and without degree-2 nodes-called the "species tree." One common approach for reconstructing a species tree consists in first constructing several phylogenetic trees from primary data (e.g., DNA sequences originating from some species in X), and then constructing a single phylogenetic tree maximizing the "concordance" with the input trees. The obtained tree is our estimation of the species tree and, when the input trees are defined on overlapping-but not identical-sets of labels, is called "supertree." In this paper, we focus on two problems that are central when combining phylogenetic trees into a supertree: the compatibility and the strict compatibility problems for unrooted phylogenetic trees. These problems are strongly related, respectively, to the notions of "containing as a minor" and "containing as a topological minor" in the graph community. Both problems are known to be fixed parameter tractable in the number of input trees k, by using their expressibility in monadic second-order logic and a reduction to graphs of bounded treewidth. Motivated by the fact that the dependency on k of these algorithms is prohibitively large, we give the first explicit dynamic programming algorithms for solving these problems, both running in time [Formula: see text], where n is the total size of the input.

Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy.

PubMed

Nouri, S; Hosseini Pooya, S M; Soltani Nabipour, J

2017-03-01

The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO) estimating tumor positions in real-time radiotherapy. One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. The internal target volume (ITV) should be determined based on the applied neural network algorithm on training steps.

Estimating Reservoir Inflow Using RADAR Forecasted Precipitation and Adaptive Neuro Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Yi, J.; Choi, C.

2014-12-01

Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.

An effective one-dimensional anisotropic fingerprint enhancement algorithm

NASA Astrophysics Data System (ADS)

Ye, Zhendong; Xie, Mei

2012-01-01

Fingerprint identification is one of the most important biometric technologies. The performance of the minutiae extraction and the speed of the fingerprint verification system rely heavily on the quality of the input fingerprint images, so the enhancement of the low fingerprint is a critical and difficult step in a fingerprint verification system. In this paper we proposed an effective algorithm for fingerprint enhancement. Firstly we use normalization algorithm to reduce the variations in gray level values along ridges and valleys. Then we utilize the structure tensor approach to estimate each pixel of the fingerprint orientations. At last we propose a novel algorithm which combines the advantages of onedimensional Gabor filtering method and anisotropic method to enhance the fingerprint in recoverable region. The proposed algorithm has been evaluated on the database of Fingerprint Verification Competition 2004, and the results show that our algorithm performs within less time.

An effective one-dimensional anisotropic fingerprint enhancement algorithm

NASA Astrophysics Data System (ADS)

Ye, Zhendong; Xie, Mei

2011-12-01

Fingerprint identification is one of the most important biometric technologies. The performance of the minutiae extraction and the speed of the fingerprint verification system rely heavily on the quality of the input fingerprint images, so the enhancement of the low fingerprint is a critical and difficult step in a fingerprint verification system. In this paper we proposed an effective algorithm for fingerprint enhancement. Firstly we use normalization algorithm to reduce the variations in gray level values along ridges and valleys. Then we utilize the structure tensor approach to estimate each pixel of the fingerprint orientations. At last we propose a novel algorithm which combines the advantages of onedimensional Gabor filtering method and anisotropic method to enhance the fingerprint in recoverable region. The proposed algorithm has been evaluated on the database of Fingerprint Verification Competition 2004, and the results show that our algorithm performs within less time.

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

PubMed

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

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

Input preshaping with frequency domain information for flexible-link manipulator control

NASA Technical Reports Server (NTRS)

Tzes, Anthony; Englehart, Matthew J.; Yurkovich, Stephen

1989-01-01

The application of an input preshaping scheme to flexible manipulators is considered. The resulting control corresponds to a feedforward term that convolves in real-time the desired reference input with a sequence of impulses and produces a vibration free output. The robustness of the algorithm with respect to injected disturbances and modal frequency variations is not satisfactory and can be improved by convolving the input with a longer sequence of impulses. The incorporation of the preshaping scheme to a closed-loop plant, using acceleration feedback, offers satisfactory disturbance rejection due to feedback and cancellation of the flexible mode effects due to the preshaping. A frequency domain identification scheme is used to estimate the modal frequencies on-line and subsequently update the spacing between the impulses. The combined adaptive input preshaping scheme provides the fastest possible slew that results in a vibration free output.

System identification using Nuclear Norm & Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ahmed, Asif A.; Schoen, Marco P.; Bosworth, Ken W.

2018-01-01

In recent years, subspace System Identification (SI) algorithms have seen increased research, stemming from advanced minimization methods being applied to the Nuclear Norm (NN) approach in system identification. These minimization algorithms are based on hard computing methodologies. To the authors’ knowledge, as of now, there has been no work reported that utilizes soft computing algorithms to address the minimization problem within the nuclear norm SI framework. A linear, time-invariant, discrete time system is used in this work as the basic model for characterizing a dynamical system to be identified. The main objective is to extract a mathematical model from collected experimental input-output data. Hankel matrices are constructed from experimental data, and the extended observability matrix is employed to define an estimated output of the system. This estimated output and the actual - measured - output are utilized to construct a minimization problem. An embedded rank measure assures minimum state realization outcomes. Current NN-SI algorithms employ hard computing algorithms for minimization. In this work, we propose a simple Tabu Search (TS) algorithm for minimization. TS algorithm based SI is compared with the iterative Alternating Direction Method of Multipliers (ADMM) line search optimization based NN-SI. For comparison, several different benchmark system identification problems are solved by both approaches. Results show improved performance of the proposed SI-TS algorithm compared to the NN-SI ADMM algorithm.

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1996-05-01

The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized int eh recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes form the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the popular efforts at designing nonlinear estimation algorithms for tracking applications, the principle one being the reduction of mathematical and computational complexities. A system architecture that efficiently integrates the processing capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described in this paper.

Evaluation of odometry algorithm performances using a railway vehicle dynamic model

NASA Astrophysics Data System (ADS)

Allotta, B.; Pugi, L.; Ridolfi, A.; Malvezzi, M.; Vettori, G.; Rindi, A.

2012-05-01

In modern railway Automatic Train Protection and Automatic Train Control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. Simplified two-dimensional models of railway vehicles have been usually used for Hardware in the Loop test rig testing of conventional odometry algorithms and of on-board safety relevant subsystems (like the Wheel Slide Protection braking system) in which the train speed is estimated from the measures of the wheel angular speed. Two-dimensional models are not suitable to develop solutions like the inertial type localisation algorithms (using 3D accelerometers and 3D gyroscopes) and the introduction of Global Positioning System (or similar) or the magnetometer. In order to test these algorithms correctly and increase odometry performances, a three-dimensional multibody model of a railway vehicle has been developed, using Matlab-Simulink™, including an efficient contact model which can simulate degraded adhesion conditions (the development and prototyping of odometry algorithms involve the simulation of realistic environmental conditions). In this paper, the authors show how a 3D railway vehicle model, able to simulate the complex interactions arising between different on-board subsystems, can be useful to evaluate the odometry algorithm and safety relevant to on-board subsystem performances.

Multi-Target Angle Tracking Algorithm for Bistatic MIMO Radar Based on the Elements of the Covariance Matrix

PubMed Central

Zhang, Zhengyan; Zhang, Jianyun; Zhou, Qingsong; Li, Xiaobo

2018-01-01

In this paper, we consider the problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple targets for bistatic multiple-input multiple-output (MIMO) radar. A high-precision tracking algorithm for target angle is proposed. First, the linear relationship between the covariance matrix difference and the angle difference of the adjacent moment was obtained through three approximate relations. Then, the proposed algorithm obtained the relationship between the elements in the covariance matrix difference. On this basis, the performance of the algorithm was improved by averaging the covariance matrix element. Finally, the least square method was used to estimate the DOD and DOA. The algorithm realized the automatic correlation of the angle and provided better performance when compared with the adaptive asymmetric joint diagonalization (AAJD) algorithm. The simulation results demonstrated the effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar. PMID:29518957

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Self-tuning control of attitude and momentum management for the Space Station

NASA Technical Reports Server (NTRS)

Shieh, L. S.; Sunkel, J. W.; Yuan, Z. Z.; Zhao, X. M.

1992-01-01

This paper presents a hybrid state-space self-tuning design methodology using dual-rate sampling for suboptimal digital adaptive control of attitude and momentum management for the Space Station. This new hybrid adaptive control scheme combines an on-line recursive estimation algorithm for indirectly identifying the parameters of a continuous-time system from the available fast-rate sampled data of the inputs and states and a controller synthesis algorithm for indirectly finding the slow-rate suboptimal digital controller from the designed optimal analog controller. The proposed method enables the development of digitally implementable control algorithms for the robust control of Space Station Freedom with unknown environmental disturbances and slowly time-varying dynamics.

Gaussian-input Gaussian mixture model for representing density maps and atomic models.

PubMed

Kawabata, Takeshi

2018-07-01

A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well; however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a down-sampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

NASA Astrophysics Data System (ADS)

Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

2008-07-01

SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

Spatiotemporal Local-Remote Senor Fusion (ST-LRSF) for Cooperative Vehicle Positioning.

PubMed

Jeong, Han-You; Nguyen, Hoa-Hung; Bhawiyuga, Adhitya

2018-04-04

Vehicle positioning plays an important role in the design of protocols, algorithms, and applications in the intelligent transport systems. In this paper, we present a new framework of spatiotemporal local-remote sensor fusion (ST-LRSF) that cooperatively improves the accuracy of absolute vehicle positioning based on two state estimates of a vehicle in the vicinity: a local sensing estimate, measured by the on-board exteroceptive sensors, and a remote sensing estimate, received from neighbor vehicles via vehicle-to-everything communications. Given both estimates of vehicle state, the ST-LRSF scheme identifies the set of vehicles in the vicinity, determines the reference vehicle state, proposes a spatiotemporal dissimilarity metric between two reference vehicle states, and presents a greedy algorithm to compute a minimal weighted matching (MWM) between them. Given the outcome of MWM, the theoretical position uncertainty of the proposed refinement algorithm is proven to be inversely proportional to the square root of matching size. To further reduce the positioning uncertainty, we also develop an extended Kalman filter model with the refined position of ST-LRSF as one of the measurement inputs. The numerical results demonstrate that the proposed ST-LRSF framework can achieve high positioning accuracy for many different scenarios of cooperative vehicle positioning.

A new approach to estimating evaporation from lakes and reservoirs based on energy balance and remote sensing data

NASA Astrophysics Data System (ADS)

Majidi, Maysam; Sadeghi, Morteza; Shafiei, Mojtaba; Alizadeh, Amin; Farid, Alireza; Azad, Mohammadreza; Vazifedoust, Majid

2016-04-01

Estimating evaporation from water bodies such as lakes and reservoirs is commonly a difficult task, especially due to the lack of reliable and available ground data. Remote sensing (RS) data has shown a great potential for filling the gap. Nonetheless, interpretation of the RS data (e.g. optical reflectance, thermal emission, etc.) for estimating water evaporation has remained as a challenge. In this paper, we present a novel approach for estimating water evaporation based on satellite RS data and some readily measurable ground data. In the proposed approach, named as "Reference and Water surface Energy Balance (RWEB)", we define a reference surface and then solve the energy balance equation simultaneously for the reference surfaces and water surface. This approach was tested over the Doosti dam reservoir (north east of Iran) using whether station and RS data as well as water temperature measured biweekly along the study. Accuracy of the RWEB algorithm was examined by comparison to the standard "Bowen Ratio Energy Balance (BREB)" RS algorithm. The RMSD value of 0.047 mm/year indicated a good agreement between RWEB and BREB algorithms, while RWEB provides an easier-to-use approach regarding its required input variables.

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

Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

PubMed Central

Michailidis, George

2014-01-01

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network. PMID:24586224

Application of the Approximate Bayesian Computation methods in the stochastic estimation of atmospheric contamination parameters for mobile sources

NASA Astrophysics Data System (ADS)

Kopka, Piotr; Wawrzynczak, Anna; Borysiewicz, Mieczyslaw

2016-11-01

In this paper the Bayesian methodology, known as Approximate Bayesian Computation (ABC), is applied to the problem of the atmospheric contamination source identification. The algorithm input data are on-line arriving concentrations of the released substance registered by the distributed sensors network. This paper presents the Sequential ABC algorithm in detail and tests its efficiency in estimation of probabilistic distributions of atmospheric release parameters of a mobile contamination source. The developed algorithms are tested using the data from Over-Land Atmospheric Diffusion (OLAD) field tracer experiment. The paper demonstrates estimation of seven parameters characterizing the contamination source, i.e.: contamination source starting position (x,y), the direction of the motion of the source (d), its velocity (v), release rate (q), start time of release (ts) and its duration (td). The online-arriving new concentrations dynamically update the probability distributions of search parameters. The atmospheric dispersion Second-order Closure Integrated PUFF (SCIPUFF) Model is used as the forward model to predict the concentrations at the sensors locations.

Evaluation of the operational SAR based Baltic sea ice concentration products

NASA Astrophysics Data System (ADS)

Karvonen, Juha

Sea ice concentration is an important ice parameter both for weather and climate modeling and sea ice navigation. We have developed an fully automated algorithm for sea ice concentration retrieval using dual-polarized ScanSAR wide mode RADARSAT-2 data. RADARSAT-2 is a C-band SAR instrument enabling dual-polarized acquisition in ScanSAR mode. The swath width for the RADARSAT-2 ScanSAR mode is about 500 km, making it very suitable for operational sea ice monitoring. The polarization combination used in our concentration estimation is HH/HV. The SAR data is first preprocessed, the preprocessing consists of geo-rectification to Mercator projection, incidence angle correction fro both the polarization channels. and SAR mosaicking. After preprocessing a segmentation is performed for the SAR mosaics, and some single-channel and dual-channel features are computed for each SAR segment. Finally the SAR concentration is estimated based on these segment-wise features. The algorithm is similar as introduced in Karvonen 2014. The ice concentration is computed daily using a daily RADARSAT-2 SAR mosaic as its input, and it thus gives the concentration estimated at each Baltic Sea location based on the most recent SAR data at the location. The algorithm has been run in an operational test mode since January 2014. We present evaluation of the SAR-based concentration estimates for the Baltic ice season 2014 by comparing the SAR results with gridded the Finnish Ice Service ice charts and ice concentration estimates from a radiometer algorithm (AMSR-2 Bootstrap algorithm results). References: J. Karvonen, Baltic Sea Ice Concentration Estimation Based on C-Band Dual-Polarized SAR Data, IEEE Transactions on Geoscience and Remote Sensing, in press, DOI: 10.1109/TGRS.2013.2290331, 2014.

Incorporating spatial context into statistical classification of multidimensional image data

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Tilton, J. C.; Swain, P. H.

1981-01-01

Compound decision theory is employed to develop a general statistical model for classifying image data using spatial context. The classification algorithm developed from this model exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. A key input to this contextural classifier is a quantitative characterization of this tendency: the context function. Several methods for estimating the context function are explored, and two complementary methods are recommended. The contextural classifier is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by a non-contextural uniform-priors maximum likelihood classifier when these methods of estimating the context function are used. An approximate algorithm, which cuts computational requirements by over one-half, is presented. The search for an optimal implementation is furthered by an exploration of the relative merits of using spectral classes or information classes for classification and/or context function estimation.

Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation

NASA Astrophysics Data System (ADS)

Ebrahimian, Hamed; Astroza, Rodrigo; Conte, Joel P.; de Callafon, Raymond A.

2017-02-01

This paper presents a framework for structural health monitoring (SHM) and damage identification of civil structures. This framework integrates advanced mechanics-based nonlinear finite element (FE) modeling and analysis techniques with a batch Bayesian estimation approach to estimate time-invariant model parameters used in the FE model of the structure of interest. The framework uses input excitation and dynamic response of the structure and updates a nonlinear FE model of the structure to minimize the discrepancies between predicted and measured response time histories. The updated FE model can then be interrogated to detect, localize, classify, and quantify the state of damage and predict the remaining useful life of the structure. As opposed to recursive estimation methods, in the batch Bayesian estimation approach, the entire time history of the input excitation and output response of the structure are used as a batch of data to estimate the FE model parameters through a number of iterations. In the case of non-informative prior, the batch Bayesian method leads to an extended maximum likelihood (ML) estimation method to estimate jointly time-invariant model parameters and the measurement noise amplitude. The extended ML estimation problem is solved efficiently using a gradient-based interior-point optimization algorithm. Gradient-based optimization algorithms require the FE response sensitivities with respect to the model parameters to be identified. The FE response sensitivities are computed accurately and efficiently using the direct differentiation method (DDM). The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem by computing the exact Fisher Information matrix using the FE response sensitivities with respect to the model parameters. The accuracy of the proposed uncertainty quantification approach is verified using a sampling approach based on the unscented transformation. Two validation studies, based on realistic structural FE models of a bridge pier and a moment resisting steel frame, are performed to validate the performance and accuracy of the presented nonlinear FE model updating approach and demonstrate its application to SHM. These validation studies show the excellent performance of the proposed framework for SHM and damage identification even in the presence of high measurement noise and/or way-out initial estimates of the model parameters. Furthermore, the detrimental effects of the input measurement noise on the performance of the proposed framework are illustrated and quantified through one of the validation studies.

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve; Urfer, Alex; Schoen, Marco P; Naidu, D Subbaram

2012-01-01

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

Fast human pose estimation using 3D Zernike descriptors

NASA Astrophysics Data System (ADS)

Berjón, Daniel; Morán, Francisco

2012-03-01

Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

Gaussian functional regression for output prediction: Model assimilation and experimental design

NASA Astrophysics Data System (ADS)

Nguyen, N. C.; Peraire, J.

2016-03-01

In this paper, we introduce a Gaussian functional regression (GFR) technique that integrates multi-fidelity models with model reduction to efficiently predict the input-output relationship of a high-fidelity model. The GFR method combines the high-fidelity model with a low-fidelity model to provide an estimate of the output of the high-fidelity model in the form of a posterior distribution that can characterize uncertainty in the prediction. A reduced basis approximation is constructed upon the low-fidelity model and incorporated into the GFR method to yield an inexpensive posterior distribution of the output estimate. As this posterior distribution depends crucially on a set of training inputs at which the high-fidelity models are simulated, we develop a greedy sampling algorithm to select the training inputs. Our approach results in an output prediction model that inherits the fidelity of the high-fidelity model and has the computational complexity of the reduced basis approximation. Numerical results are presented to demonstrate the proposed approach.

Understanding How Kurtosis Is Transferred from Input Acceleration to Stress Response and Its Influence on Fatigue Llife

NASA Technical Reports Server (NTRS)

Kihm, Frederic; Rizzi, Stephen A.; Ferguson, Neil S.; Halfpenny, Andrew

2013-01-01

High cycle fatigue of metals typically occurs through long term exposure to time varying loads which, although modest in amplitude, give rise to microscopic cracks that can ultimately propagate to failure. The fatigue life of a component is primarily dependent on the stress amplitude response at critical failure locations. For most vibration tests, it is common to assume a Gaussian distribution of both the input acceleration and stress response. In real life, however, it is common to experience non-Gaussian acceleration input, and this can cause the response to be non-Gaussian. Examples of non-Gaussian loads include road irregularities such as potholes in the automotive world or turbulent boundary layer pressure fluctuations for the aerospace sector or more generally wind, wave or high amplitude acoustic loads. The paper first reviews some of the methods used to generate non-Gaussian excitation signals with a given power spectral density and kurtosis. The kurtosis of the response is examined once the signal is passed through a linear time invariant system. Finally an algorithm is presented that determines the output kurtosis based upon the input kurtosis, the input power spectral density and the frequency response function of the system. The algorithm is validated using numerical simulations. Direct applications of these results include improved fatigue life estimations and a method to accelerate shaker tests by generating high kurtosis, non-Gaussian drive signals.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-07-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations and in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, using either magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

NASA Astrophysics Data System (ADS)

Rivera, J. D.; Moraes, B.; Merson, A. I.; Jouvel, S.; Abdalla, F. B.; Abdalla, M. C. B.

2018-04-01

We perform an analysis of photometric redshifts estimated by using a non-representative training sets in magnitude space. We use the ANNz2 and GPz algorithms to estimate the photometric redshift both in simulations as well as in real data from the Sloan Digital Sky Survey (DR12). We show that for the representative case, the results obtained by using both algorithms have the same quality, either using magnitudes or colours as input. In order to reduce the errors when estimating the redshifts with a non-representative training set, we perform the training in colour space. We estimate the quality of our results by using a mock catalogue which is split samples cuts in the r-band between 19.4 < r < 20.8. We obtain slightly better results with GPz on single point z-phot estimates in the complete training set case, however the photometric redshifts estimated with ANNz2 algorithm allows us to obtain mildly better results in deeper r-band cuts when estimating the full redshift distribution of the sample in the incomplete training set case. By using a cumulative distribution function and a Monte-Carlo process, we manage to define a photometric estimator which fits well the spectroscopic distribution of galaxies in the mock testing set, but with a larger scatter. To complete this work, we perform an analysis of the impact on the detection of clusters via density of galaxies in a field by using the photometric redshifts obtained with a non-representative training set.

Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1985-01-01

This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Seismic noise attenuation using an online subspace tracking algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yatong; Li, Shuhua; Zhang, Dong; Chen, Yangkang

2018-02-01

We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

Enhanced ID Pit Sizing Using Multivariate Regression Algorithm

NASA Astrophysics Data System (ADS)

Krzywosz, Kenji

2007-03-01

EPRI is funding a program to enhance and improve the reliability of inside diameter (ID) pit sizing for balance-of plant heat exchangers, such as condensers and component cooling water heat exchangers. More traditional approaches to ID pit sizing involve the use of frequency-specific amplitude or phase angles. The enhanced multivariate regression algorithm for ID pit depth sizing incorporates three simultaneous input parameters of frequency, amplitude, and phase angle. A set of calibration data sets consisting of machined pits of various rounded and elongated shapes and depths was acquired in the frequency range of 100 kHz to 1 MHz for stainless steel tubing having nominal wall thickness of 0.028 inch. To add noise to the acquired data set, each test sample was rotated and test data acquired at 3, 6, 9, and 12 o'clock positions. The ID pit depths were estimated using a second order and fourth order regression functions by relying on normalized amplitude and phase angle information from multiple frequencies. Due to unique damage morphology associated with the microbiologically-influenced ID pits, it was necessary to modify the elongated calibration standard-based algorithms by relying on the algorithm developed solely from the destructive sectioning results. This paper presents the use of transformed multivariate regression algorithm to estimate ID pit depths and compare the results with the traditional univariate phase angle analysis. Both estimates were then compared with the destructive sectioning results.

Flight dynamics analysis and simulation of heavy lift airships. Volume 2: Technical manual

NASA Technical Reports Server (NTRS)

Ringland, R. F.; Tischler, M. B.; Jex, H. R.; Emmen, R. D.; Ashkenas, I. L.

1982-01-01

The mathematical models embodied in the simulation are described in considerable detail and with supporting evidence for the model forms chosen. In addition the trimming and linearization algorithms used in the simulation are described. Appendices to the manual identify reference material for estimating the needed coefficients for the input data and provide example simulation results.

Implementation of a Wavefront-Sensing Algorithm

NASA Technical Reports Server (NTRS)

Smith, Jeffrey S.; Dean, Bruce; Aronstein, David

2013-01-01

A computer program has been written as a unique implementation of an image-based wavefront-sensing algorithm reported in "Iterative-Transform Phase Retrieval Using Adaptive Diversity" (GSC-14879-1), NASA Tech Briefs, Vol. 31, No. 4 (April 2007), page 32. This software was originally intended for application to the James Webb Space Telescope, but is also applicable to other segmented-mirror telescopes. The software is capable of determining optical-wavefront information using, as input, a variable number of irradiance measurements collected in defocus planes about the best focal position. The software also uses input of the geometrical definition of the telescope exit pupil (otherwise denoted the pupil mask) to identify the locations of the segments of the primary telescope mirror. From the irradiance data and mask information, the software calculates an estimate of the optical wavefront (a measure of performance) of the telescope generally and across each primary mirror segment specifically. The software is capable of generating irradiance data, wavefront estimates, and basis functions for the full telescope and for each primary-mirror segment. Optionally, each of these pieces of information can be measured or computed outside of the software and incorporated during execution of the software.

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

NASA Technical Reports Server (NTRS)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

An algorithm that improves speech intelligibility in noise for normal-hearing listeners.

PubMed

Kim, Gibak; Lu, Yang; Hu, Yi; Loizou, Philipos C

2009-09-01

Traditional noise-suppression algorithms have been shown to improve speech quality, but not speech intelligibility. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into time-frequency (T-F) units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit is dominated by the target or the masker. Speech corrupted at low signal-to-noise ratio (SNR) levels (-5 and 0 dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility (over 60% points in -5 dB babble) over that attained by human listeners with unprocessed stimuli. The findings from this study suggest that algorithms that can estimate reliably the SNR in each T-F unit can improve speech intelligibility.

Wavelet denoising of multiframe optical coherence tomography data

PubMed Central

Mayer, Markus A.; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y.; Tornow, Ralf P.

2012-01-01

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise. PMID:22435103

Wavelet denoising of multiframe optical coherence tomography data.

PubMed

Mayer, Markus A; Borsdorf, Anja; Wagner, Martin; Hornegger, Joachim; Mardin, Christian Y; Tornow, Ralf P

2012-03-01

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.

FAST-PT: a novel algorithm to calculate convolution integrals in cosmological perturbation theory

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

McEwen, Joseph E.; Fang, Xiao; Hirata, Christopher M.

2016-09-01

We present a novel algorithm, FAST-PT, for performing convolution or mode-coupling integrals that appear in nonlinear cosmological perturbation theory. The algorithm uses several properties of gravitational structure formation—the locality of the dark matter equations and the scale invariance of the problem—as well as Fast Fourier Transforms to describe the input power spectrum as a superposition of power laws. This yields extremely fast performance, enabling mode-coupling integral computations fast enough to embed in Monte Carlo Markov Chain parameter estimation. We describe the algorithm and demonstrate its application to calculating nonlinear corrections to the matter power spectrum, including one-loop standard perturbation theorymore » and the renormalization group approach. We also describe our public code (in Python) to implement this algorithm. The code, along with a user manual and example implementations, is available at https://github.com/JoeMcEwen/FAST-PT.« less

Determination of power system component parameters using nonlinear dead beat estimation method

NASA Astrophysics Data System (ADS)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are introduced in the virtual test systems and the dynamic data obtained in each case is analyzed and recorded. Ideally, actual measurements are to be provided to the algorithm. As the measurements are not readily available the data obtained from simulations is fed into the determination algorithm as inputs. The obtained results are then compared to the original (or assumed) values of the parameters. The results obtained suggest that the algorithm is able to determine the parameters of a synchronous machine when crisp data is available.

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

NASA Astrophysics Data System (ADS)

Lamorski, Krzysztof; Šimūnek, Jiří; Sławiński, Cezary; Lamorska, Joanna

2017-02-01

In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models.

A Predictor Analysis Framework for Surface Radiation Budget Reprocessing Using Design of Experiments

NASA Astrophysics Data System (ADS)

Quigley, Patricia Allison

Earth's Radiation Budget (ERB) is an accounting of all incoming energy from the sun and outgoing energy reflected and radiated to space by earth's surface and atmosphere. The National Aeronautics and Space Administration (NASA)/Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project produces and archives long-term datasets representative of this energy exchange system on a global scale. The data are comprised of the longwave and shortwave radiative components of the system and is algorithmically derived from satellite and atmospheric assimilation products, and acquired atmospheric data. It is stored as 3-hourly, daily, monthly/3-hourly, and monthly averages of 1° x 1° grid cells. Input parameters used by the algorithms are a key source of variability in the resulting output data sets. Sensitivity studies have been conducted to estimate the effects this variability has on the output data sets using linear techniques. This entails varying one input parameter at a time while keeping all others constant or by increasing all input parameters by equal random percentages, in effect changing input values for every cell for every three hour period and for every day in each month. This equates to almost 11 million independent changes without ever taking into consideration the interactions or dependencies among the input parameters. A more comprehensive method is proposed here for the evaluating the shortwave algorithm to identify both the input parameters and parameter interactions that most significantly affect the output data. This research utilized designed experiments that systematically and simultaneously varied all of the input parameters of the shortwave algorithm. A D-Optimal design of experiments (DOE) was chosen to accommodate the 14 types of atmospheric properties computed by the algorithm and to reduce the number of trials required by a full factorial study from millions to 128. A modified version of the algorithm was made available for testing such that global calculations of the algorithm were tuned to accept information for a single temporal and spatial point and for one month of averaged data. The points were from each of four atmospherically distinct regions to include the Amazon Rainforest, Sahara Desert, Indian Ocean and Mt. Everest. The same design was used for all of the regions. Least squares multiple regression analysis of the results of the modified algorithm identified those parameters and parameter interactions that most significantly affected the output products. It was found that Cosine solar zenith angle was the strongest influence on the output data in all four regions. The interaction of Cosine Solar Zenith Angle and Cloud Fraction had the strongest influence on the output data in the Amazon, Sahara Desert and Mt. Everest Regions, while the interaction of Cloud Fraction and Cloudy Shortwave Radiance most significantly affected output data in the Indian Ocean region. Second order response models were built using the resulting regression coefficients. A Monte Carlo simulation of each model extended the probability distribution beyond the initial design trials to quantify variability in the modeled output data.

Generalization of some hidden subgroup algorithms for input sets of arbitrary size

NASA Astrophysics Data System (ADS)

Poslu, Damla; Say, A. C. Cem

2006-05-01

We consider the problem of generalizing some quantum algorithms so that they will work on input domains whose cardinalities are not necessarily powers of two. When analyzing the algorithms we assume that generating superpositions of arbitrary subsets of basis states whose cardinalities are not necessarily powers of two perfectly is possible. We have taken Ballhysa's model as a template and have extended it to Chi, Kim and Lee's generalizations of the Deutsch-Jozsa algorithm and to Simon's algorithm. With perfectly equal superpositions of input sets of arbitrary size, Chi, Kim and Lee's generalized Deutsch-Jozsa algorithms, both for evenly-distributed and evenly-balanced functions, worked with one-sided error property. For Simon's algorithm the success probability of the generalized algorithm is the same as that of the original for input sets of arbitrary cardinalities with equiprobable superpositions, since the property that the measured strings are all those which have dot product zero with the string we search, for the case where the function is 2-to-1, is not lost.

Implementation and testing of a sensor-netting algorithm for early warning and high confidence C/B threat detection

NASA Astrophysics Data System (ADS)

Gruber, Thomas; Grim, Larry; Fauth, Ryan; Tercha, Brian; Powell, Chris; Steinhardt, Kristin

2011-05-01

Large networks of disparate chemical/biological (C/B) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. Sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A data fusion algorithm design was completed in a Phase I SBIR effort and development continues in the Phase II SBIR effort. The initial implementation and testing of the algorithm has produced some performance results. The algorithm accepts point and/or standoff sensor data, and event detection data (e.g., the location of an explosion) from various ISR sensors (e.g., acoustic, infrared cameras, etc.). These input data are preprocessed to assign estimated uncertainty to each incoming piece of data. The data are then sent to a weighted tomography process to obtain a consensus threat map, including estimated threat concentration level uncertainty. The threat map is then tested for consistency and the overall confidence for the map result is estimated. The map and confidence results are displayed on a COP. The benefits of a modular implementation of the algorithm and comparisons of fused / un-fused data results will be presented. The metrics for judging the sensor-netting algorithm performance are warning time, threat map accuracy (as compared to ground truth), false alarm rate, and false alarm rate v. reported threat confidence level.

A fuzzy measure approach to motion frame analysis for scene detection. M.S. Thesis - Houston Univ.

NASA Technical Reports Server (NTRS)

Leigh, Albert B.; Pal, Sankar K.

1992-01-01

This paper addresses a solution to the problem of scene estimation of motion video data in the fuzzy set theoretic framework. Using fuzzy image feature extractors, a new algorithm is developed to compute the change of information in each of two successive frames to classify scenes. This classification process of raw input visual data can be used to establish structure for correlation. The algorithm attempts to fulfill the need for nonlinear, frame-accurate access to video data for applications such as video editing and visual document archival/retrieval systems in multimedia environments.

Detecting Nano-Scale Vibrations in Rotating Devices by Using Advanced Computational Methods

PubMed Central

del Toro, Raúl M.; Haber, Rodolfo E.; Schmittdiel, Michael C.

2010-01-01

This paper presents a computational method for detecting vibrations related to eccentricity in ultra precision rotation devices used for nano-scale manufacturing. The vibration is indirectly measured via a frequency domain analysis of the signal from a piezoelectric sensor attached to the stationary component of the rotating device. The algorithm searches for particular harmonic sequences associated with the eccentricity of the device rotation axis. The detected sequence is quantified and serves as input to a regression model that estimates the eccentricity. A case study presents the application of the computational algorithm during precision manufacturing processes. PMID:22399918

An enhanced multi-channel bacterial foraging optimization algorithm for MIMO communication system

NASA Astrophysics Data System (ADS)

Palanimuthu, Senthilkumar Jayalakshmi; Muthial, Chandrasekaran

2017-04-01

Channel estimation and optimisation are the main challenging tasks in Multi Input Multi Output (MIMO) wireless communication systems. In this work, a Multi-Channel Bacterial Foraging Optimization Algorithm approach is proposed for the selection of antenna in a transmission area. The main advantage of this method is, it reduces the loss of bandwidth during data transmission effectively. Here, we considered the channel estimation and optimisation for improving the transmission speed and reducing the unused bandwidth. Initially, the message is given to the input of the communication system. Then, the symbol mapping process is performed for converting the message into signals. It will be encoded based on the space-time encoding technique. Here, the single signal is divided into multiple signals and it will be given to the input of space-time precoder. Hence, the multiplexing is applied to transmission channel estimation. In this paper, the Rayleigh channel is selected based on the bandwidth range. This is the Gaussian distribution type channel. Then, the demultiplexing is applied on the obtained signal that is the reverse function of multiplexing, which splits the combined signal arriving from a medium into the original information signal. Furthermore, the long-term evolution technique is used for scheduling the time to channels during transmission. Here, the hidden Markov model technique is employed to predict the status information of the channel. Finally, the signals are decoded and the reconstructed signal is obtained after performing the scheduling process. The experimental results evaluate the performance of the proposed MIMO communication system in terms of bit error rate, mean squared error, average throughput, outage capacity and signal to interference noise ratio.

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Swain, P. H. (Principal Investigator); Vardeman, S. B.

1981-01-01

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection

PubMed Central

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-01-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency. PMID:29194393

Developing a Novel Parameter Estimation Method for Agent-Based Model in Immune System Simulation under the Framework of History Matching: A Case Study on Influenza A Virus Infection.

PubMed

Li, Tingting; Cheng, Zhengguo; Zhang, Le

2017-12-01

Since they can provide a natural and flexible description of nonlinear dynamic behavior of complex system, Agent-based models (ABM) have been commonly used for immune system simulation. However, it is crucial for ABM to obtain an appropriate estimation for the key parameters of the model by incorporating experimental data. In this paper, a systematic procedure for immune system simulation by integrating the ABM and regression method under the framework of history matching is developed. A novel parameter estimation method by incorporating the experiment data for the simulator ABM during the procedure is proposed. First, we employ ABM as simulator to simulate the immune system. Then, the dimension-reduced type generalized additive model (GAM) is employed to train a statistical regression model by using the input and output data of ABM and play a role as an emulator during history matching. Next, we reduce the input space of parameters by introducing an implausible measure to discard the implausible input values. At last, the estimation of model parameters is obtained using the particle swarm optimization algorithm (PSO) by fitting the experiment data among the non-implausible input values. The real Influeza A Virus (IAV) data set is employed to demonstrate the performance of our proposed method, and the results show that the proposed method not only has good fitting and predicting accuracy, but it also owns favorable computational efficiency.

Zonal Aerosol Direct and Indirect Radiative Forcing using Combined CALIOP, CERES, CloudSat, and CERES Data

NASA Astrophysics Data System (ADS)

Miller, W. F.; Kato, S.; Rose, F. G.; Sun-Mack, S.

2009-12-01

Under the NASA Energy and Water Cycle System (NEWS) program, cloud and aerosol properties derived from CALIPSO, CloudSat, and MODIS data then matched to the CERES footprint are used for irradiance profile computations. Irradiance profiles are included in the publicly available product, CCCM. In addition to the MODIS and CALIPSO generated aerosol, aerosol optical thickness is calculated over ocean by processing MODIS radiance through the Stowe-Ignatov algorithm. The CERES cloud mask and properties algorithm are use with MODIS radiance to provide additional cloud information to accompany the actively sensed data. The passively sensed data is the only input to the standard CERES radiative flux products. The combined information is used as input to the NASA Langley Fu-Liou radiative transfer model to determine vertical profiles and Top of Atmosphere shortwave and longwave flux for pristine, all-sky, and aerosol conditions for the special data product. In this study, the three sources of aerosol optical thickness will be compared directly and their influence on the calculated and measured TOA fluxes. Earlier studies indicate that the largest uncertainty in estimating direct aerosol forcing using aerosol optical thickness derived from passive sensors is caused by cloud contamination. With collocated CALIPSO data, we are able to estimate frequency of occurrence of cloud contamination, effect on the aerosol optical thickness and direct radiative effect estimates.

A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC.

PubMed

Khadke, Piyush; Patne, Nita; Singh, Arvind; Shinde, Gulab

2016-01-01

In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha-Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.

Missing value imputation in DNA microarrays based on conjugate gradient method.

PubMed

Dorri, Fatemeh; Azmi, Paeiz; Dorri, Faezeh

2012-02-01

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Numerical algorithm for rigid body position estimation using the quaternion approach

NASA Astrophysics Data System (ADS)

Zigic, Miodrag; Grahovac, Nenad

2017-11-01

This paper deals with rigid body attitude estimation on the basis of the data obtained from an inertial measurement unit mounted on the body. The aim of this work is to present the numerical algorithm, which can be easily applied to the wide class of problems concerning rigid body positioning, arising in aerospace and marine engineering, or in increasingly popular robotic systems and unmanned aerial vehicles. Following the considerations of kinematics of rigid bodies, the relations between accelerations of different points of the body are given. A rotation matrix is formed using the quaternion approach to avoid singularities. We present numerical procedures for determination of the absolute accelerations of the center of mass and of an arbitrary point of the body expressed in the inertial reference frame, as well as its attitude. An application of the algorithm to the example of a heavy symmetrical gyroscope is presented, where input data for the numerical procedure are obtained from the solution of differential equations of motion, instead of using sensor measurements.

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

DOE PAGES

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

2016-01-01

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

Forward and inverse uncertainty quantification using multilevel Monte Carlo algorithms for an elliptic non-local equation

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

Jasra, Ajay; Law, Kody J. H.; Zhou, Yan

Our paper considers uncertainty quantification for an elliptic nonlocal equation. In particular, it is assumed that the parameters which define the kernel in the nonlocal operator are uncertain and a priori distributed according to a probability measure. It is shown that the induced probability measure on some quantities of interest arising from functionals of the solution to the equation with random inputs is well-defined,s as is the posterior distribution on parameters given observations. As the elliptic nonlocal equation cannot be solved approximate posteriors are constructed. The multilevel Monte Carlo (MLMC) and multilevel sequential Monte Carlo (MLSMC) sampling algorithms are usedmore » for a priori and a posteriori estimation, respectively, of quantities of interest. Furthermore, these algorithms reduce the amount of work to estimate posterior expectations, for a given level of error, relative to Monte Carlo and i.i.d. sampling from the posterior at a given level of approximation of the solution of the elliptic nonlocal equation.« less

A Height Estimation Approach for Terrain Following Flights from Monocular Vision

PubMed Central

Campos, Igor S. G.; Nascimento, Erickson R.; Freitas, Gustavo M.; Chaimowicz, Luiz

2016-01-01

In this paper, we present a monocular vision-based height estimation algorithm for terrain following flights. The impressive growth of Unmanned Aerial Vehicle (UAV) usage, notably in mapping applications, will soon require the creation of new technologies to enable these systems to better perceive their surroundings. Specifically, we chose to tackle the terrain following problem, as it is still unresolved for consumer available systems. Virtually every mapping aircraft carries a camera; therefore, we chose to exploit this in order to use presently available hardware to extract the height information toward performing terrain following flights. The proposed methodology consists of using optical flow to track features from videos obtained by the UAV, as well as its motion information to estimate the flying height. To determine if the height estimation is reliable, we trained a decision tree that takes the optical flow information as input and classifies whether the output is trustworthy or not. The classifier achieved accuracies of 80% for positives and 90% for negatives, while the height estimation algorithm presented good accuracy. PMID:27929424

Comparison of NASA Team2 and AES-York Ice Concentration Algorithms Against Operational Ice Charts From the Canadian Ice Service

NASA Technical Reports Server (NTRS)

Shokr, Mohammed; Markus, Thorsten

2006-01-01

Ice concentration retrieved from spaceborne passive-microwave observations is a prime input to operational sea-ice-monitoring programs, numerical weather prediction models, and global climate models. Atmospheric Environment Service (AES)- York and the Enhanced National Aeronautics and Space Administration Team (NT2) are two algorithms that calculate ice concentration from Special Sensor Microwave/Imager observations. This paper furnishes a comparison between ice concentrations (total, thin, and thick types) output from NT2 and AES-York algorithms against the corresponding estimates from the operational analysis of Radarsat images in the Canadian Ice Service (CIS). A new data fusion technique, which incorporates the actual sensor's footprint, was developed to facilitate this study. Results have shown that the NT2 and AES-York algorithms underestimate total ice concentration by 18.35% and 9.66% concentration counts on average, with 16.8% and 15.35% standard deviation, respectively. However, the retrieved concentrations of thin and thick ice are in much more discrepancy with the operational CIS estimates when either one of these two types dominates the viewing area. This is more likely to occur when the total ice concentration approaches 100%. If thin and thick ice types coexist in comparable concentrations, the algorithms' estimates agree with CIS'S estimates. In terms of ice concentration retrieval, thin ice is more problematic than thick ice. The concept of using a single tie point to represent a thin ice surface is not realistic and provides the largest error source for retrieval accuracy. While AES-York provides total ice concentration in slightly more agreement with CIS'S estimates, NT2 provides better agreement in retrieving thin and thick ice concentrations.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

PubMed Central

Selmants, Paul C.; Moreno, Alvaro; Running, Steve W.; Giardina, Christian P.

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales. PMID:28886187

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

USGS Publications Warehouse

Kimball, Heather L.; Selmants, Paul; Moreno, Alvaro; Running Steve W,; Giardina, Christian P.

2017-01-01

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems.

PubMed

Kimball, Heather L; Selmants, Paul C; Moreno, Alvaro; Running, Steve W; Giardina, Christian P

2017-01-01

Gross primary production (GPP) is the Earth's largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Integrated controls design optimization

DOEpatents

Lou, Xinsheng; Neuschaefer, Carl H.

2015-09-01

A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.

Initial Navigation Alignment of Optical Instruments on GOES-R

NASA Astrophysics Data System (ADS)

Isaacson, P.; DeLuccia, F.; Reth, A. D.; Igli, D. A.; Carter, D.

2016-12-01

The GOES-R satellite is the first in NOAA's next-generation series of geostationary weather satellites. In addition to a number of space weather sensors, it will carry two principal optical earth-observing instruments, the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). During launch, currently scheduled for November of 2016, the alignment of these optical instruments is anticipated to shift from that measured during pre-launch characterization. While both instruments have image navigation and registration (INR) processing algorithms to enable automated geolocation of the collected data, the launch-derived misalignment may be too large for these approaches to function without an initial adjustment to calibration parameters. The parameters that may require adjustment are for Line of Sight Motion Compensation (LMC), and the adjustments will be estimated on orbit during the post-launch test (PLT) phase. We have developed approaches to estimate the initial alignment errors for both ABI and GLM image products. Our approaches involve comparison of ABI and GLM images collected during PLT to a set of reference ("truth") images using custom image processing tools and other software (the INR Performance Assessment Tool Set, or "IPATS") being developed for other INR assessments of ABI and GLM data. IPATS is based on image correlation approaches to determine offsets between input and reference images, and these offsets are the fundamental input to our estimate of the initial alignment errors. Initial testing of our alignment algorithms on proxy datasets lends high confidence that their application will determine the initial alignment errors to within sufficient accuracy to enable the operational INR processing approaches to proceed in a nominal fashion. We will report on the algorithms, implementation approach, and status of these initial alignment tools being developed for the GOES-R ABI and GLM instruments.

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Aarnink, Rene G.; de la Rosette, Jean J.; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, Frans M. J.; Kim, Yongmin

1998-06-01

With the number of men seeking medical care for prostate diseases rising steadily, the need of a fast and accurate prostate boundary detection and volume estimation tool is being increasingly experienced by the clinicians. Currently, these measurements are made manually, which results in a large examination time. A possible solution is to improve the efficiency by automating the boundary detection and volume estimation process with minimal involvement from the human experts. In this paper, we present an algorithm based on SNAKES to detect the boundaries. Our approach is to selectively enhance the contrast along the edges using an algorithm called sticks and integrate it with a SNAKES model. This integrated algorithm requires an initial curve for each ultrasound image to initiate the boundary detection process. We have used different schemes to generate the curves with a varying degree of automation and evaluated its effects on the algorithm performance. After the boundaries are identified, the prostate volume is calculated using planimetric volumetry. We have tested our algorithm on 6 different prostate volumes and compared the performance against the volumes manually measured by 3 experts. With the increase in the user inputs, the algorithm performance improved as expected. The results demonstrate that given an initial contour reasonably close to the prostate boundaries, the algorithm successfully delineates the prostate boundaries in an image, and the resulting volume measurements are in close agreement with those made by the human experts.

Variational optical flow estimation for images with spectral and photometric sensor diversity

NASA Astrophysics Data System (ADS)

Bengtsson, Tomas; McKelvey, Tomas; Lindström, Konstantin

2015-03-01

Motion estimation of objects in image sequences is an essential computer vision task. To this end, optical flow methods compute pixel-level motion, with the purpose of providing low-level input to higher-level algorithms and applications. Robust flow estimation is crucial for the success of applications, which in turn depends on the quality of the captured image data. This work explores the use of sensor diversity in the image data within a framework for variational optical flow. In particular, a custom image sensor setup intended for vehicle applications is tested. Experimental results demonstrate the improved flow estimation performance when IR sensitivity or flash illumination is added to the system.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Offline Performance of the Filter Bank EEW Algorithm in the 2014 M6.0 South Napa Earthquake

NASA Astrophysics Data System (ADS)

Meier, M. A.; Heaton, T. H.; Clinton, J. F.

2014-12-01

Medium size events like the M6.0 South Napa earthquake are very challenging for EEW: the damage such events produce can be severe, but it is generally confined to relatively small zones around the epicenter and the shaking duration is short. This leaves a very short window for timely EEW alerts. Algorithms that wait for several stations to trigger before sending out EEW alerts are typically not fast enough for these kind of events because their blind zone (the zone where strong ground motions start before the warnings arrive) typically covers all or most of the area that experiences strong ground motions. At the same time, single station algorithms are often too unreliable to provide useful alerts. The filter bank EEW algorithm is a new algorithm that is designed to provide maximally accurate and precise earthquake parameter estimates with minimum data input, with the goal of producing reliable EEW alerts when only a very small number of stations have been reached by the p-wave. It combines the strengths of single station and network based algorithms in that it starts parameter estimates as soon as 0.5 seconds of data are available from the first station, but then perpetually incorporates additional data from the same or from any number of other stations. The algorithm analyzes the time dependent frequency content of real time waveforms with a filter bank. It then uses an extensive training data set to find earthquake records from the past that have had similar frequency content at a given time since the p-wave onset. The source parameters of the most similar events are used to parameterize a likelihood function for the source parameters of the ongoing event, which can then be maximized to find the most likely parameter estimates. Our preliminary results show that the filter bank EEW algorithm correctly estimated the magnitude of the South Napa earthquake to be ~M6 with only 1 second worth of data at the nearest station to the epicenter. This estimate is then confirmed when updates based on more data from stations at farther distances become available. Because these early estimates saturate at ~M6.5, however, the magnitude estimate might have had to be considered a minimum bound.

Spatiotemporal Local-Remote Senor Fusion (ST-LRSF) for Cooperative Vehicle Positioning

PubMed Central

Bhawiyuga, Adhitya

2018-01-01

Vehicle positioning plays an important role in the design of protocols, algorithms, and applications in the intelligent transport systems. In this paper, we present a new framework of spatiotemporal local-remote sensor fusion (ST-LRSF) that cooperatively improves the accuracy of absolute vehicle positioning based on two state estimates of a vehicle in the vicinity: a local sensing estimate, measured by the on-board exteroceptive sensors, and a remote sensing estimate, received from neighbor vehicles via vehicle-to-everything communications. Given both estimates of vehicle state, the ST-LRSF scheme identifies the set of vehicles in the vicinity, determines the reference vehicle state, proposes a spatiotemporal dissimilarity metric between two reference vehicle states, and presents a greedy algorithm to compute a minimal weighted matching (MWM) between them. Given the outcome of MWM, the theoretical position uncertainty of the proposed refinement algorithm is proven to be inversely proportional to the square root of matching size. To further reduce the positioning uncertainty, we also develop an extended Kalman filter model with the refined position of ST-LRSF as one of the measurement inputs. The numerical results demonstrate that the proposed ST-LRSF framework can achieve high positioning accuracy for many different scenarios of cooperative vehicle positioning. PMID:29617341

Development and evaluation of an empirical diurnal sea surface temperature model

NASA Astrophysics Data System (ADS)

Weihs, R. R.; Bourassa, M. A.

2013-12-01

An innovative method is developed to determine the diurnal heating amplitude of sea surface temperatures (SSTs) using observations of high-quality satellite SST measurements and NWP atmospheric meteorological data. The diurnal cycle results from heating that develops at the surface of the ocean from low mechanical or shear produced turbulence and large solar radiation absorption. During these typically calm weather conditions, the absorption of solar radiation causes heating of the upper few meters of the ocean, which become buoyantly stable; this heating causes a temperature differential between the surface and the mixed [or bulk] layer on the order of a few degrees. It has been shown that capturing the diurnal cycle is important for a variety of applications, including surface heat flux estimates, which have been shown to be underestimated when neglecting diurnal warming, and satellite and buoy calibrations, which can be complicated because of the heating differential. An empirical algorithm using a pre-dawn sea surface temperature, peak solar radiation, and accumulated wind stress is used to estimate the cycle. The empirical algorithm is derived from a multistep process in which SSTs from MTG's SEVIRI SST experimental hourly data set are combined with hourly wind stress fields derived from a bulk flux algorithm. Inputs for the flux model are taken from NASA's MERRA reanalysis product. NWP inputs are necessary because the inputs need to incorporate diurnal and air-sea interactive processes, which are vital to the ocean surface dynamics, with a high enough temporal resolution. The MERRA winds are adjusted with CCMP winds to obtain more realistic spatial and variance characteristics and the other atmospheric inputs (air temperature, specific humidity) are further corrected on the basis of in situ comparisons. The SSTs are fitted to a Gaussian curve (using one or two peaks), forming a set of coefficients used to fit the data. The coefficient data are combined with accumulated wind stress and peak solar radiation to create an empirical relationship that approximates physical processes such as turbulence and heating memory (capacity) of the ocean. Weaknesses and strengths of the model, including potential spatial biases, will be discussed.

Single image super-resolution reconstruction algorithm based on eage selection

NASA Astrophysics Data System (ADS)

Zhang, Yaolan; Liu, Yijun

2017-05-01

Super-resolution (SR) has become more important, because it can generate high-quality high-resolution (HR) images from low-resolution (LR) input images. At present, there are a lot of work is concentrated on developing sophisticated image priors to improve the image quality, while taking much less attention to estimating and incorporating the blur model that can also impact the reconstruction results. We present a new reconstruction method based on eager selection. This method takes full account of the factors that affect the blur kernel estimation and accurately estimating the blur process. When comparing with the state-of-the-art methods, our method has comparable performance.

Optimal Filter Estimation for Lucas-Kanade Optical Flow

PubMed Central

Sharmin, Nusrat; Brad, Remus

2012-01-01

Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm.

Support vector machines for nuclear reactor state estimation

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

Zavaljevski, N.; Gross, K. C.

2000-02-14

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

A novel ULA-based geometry for improving AOA estimation

NASA Astrophysics Data System (ADS)

Shirvani-Moghaddam, Shahriar; Akbari, Farida

2011-12-01

Due to relatively simple implementation, Uniform Linear Array (ULA) is a popular geometry for array signal processing. Despite this advantage, it does not have a uniform performance in all directions and Angle of Arrival (AOA) estimation performance degrades considerably in the angles close to endfire. In this article, a new configuration is proposed which can solve this problem. Proposed Array (PA) configuration adds two elements to the ULA in top and bottom of the array axis. By extending signal model of the ULA to the new proposed ULA-based array, AOA estimation performance has been compared in terms of angular accuracy and resolution threshold through two well-known AOA estimation algorithms, MUSIC and MVDR. In both algorithms, Root Mean Square Error (RMSE) of the detected angles descends as the input Signal to Noise Ratio (SNR) increases. Simulation results show that the proposed array geometry introduces uniform accurate performance and higher resolution in middle angles as well as border ones. The PA also presents less RMSE than the ULA in endfire directions. Therefore, the proposed array offers better performance for the border angles with almost the same array size and simplicity in both MUSIC and MVDR algorithms with respect to the conventional ULA. In addition, AOA estimation performance of the PA geometry is compared with two well-known 2D-array geometries: L-shape and V-shape, and acceptable results are obtained with equivalent or lower complexity.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

NASA Astrophysics Data System (ADS)

Zhang, Li

With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.

New multirate sampled-data control law structure and synthesis algorithm

NASA Technical Reports Server (NTRS)

Berg, Martin C.; Mason, Gregory S.; Yang, Gen-Sheng

1992-01-01

A new multirate sampled-data control law structure is defined and a new parameter-optimization-based synthesis algorithm for that structure is introduced. The synthesis algorithm can be applied to multirate, multiple-input/multiple-output, sampled-data control laws having a prescribed dynamic order and structure, and a priori specified sampling/update rates for all sensors, processor states, and control inputs. The synthesis algorithm is applied to design two-input, two-output tip position controllers of various dynamic orders for a sixth-order, two-link robot arm model.

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

Zhang Yanxia; Ma He; Peng Nanbo

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISEmore » sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.« less

Sequential reconstruction of driving-forces from nonlinear nonstationary dynamics

NASA Astrophysics Data System (ADS)

Güntürkün, Ulaş

2010-07-01

This paper describes a functional analysis-based method for the estimation of driving-forces from nonlinear dynamic systems. The driving-forces account for the perturbation inputs induced by the external environment or the secular variations in the internal variables of the system. The proposed algorithm is applicable to the problems for which there is too little or no prior knowledge to build a rigorous mathematical model of the unknown dynamics. We derive the estimator conditioned on the differentiability of the unknown system’s mapping, and smoothness of the driving-force. The proposed algorithm is an adaptive sequential realization of the blind prediction error method, where the basic idea is to predict the observables, and retrieve the driving-force from the prediction error. Our realization of this idea is embodied by predicting the observables one-step into the future using a bank of echo state networks (ESN) in an online fashion, and then extracting the raw estimates from the prediction error and smoothing these estimates in two adaptive filtering stages. The adaptive nature of the algorithm enables to retrieve both slowly and rapidly varying driving-forces accurately, which are illustrated by simulations. Logistic and Moran-Ricker maps are studied in controlled experiments, exemplifying chaotic state and stochastic measurement models. The algorithm is also applied to the estimation of a driving-force from another nonlinear dynamic system that is stochastic in both state and measurement equations. The results are judged by the posterior Cramer-Rao lower bounds. The method is finally put into test on a real-world application; extracting sun’s magnetic flux from the sunspot time series.

Algorithms for System Identification and Source Location.

NASA Astrophysics Data System (ADS)

Nehorai, Arye

This thesis deals with several topics in least squares estimation and applications to source location. It begins with a derivation of a mapping between Wiener theory and Kalman filtering for nonstationary autoregressive moving average (ARMO) processes. Applying time domain analysis, connections are found between time-varying state space realizations and input-output impulse response by matrix fraction description (MFD). Using these connections, the whitening filters are derived by the two approaches, and the Kalman gain is expressed in terms of Wiener theory. Next, fast estimation algorithms are derived in a unified way as special cases of the Conjugate Direction Method. The fast algorithms included are the block Levinson, fast recursive least squares, ladder (or lattice) and fast Cholesky algorithms. The results give a novel derivation and interpretation for all these methods, which are efficient alternatives to available recursive system identification algorithms. Multivariable identification algorithms are usually designed only for left MFD models. In this work, recursive multivariable identification algorithms are derived for right MFD models with diagonal denominator matrices. The algorithms are of prediction error and model reference type. Convergence analysis results obtained by the Ordinary Differential Equation (ODE) method are presented along with simulations. Sources of energy can be located by estimating time differences of arrival (TDOA's) of waves between the receivers. A new method for TDOA estimation is proposed for multiple unknown ARMA sources and additive correlated receiver noise. The method is based on a formula that uses only the receiver cross-spectra and the source poles. Two algorithms are suggested that allow tradeoffs between computational complexity and accuracy. A new time delay model is derived and used to show the applicability of the methods for non -integer TDOA's. Results from simulations illustrate the performance of the algorithms. The last chapter analyzes the response of exact least squares predictors for enhancement of sinusoids with additive colored noise. Using the matrix inversion lemma and the Christoffel-Darboux formula, the frequency response and amplitude gain of the sinusoids are expressed as functions of the signal and noise characteristics. The results generalize the available white noise case.

Analytic and rule-based decision support tool for VDT workstation adjustment and computer accessories arrangement.

PubMed

Rurkhamet, Busagarin; Nanthavanij, Suebsak

2004-12-01

One important factor that leads to the development of musculoskeletal disorders (MSD) and cumulative trauma disorders (CTD) among visual display terminal (VDT) users is their work posture. While operating a VDT, a user's body posture is strongly influenced by the task, VDT workstation settings, and layout of computer accessories. This paper presents an analytic and rule-based decision support tool called EQ-DeX (an ergonomics and quantitative design expert system) that is developed to provide valid and practical recommendations regarding the adjustment of a VDT workstation and the arrangement of computer accessories. The paper explains the structure and components of EQ-DeX, input data, rules, and adjustment and arrangement algorithms. From input information such as gender, age, body height, task, etc., EQ-DeX uses analytic and rule-based algorithms to estimate quantitative settings of a computer table and a chair, as well as locations of computer accessories such as monitor, document holder, keyboard, and mouse. With the input and output screens that are designed using the concept of usability, the interactions between the user and EQ-DeX are convenient. Examples are also presented to demonstrate the recommendations generated by EQ-DeX.

Space Object Maneuver Detection Algorithms Using TLE Data

NASA Astrophysics Data System (ADS)

Pittelkau, M.

2016-09-01

An important aspect of Space Situational Awareness (SSA) is detection of deliberate and accidental orbit changes of space objects. Although space surveillance systems detect orbit maneuvers within their tracking algorithms, maneuver data are not readily disseminated for general use. However, two-line element (TLE) data is available and can be used to detect maneuvers of space objects. This work is an attempt to improve upon existing TLE-based maneuver detection algorithms. Three adaptive maneuver detection algorithms are developed and evaluated: The first is a fading-memory Kalman filter, which is equivalent to the sliding-window least-squares polynomial fit, but computationally more efficient and adaptive to the noise in the TLE data. The second algorithm is based on a sample cumulative distribution function (CDF) computed from a histogram of the magnitude-squared |V|2 of change-in-velocity vectors (V), which is computed from the TLE data. A maneuver detection threshold is computed from the median estimated from the CDF, or from the CDF and a specified probability of false alarm. The third algorithm is a median filter. The median filter is the simplest of a class of nonlinear filters called order statistics filters, which is within the theory of robust statistics. The output of the median filter is practically insensitive to outliers, or large maneuvers. The median of the |V|2 data is proportional to the variance of the V, so the variance is estimated from the output of the median filter. A maneuver is detected when the input data exceeds a constant times the estimated variance.

Connecting Satellite-Based Precipitation Estimates to Users

NASA Technical Reports Server (NTRS)

Huffman, George J.; Bolvin, David T.; Nelkin, Eric

2018-01-01

Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

Creating an anthropomorphic digital MR phantom—an extensible tool for comparing and evaluating quantitative imaging algorithms

NASA Astrophysics Data System (ADS)

Bosca, Ryan J.; Jackson, Edward F.

2016-01-01

Assessing and mitigating the various sources of bias and variance associated with image quantification algorithms is essential to the use of such algorithms in clinical research and practice. Assessment is usually accomplished with grid-based digital reference objects (DRO) or, more recently, digital anthropomorphic phantoms based on normal human anatomy. Publicly available digital anthropomorphic phantoms can provide a basis for generating realistic model-based DROs that incorporate the heterogeneity commonly found in pathology. Using a publicly available vascular input function (VIF) and digital anthropomorphic phantom of a normal human brain, a methodology was developed to generate a DRO based on the general kinetic model (GKM) that represented realistic and heterogeneously enhancing pathology. GKM parameters were estimated from a deidentified clinical dynamic contrast-enhanced (DCE) MRI exam. This clinical imaging volume was co-registered with a discrete tissue model, and model parameters estimated from clinical images were used to synthesize a DCE-MRI exam that consisted of normal brain tissues and a heterogeneously enhancing brain tumor. An example application of spatial smoothing was used to illustrate potential applications in assessing quantitative imaging algorithms. A voxel-wise Bland-Altman analysis demonstrated negligible differences between the parameters estimated with and without spatial smoothing (using a small radius Gaussian kernel). In this work, we reported an extensible methodology for generating model-based anthropomorphic DROs containing normal and pathological tissue that can be used to assess quantitative imaging algorithms.

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

PubMed Central

Karatsidis, Angelos; Bellusci, Giovanni; Schepers, H. Martin; de Zee, Mark; Andersen, Michael S.; Veltink, Peter H.

2016-01-01

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory. PMID:28042857

Water Residence Time estimation by 1D deconvolution in the form of a l2 -regularized inverse problem with smoothness, positivity and causality constraints

NASA Astrophysics Data System (ADS)

Meresescu, Alina G.; Kowalski, Matthieu; Schmidt, Frédéric; Landais, François

2018-06-01

The Water Residence Time distribution is the equivalent of the impulse response of a linear system allowing the propagation of water through a medium, e.g. the propagation of rain water from the top of the mountain towards the aquifers. We consider the output aquifer levels as the convolution between the input rain levels and the Water Residence Time, starting with an initial aquifer base level. The estimation of Water Residence Time is important for a better understanding of hydro-bio-geochemical processes and mixing properties of wetlands used as filters in ecological applications, as well as protecting fresh water sources for wells from pollutants. Common methods of estimating the Water Residence Time focus on cross-correlation, parameter fitting and non-parametric deconvolution methods. Here we propose a 1D full-deconvolution, regularized, non-parametric inverse problem algorithm that enforces smoothness and uses constraints of causality and positivity to estimate the Water Residence Time curve. Compared to Bayesian non-parametric deconvolution approaches, it has a fast runtime per test case; compared to the popular and fast cross-correlation method, it produces a more precise Water Residence Time curve even in the case of noisy measurements. The algorithm needs only one regularization parameter to balance between smoothness of the Water Residence Time and accuracy of the reconstruction. We propose an approach on how to automatically find a suitable value of the regularization parameter from the input data only. Tests on real data illustrate the potential of this method to analyze hydrological datasets.

Deinterlacing using modular neural network

NASA Astrophysics Data System (ADS)

Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

2004-05-01

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

A Variable Step-Size Proportionate Affine Projection Algorithm for Identification of Sparse Impulse Response

NASA Astrophysics Data System (ADS)

Liu, Ligang; Fukumoto, Masahiro; Saiki, Sachio; Zhang, Shiyong

2009-12-01

Proportionate adaptive algorithms have been proposed recently to accelerate convergence for the identification of sparse impulse response. When the excitation signal is colored, especially the speech, the convergence performance of proportionate NLMS algorithms demonstrate slow convergence speed. The proportionate affine projection algorithm (PAPA) is expected to solve this problem by using more information in the input signals. However, its steady-state performance is limited by the constant step-size parameter. In this article we propose a variable step-size PAPA by canceling the a posteriori estimation error. This can result in high convergence speed using a large step size when the identification error is large, and can then considerably decrease the steady-state misalignment using a small step size after the adaptive filter has converged. Simulation results show that the proposed approach can greatly improve the steady-state misalignment without sacrificing the fast convergence of PAPA.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Part 3; The Map and Related Decoding Algirithms

NASA Technical Reports Server (NTRS)

Lin, Shu; Fossorier, Marc

1998-01-01

In a coded communication system with equiprobable signaling, MLD minimizes the word error probability and delivers the most likely codeword associated with the corresponding received sequence. This decoding has two drawbacks. First, minimization of the word error probability is not equivalent to minimization of the bit error probability. Therefore, MLD becomes suboptimum with respect to the bit error probability. Second, MLD delivers a hard-decision estimate of the received sequence, so that information is lost between the input and output of the ML decoder. This information is important in coded schemes where the decoded sequence is further processed, such as concatenated coding schemes, multi-stage and iterative decoding schemes. In this chapter, we first present a decoding algorithm which both minimizes bit error probability, and provides the corresponding soft information at the output of the decoder. This algorithm is referred to as the MAP (maximum aposteriori probability) decoding algorithm.

Reconstruction of the temperature field for inverse ultrasound hyperthermia calculations at a muscle/bone interface.

PubMed

Liauh, Chihng-Tsung; Shih, Tzu-Ching; Huang, Huang-Wen; Lin, Win-Li

2004-02-01

An inverse algorithm with Tikhonov regularization of order zero has been used to estimate the intensity ratios of the reflected longitudinal wave to the incident longitudinal wave and that of the refracted shear wave to the total transmitted wave into bone in calculating the absorbed power field and then to reconstruct the temperature distribution in muscle and bone regions based on a limited number of temperature measurements during simulated ultrasound hyperthermia. The effects of the number of temperature sensors are investigated, as is the amount of noise superimposed on the temperature measurements, and the effects of the optimal sensor location on the performance of the inverse algorithm. Results show that noisy input data degrades the performance of this inverse algorithm, especially when the number of temperature sensors is small. Results are also presented demonstrating an improvement in the accuracy of the temperature estimates by employing an optimal value of the regularization parameter. Based on the analysis of singular-value decomposition, the optimal sensor position in a case utilizing only one temperature sensor can be determined to make the inverse algorithm converge to the true solution.

A simple algorithm for identifying periods of snow accumulation on a radiometer

NASA Astrophysics Data System (ADS)

Lapo, Karl E.; Hinkelman, Laura M.; Landry, Christopher C.; Massmann, Adam K.; Lundquist, Jessica D.

2015-09-01

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70-80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

Fully probabilistic control for stochastic nonlinear control systems with input dependent noise.

PubMed

Herzallah, Randa

2015-03-01

Robust controllers for nonlinear stochastic systems with functional uncertainties can be consistently designed using probabilistic control methods. In this paper a generalised probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented emphasising how the uncertainty can be systematically incorporated in the absence of reliable systems models. To achieve this objective all probabilistic models of the system are estimated from process data using mixture density networks (MDNs) where all the parameters of the estimated pdfs are taken to be state and control input dependent. Based on this dependency of the density parameters on the input values, explicit formulations to the construction of optimal generalised probabilistic controllers are obtained through the techniques of dynamic programming and adaptive critic methods. Using the proposed generalised probabilistic controller, the conditional joint pdfs can be made to follow the ideal ones. A simulation example is used to demonstrate the implementation of the algorithm and encouraging results are obtained. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of observation-driven evaporation algorithms: results of the WACMOS-ET project

NASA Astrophysics Data System (ADS)

Miralles, Diego G.; Jimenez, Carlos; Ershadi, Ali; McCabe, Matthew F.; Michel, Dominik; Hirschi, Martin; Seneviratne, Sonia I.; Jung, Martin; Wood, Eric F.; (Bob) Su, Z.; Timmermans, Joris; Chen, Xuelong; Fisher, Joshua B.; Mu, Quiaozen; Fernandez, Diego

2015-04-01

Terrestrial evaporation (ET) links the continental water, energy and carbon cycles. Understanding the magnitude and variability of ET at the global scale is an essential step towards reducing uncertainties in our projections of climatic conditions and water availability for the future. However, the requirement of global observational data of ET can neither be satisfied with our sparse global in-situ networks, nor with the existing satellite sensors (which cannot measure evaporation directly from space). This situation has led to the recent rise of several algorithms dedicated to deriving ET fields from satellite data indirectly, based on the combination of ET-drivers that can be observed from space (e.g. radiation, temperature, phenological variability, water content, etc.). These algorithms can either be based on physics (e.g. Priestley and Taylor or Penman-Monteith approaches) or be purely statistical (e.g., machine learning). However, and despite the efforts from different initiatives like GEWEX LandFlux (Jimenez et al., 2011; Mueller et al., 2013), the uncertainties inherent in the resulting global ET datasets remain largely unexplored, partly due to a lack of inter-product consistency in forcing data. In response to this need, the ESA WACMOS-ET project started in 2012 with the main objectives of (a) developing a Reference Input Data Set to derive and validate ET estimates, and (b) performing a cross-comparison, error characterization and validation exercise of a group of selected ET algorithms driven by this Reference Input Data Set and by in-situ forcing data. The algorithms tested are SEBS (Su et al., 2002), the Penman- Monteith approach from MODIS (Mu et al., 2011), the Priestley and Taylor JPL model (Fisher et al., 2008), the MPI-MTE model (Jung et al., 2010) and GLEAM (Miralles et al., 2011). In this presentation we will show the first results from the ESA WACMOS-ET project. The performance of the different algorithms at multiple spatial and temporal scales for the 2005-2007 reference period will be disclosed. The skill of these algorithms to close the water balance over the continents will be assessed by comparisons to runoff data. The consistency in forcing data will allow to (a) evaluate the skill of these five algorithms in producing ET over particular ecosystems, (b) facilitate the attribution of the observed differences to either algorithms or driving data, and (c) set up a solid scientific basis for the development of global long-term benchmark ET products. Project progress can be followed on our website http://wacmoset.estellus.eu. REFERENCES Fisher, J. B., Tu, K.P., and Baldocchi, D.D. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ. 112, 901-919, 2008. Jiménez, C. et al. Global intercomparison of 12 land surface heat flux estimates. J. Geophys. Res. 116, D02102, 2011. Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951-954, 2010. Miralles, D.G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453-469, 2011. Mu, Q., Zhao, M. & Running, S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781-1800, 2011. Mueller, B. et al. Benchmark products for land evapotranspiration: LandFlux-EVAL multi- dataset synthesis. Hydrol. Earth Syst. Sci. 17, 3707-3720, 2013. Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci. 6, 85-99, 2002.

Improved quantum backtracking algorithms using effective resistance estimates

NASA Astrophysics Data System (ADS)

Jarret, Michael; Wan, Kianna

2018-02-01

We investigate quantum backtracking algorithms of the type introduced by Montanaro (Montanaro, arXiv:1509.02374). These algorithms explore trees of unknown structure and in certain settings exponentially outperform their classical counterparts. Some of the previous work focused on obtaining a quantum advantage for trees in which a unique marked vertex is promised to exist. We remove this restriction by recharacterizing the problem in terms of the effective resistance of the search space. In this paper, we present a generalization of one of Montanaro's algorithms to trees containing k marked vertices, where k is not necessarily known a priori. Our approach involves using amplitude estimation to determine a near-optimal weighting of a diffusion operator, which can then be applied to prepare a superposition state with support only on marked vertices and ancestors thereof. By repeatedly sampling this state and updating the input vertex, a marked vertex is reached in a logarithmic number of steps. The algorithm thereby achieves the conjectured bound of O ˜(√{T Rmax }) for finding a single marked vertex and O ˜(k √{T Rmax }) for finding all k marked vertices, where T is an upper bound on the tree size and Rmax is the maximum effective resistance encountered by the algorithm. This constitutes a speedup over Montanaro's original procedure in both the case of finding one and the case of finding multiple marked vertices in an arbitrary tree.

Fast template matching with polynomials.

PubMed

Omachi, Shinichiro; Omachi, Masako

2007-08-01

Template matching is widely used for many applications in image and signal processing. This paper proposes a novel template matching algorithm, called algebraic template matching. Given a template and an input image, algebraic template matching efficiently calculates similarities between the template and the partial images of the input image, for various widths and heights. The partial image most similar to the template image is detected from the input image for any location, width, and height. In the proposed algorithm, a polynomial that approximates the template image is used to match the input image instead of the template image. The proposed algorithm is effective especially when the width and height of the template image differ from the partial image to be matched. An algorithm using the Legendre polynomial is proposed for efficient approximation of the template image. This algorithm not only reduces computational costs, but also improves the quality of the approximated image. It is shown theoretically and experimentally that the computational cost of the proposed algorithm is much smaller than the existing methods.

Deadbeat Predictive Controllers

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Phan, Minh

1997-01-01

Several new computational algorithms are presented to compute the deadbeat predictive control law. The first algorithm makes use of a multi-step-ahead output prediction to compute the control law without explicitly calculating the controllability matrix. The system identification must be performed first and then the predictive control law is designed. The second algorithm uses the input and output data directly to compute the feedback law. It combines the system identification and the predictive control law into one formulation. The third algorithm uses an observable-canonical form realization to design the predictive controller. The relationship between all three algorithms is established through the use of the state-space representation. All algorithms are applicable to multi-input, multi-output systems with disturbance inputs. In addition to the feedback terms, feed forward terms may also be added for disturbance inputs if they are measurable. Although the feedforward terms do not influence the stability of the closed-loop feedback law, they enhance the performance of the controlled system.

A novel angle computation and calibration algorithm of bio-inspired sky-light polarization navigation sensor.

PubMed

Xian, Zhiwen; Hu, Xiaoping; Lian, Junxiang; Zhang, Lilian; Cao, Juliang; Wang, Yujie; Ma, Tao

2014-09-15

Navigation plays a vital role in our daily life. As traditional and commonly used navigation technologies, Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) can provide accurate location information, but suffer from the accumulative error of inertial sensors and cannot be used in a satellite denied environment. The remarkable navigation ability of animals shows that the pattern of the polarization sky can be used for navigation. A bio-inspired POLarization Navigation Sensor (POLNS) is constructed to detect the polarization of skylight. Contrary to the previous approach, we utilize all the outputs of POLNS to compute input polarization angle, based on Least Squares, which provides optimal angle estimation. In addition, a new sensor calibration algorithm is presented, in which the installation angle errors and sensor biases are taken into consideration. Derivation and implementation of our calibration algorithm are discussed in detail. To evaluate the performance of our algorithms, simulation and real data test are done to compare our algorithms with several exiting algorithms. Comparison results indicate that our algorithms are superior to the others and are more feasible and effective in practice.

Fast Dating Using Least-Squares Criteria and Algorithms.

PubMed

To, Thu-Hien; Jung, Matthieu; Lycett, Samantha; Gascuel, Olivier

2016-01-01

Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodeficiency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here, we present very fast dating algorithms, based on a Gaussian model closely related to the Langley-Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least-squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal precedence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley-Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Fast Dating Using Least-Squares Criteria and Algorithms

PubMed Central

To, Thu-Hien; Jung, Matthieu; Lycett, Samantha; Gascuel, Olivier

2016-01-01

Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodeficiency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here, we present very fast dating algorithms, based on a Gaussian model closely related to the Langley–Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least-squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal precedence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley–Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3. PMID:26424727

Dual ant colony operational modal analysis parameter estimation method

NASA Astrophysics Data System (ADS)

Sitarz, Piotr; Powałka, Bartosz

2018-01-01

Operational Modal Analysis (OMA) is a common technique used to examine the dynamic properties of a system. Contrary to experimental modal analysis, the input signal is generated in object ambient environment. Operational modal analysis mainly aims at determining the number of pole pairs and at estimating modal parameters. Many methods are used for parameter identification. Some methods operate in time while others in frequency domain. The former use correlation functions, the latter - spectral density functions. However, while some methods require the user to select poles from a stabilisation diagram, others try to automate the selection process. Dual ant colony operational modal analysis parameter estimation method (DAC-OMA) presents a new approach to the problem, avoiding issues involved in the stabilisation diagram. The presented algorithm is fully automated. It uses deterministic methods to define the interval of estimated parameters, thus reducing the problem to optimisation task which is conducted with dedicated software based on ant colony optimisation algorithm. The combination of deterministic methods restricting parameter intervals and artificial intelligence yields very good results, also for closely spaced modes and significantly varied mode shapes within one measurement point.

AutoBayes Program Synthesis System Users Manual

NASA Technical Reports Server (NTRS)

Schumann, Johann; Jafari, Hamed; Pressburger, Tom; Denney, Ewen; Buntine, Wray; Fischer, Bernd

2008-01-01

Program synthesis is the systematic, automatic construction of efficient executable code from high-level declarative specifications. AutoBayes is a fully automatic program synthesis system for the statistical data analysis domain; in particular, it solves parameter estimation problems. It has seen many successful applications at NASA and is currently being used, for example, to analyze simulation results for Orion. The input to AutoBayes is a concise description of a data analysis problem composed of a parameterized statistical model and a goal that is a probability term involving parameters and input data. The output is optimized and fully documented C/C++ code computing the values for those parameters that maximize the probability term. AutoBayes can solve many subproblems symbolically rather than having to rely on numeric approximation algorithms, thus yielding effective, efficient, and compact code. Statistical analysis is faster and more reliable, because effort can be focused on model development and validation rather than manual development of solution algorithms and code.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

On-line, adaptive state estimator for active noise control

NASA Technical Reports Server (NTRS)

Lim, Tae W.

1994-01-01

Dynamic characteristics of airframe structures are expected to vary as aircraft flight conditions change. Accurate knowledge of the changing dynamic characteristics is crucial to enhancing the performance of the active noise control system using feedback control. This research investigates the development of an adaptive, on-line state estimator using a neural network concept to conduct active noise control. In this research, an algorithm has been developed that can be used to estimate displacement and velocity responses at any locations on the structure from a limited number of acceleration measurements and input force information. The algorithm employs band-pass filters to extract from the measurement signal the frequency contents corresponding to a desired mode. The filtered signal is then used to train a neural network which consists of a linear neuron with three weights. The structure of the neural network is designed as simple as possible to increase the sampling frequency as much as possible. The weights obtained through neural network training are then used to construct the transfer function of a mode in z-domain and to identify modal properties of each mode. By using the identified transfer function and interpolating the mode shape obtained at sensor locations, the displacement and velocity responses are estimated with reasonable accuracy at any locations on the structure. The accuracy of the response estimates depends on the number of modes incorporated in the estimates and the number of sensors employed to conduct mode shape interpolation. Computer simulation demonstrates that the algorithm is capable of adapting to the varying dynamic characteristics of structural properties. Experimental implementation of the algorithm on a DSP (digital signal processing) board for a plate structure is underway. The algorithm is expected to reach the sampling frequency range of about 10 kHz to 20 kHz which needs to be maintained for a typical active noise control application.

Demonstration of UXO-PenDepth for the Estimation of Projectile Penetration Depth

DTIC Science & Technology

2010-08-01

Effects (JTCG/ME) in August 2001. The accreditation process included verification and validation (V&V) by a subject matter expert (SME) other than...Within UXO-PenDepth, there are three sets of input parameters that are required: impact conditions (Fig. 1a), penetrator properties , and target... properties . The impact conditions that need to be defined are projectile orientation and impact velocity. The algorithm has been evaluated against

Vision-Aided Autonomous Landing and Ingress of Micro Aerial Vehicles

NASA Technical Reports Server (NTRS)

Brockers, Roland; Ma, Jeremy C.; Matthies, Larry H.; Bouffard, Patrick

2012-01-01

Micro aerial vehicles have limited sensor suites and computational power. For reconnaissance tasks and to conserve energy, these systems need the ability to autonomously land at vantage points or enter buildings (ingress). But for autonomous navigation, information is needed to identify and guide the vehicle to the target. Vision algorithms can provide egomotion estimation and target detection using input from cameras that are easy to include in miniature systems.

Advanced Physiological Estimation of Cognitive Status

DTIC Science & Technology

2011-05-24

Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS fatigue mental workload cognitive status EEG machine learning algorithms Leonard J. Trejo...Information Transfer (NOIT)” • ARO Proposal No. 56469-LS • Three-year basic research with UCLA team 2. “ EEG -guided Input Lateralization and Hemispheric...Activation with Neurofeedback for Display Data Control and Apprehension.” • ARO Proposal No. 59502-LS • One-year Infrastructure technology transfer to

Automatic Boosted Flood Mapping from Satellite Data

NASA Technical Reports Server (NTRS)

Coltin, Brian; McMichael, Scott; Smith, Trey; Fong, Terrence

2016-01-01

Numerous algorithms have been proposed to map floods from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, most require human input to succeed, either to specify a threshold value or to manually annotate training data. We introduce a new algorithm based on Adaboost which effectively maps floods without any human input, allowing for a truly rapid and automatic response. The Adaboost algorithm combines multiple thresholds to achieve results comparable to state-of-the-art algorithms which do require human input. We evaluate Adaboost, as well as numerous previously proposed flood mapping algorithms, on multiple MODIS flood images, as well as on hundreds of non-flood MODIS lake images, demonstrating its effectiveness across a wide variety of conditions.

Encryption and decryption using FPGA

NASA Astrophysics Data System (ADS)

Nayak, Nikhilesh; Chandak, Akshay; Shah, Nisarg; Karthikeyan, B.

2017-11-01

In this paper, we are performing multiple cryptography methods on a set of data and comparing their outputs. Here AES algorithm and RSA algorithm are used. Using AES Algorithm an 8 bit input (plain text) gets encrypted using a cipher key and the result is displayed on tera term (serially). For simulation a 128 bit input is used and operated with a 128 bit cipher key to generate encrypted text. The reverse operations are then performed to get decrypted text. In RSA Algorithm file handling is used to input plain text. This text is then operated on to get the encrypted and decrypted data, which are then stored in a file. Finally the results of both the algorithms are compared.

A new retrieval algorithm for tropospheric temperature, humidity and pressure profiling based on GNSS radio occultation data

NASA Astrophysics Data System (ADS)

Kirchengast, Gottfried; Li, Ying; Scherllin-Pirscher, Barbara; Schwärz, Marc; Schwarz, Jakob; Nielsen, Johannes K.

2017-04-01

The GNSS radio occultation (RO) technique is an important remote sensing technique for obtaining thermodynamic profiles of temperature, humidity, and pressure in the Earth's troposphere. However, due to refraction effects of both dry ambient air and water vapor in the troposphere, retrieval of accurate thermodynamic profiles at these lower altitudes is challenging and requires suitable background information in addition to the RO refractivity information. Here we introduce a new moist air retrieval algorithm aiming to improve the quality and robustness of retrieving temperature, humidity and pressure profiles in moist air tropospheric conditions. The new algorithm consists of four steps: (1) use of prescribed specific humidity and its uncertainty to retrieve temperature and its associated uncertainty; (2) use of prescribed temperature and its uncertainty to retrieve specific humidity and its associated uncertainty; (3) use of the previous results to estimate final temperature and specific humidity profiles through optimal estimation; (4) determination of air pressure and density profiles from the results obtained before. The new algorithm does not require elaborated matrix inversions which are otherwise widely used in 1D-Var retrieval algorithms, and it allows a transparent uncertainty propagation, whereby the uncertainties of prescribed variables are dynamically estimated accounting for their spatial and temporal variations. Estimated random uncertainties are calculated by constructing error covariance matrices from co-located ECMWF short-range forecast and corresponding analysis profiles. Systematic uncertainties are estimated by empirical modeling. The influence of regarding or disregarding vertical error correlations is quantified. The new scheme is implemented with static input uncertainty profiles in WEGC's current OPSv5.6 processing system and with full scope in WEGC's next-generation system, the Reference Occultation Processing System (rOPS). Results from both WEGC systems, current OPSv5.6 and next-generation rOPS, are shown and discussed, based on both insights from individual profiles and statistical ensembles, and compared to moist air retrieval results from the UCAR Boulder and ROM-SAF Copenhagen centers. The results show that the new algorithmic scheme improves the temperature, humidity and pressure retrieval performance, in particular also the robustness including for integrated uncertainty estimation for large-scale applications, over the previous algorithms. The new rOPS-implemented algorithm will therefore be used in the first large-scale reprocessing towards a tropospheric climate data record 2001-2016 by the rOPS, including its integrated uncertainty propagation.

Global rotational motion and displacement estimation of digital image stabilization based on the oblique vectors matching algorithm

NASA Astrophysics Data System (ADS)

Yu, Fei; Hui, Mei; Zhao, Yue-jin

2009-08-01

The image block matching algorithm based on motion vectors of correlative pixels in oblique direction is presented for digital image stabilization. The digital image stabilization is a new generation of image stabilization technique which can obtains the information of relative motion among frames of dynamic image sequences by the method of digital image processing. In this method the matching parameters are calculated from the vectors projected in the oblique direction. The matching parameters based on the vectors contain the information of vectors in transverse and vertical direction in the image blocks at the same time. So the better matching information can be obtained after making correlative operation in the oblique direction. And an iterative weighted least square method is used to eliminate the error of block matching. The weights are related with the pixels' rotational angle. The center of rotation and the global emotion estimation of the shaking image can be obtained by the weighted least square from the estimation of each block chosen evenly from the image. Then, the shaking image can be stabilized with the center of rotation and the global emotion estimation. Also, the algorithm can run at real time by the method of simulated annealing in searching method of block matching. An image processing system based on DSP was used to exam this algorithm. The core processor in the DSP system is TMS320C6416 of TI, and the CCD camera with definition of 720×576 pixels was chosen as the input video signal. Experimental results show that the algorithm can be performed at the real time processing system and have an accurate matching precision.

Robust transceiver design for reciprocal M × N interference channel based on statistical linearization approximation

NASA Astrophysics Data System (ADS)

Mayvan, Ali D.; Aghaeinia, Hassan; Kazemi, Mohammad

2017-12-01

This paper focuses on robust transceiver design for throughput enhancement on the interference channel (IC), under imperfect channel state information (CSI). In this paper, two algorithms are proposed to improve the throughput of the multi-input multi-output (MIMO) IC. Each transmitter and receiver has, respectively, M and N antennas and IC operates in a time division duplex mode. In the first proposed algorithm, each transceiver adjusts its filter to maximize the expected value of signal-to-interference-plus-noise ratio (SINR). On the other hand, the second algorithm tries to minimize the variances of the SINRs to hedge against the variability due to CSI error. Taylor expansion is exploited to approximate the effect of CSI imperfection on mean and variance. The proposed robust algorithms utilize the reciprocity of wireless networks to optimize the estimated statistical properties in two different working modes. Monte Carlo simulations are employed to investigate sum rate performance of the proposed algorithms and the advantage of incorporating variation minimization into the transceiver design.

User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

1988-01-01

Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

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

PubMed

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

2017-03-01

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

Estimation of effective wind speed

NASA Astrophysics Data System (ADS)

Østergaard, K. Z.; Brath, P.; Stoustrup, J.

2007-07-01

The wind speed has a huge impact on the dynamic response of wind turbine. Because of this, many control algorithms use a measure of the wind speed to increase performance, e.g. by gain scheduling and feed forward. Unfortunately, no accurate measurement of the effective wind speed is online available from direct measurements, which means that it must be estimated in order to make such control methods applicable in practice. In this paper a new method is presented for the estimation of the effective wind speed. First, the rotor speed and aerodynamic torque are estimated by a combined state and input observer. These two variables combined with the measured pitch angle is then used to calculate the effective wind speed by an inversion of a static aerodynamic model.

A Bayesian approach to model structural error and input variability in groundwater modeling

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Lin, Y. F. F.; Liang, F.

2015-12-01

Effective water resource management typically relies on numerical models to analyze groundwater flow and solute transport processes. Model structural error (due to simplification and/or misrepresentation of the "true" environmental system) and input forcing variability (which commonly arises since some inputs are uncontrolled or estimated with high uncertainty) are ubiquitous in groundwater models. Calibration that overlooks errors in model structure and input data can lead to biased parameter estimates and compromised predictions. We present a fully Bayesian approach for a complete assessment of uncertainty for spatially distributed groundwater models. The approach explicitly recognizes stochastic input and uses data-driven error models based on nonparametric kernel methods to account for model structural error. We employ exploratory data analysis to assist in specifying informative prior for error models to improve identifiability. The inference is facilitated by an efficient sampling algorithm based on DREAM-ZS and a parameter subspace multiple-try strategy to reduce the required number of forward simulations of the groundwater model. We demonstrate the Bayesian approach through a synthetic case study of surface-ground water interaction under changing pumping conditions. It is found that explicit treatment of errors in model structure and input data (groundwater pumping rate) has substantial impact on the posterior distribution of groundwater model parameters. Using error models reduces predictive bias caused by parameter compensation. In addition, input variability increases parametric and predictive uncertainty. The Bayesian approach allows for a comparison among the contributions from various error sources, which could inform future model improvement and data collection efforts on how to best direct resources towards reducing predictive uncertainty.

The method for homography estimation between two planes based on lines and points

NASA Astrophysics Data System (ADS)

Shemiakina, Julia; Zhukovsky, Alexander; Nikolaev, Dmitry

2018-04-01

The paper considers the problem of estimating a transform connecting two images of one plane object. The method based on RANSAC is proposed for calculating the parameters of projective transform which uses points and lines correspondences simultaneously. A series of experiments was performed on synthesized data. Presented results show that the algorithm convergence rate is significantly higher when actual lines are used instead of points of lines intersection. When using both lines and feature points it is shown that the convergence rate does not depend on the ratio between lines and feature points in the input dataset.

Method and apparatus for digitally based high speed x-ray spectrometer

DOEpatents

Warburton, W.K.; Hubbard, B.

1997-11-04

A high speed, digitally based, signal processing system which accepts input data from a detector-preamplifier and produces a spectral analysis of the x-rays illuminating the detector. The system achieves high throughputs at low cost by dividing the required digital processing steps between a ``hardwired`` processor implemented in combinatorial digital logic, which detects the presence of the x-ray signals in the digitized data stream and extracts filtered estimates of their amplitudes, and a programmable digital signal processing computer, which refines the filtered amplitude estimates and bins them to produce the desired spectral analysis. One set of algorithms allow this hybrid system to match the resolution of analog systems while operating at much higher data rates. A second set of algorithms implemented in the processor allow the system to be self calibrating as well. The same processor also handles the interface to an external control computer. 19 figs.

Method and apparatus for digitally based high speed x-ray spectrometer

DOEpatents

Warburton, William K.; Hubbard, Bradley

1997-01-01

A high speed, digitally based, signal processing system which accepts input data from a detector-preamplifier and produces a spectral analysis of the x-rays illuminating the detector. The system achieves high throughputs at low cost by dividing the required digital processing steps between a "hardwired" processor implemented in combinatorial digital logic, which detects the presence of the x-ray signals in the digitized data stream and extracts filtered estimates of their amplitudes, and a programmable digital signal processing computer, which refines the filtered amplitude estimates and bins them to produce the desired spectral analysis. One set of algorithms allow this hybrid system to match the resolution of analog systems while operating at much higher data rates. A second set of algorithms implemented in the processor allow the system to be self calibrating as well. The same processor also handles the interface to an external control computer.

A Trajectory Algorithm to Support En Route and Terminal Area Self-Spacing Concepts

NASA Technical Reports Server (NTRS)

Abbott, Terence S.

2007-01-01

This document describes an algorithm for the generation of a four dimensional aircraft trajectory. Input data for this algorithm are similar to an augmented Standard Terminal Arrival Route (STAR) with the augmentation in the form of altitude or speed crossing restrictions at waypoints on the route. Wind data at each waypoint are also inputs into this algorithm. The algorithm calculates the altitude, speed, along path distance, and along path time for each waypoint.

Deconvolution of noisy transient signals: a Kalman filtering application

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

Candy, J.V.; Zicker, J.E.

The deconvolution of transient signals from noisy measurements is a common problem occuring in various tests at Lawrence Livermore National Laboratory. The transient deconvolution problem places atypical constraints on algorithms presently available. The Schmidt-Kalman filter, a time-varying, tunable predictor, is designed using a piecewise constant model of the transient input signal. A simulation is developed to test the algorithm for various input signal bandwidths and different signal-to-noise ratios for the input and output sequences. The algorithm performance is reasonable.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

PubMed

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Frequency domain surface EMG sensor fusion for estimating finger forces.

PubMed

Potluri, Chandrasekhar; Kumar, Parmod; Anugolu, Madhavi; Urfer, Alex; Chiu, Steve; Naidu, D; Schoen, Marco P

2010-01-01

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

NASA Astrophysics Data System (ADS)

Habarulema, J. B.; McKinnell, L.-A.

2012-05-01

In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000-2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research.

Temporal rainfall estimation using input data reduction and model inversion

NASA Astrophysics Data System (ADS)

Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

2016-12-01

Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a demonstration of equifinality. The use of a likelihood function that considers both rainfall and streamflow error combined with the use of the DWT as a model data reduction technique allows the joint inference of hydrologic model parameters along with rainfall.

Realization of daily evapotranspiration in arid ecosystems based on remote sensing techniques

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Bahrawi, Jarbou A.

2017-03-01

Daily evapotranspiration is a major component of water resources management plans. In arid ecosystems, the quest for an efficient water budget is always hard to achieve due to insufficient irrigational water and high evapotranspiration rates. Therefore, monitoring of daily evapotranspiration is a key practice for sustainable water resources management, especially in arid environments. Remote sensing techniques offered a great help to estimate the daily evapotranspiration on a regional scale. Existing open-source algorithms proved to estimate daily evapotranspiration comprehensively in arid environments. The only deficiency of these algorithms is the course scale of the used remote sensing data. Consequently, the adequate downscaling algorithm is a compulsory step to rationalize an effective water resources management plan. Daily evapotranspiration was estimated fairly well using an Advance Along-Track Scanner Radiometer (AATSR) in conjunction with (MEdium Resolution Imaging Spectrometer) MERIS data acquired in July 2013 with 1 km spatial resolution and 3 days of temporal resolution under a surface energy balance system (SEBS) model. Results were validated against reference evapotranspiration ground truth values using standardized Penman-Monteith method with R2 of 0.879. The findings of the current research successfully monitor turbulent heat fluxes values estimated from AATSR and MERIS data with a temporal resolution of 3 days only in conjunction with reliable meteorological data. Research verdicts are necessary inputs for a well-informed decision-making processes regarding sustainable water resource management.

Breast cancer detection using time reversal

NASA Astrophysics Data System (ADS)

Sheikh Sajjadieh, Mohammad Hossein

Breast cancer is the second leading cause of cancer death after lung cancer among women. Mammography and magnetic resonance imaging (MRI) have certain limitations in detecting breast cancer, especially during its early stage of development. A number of studies have shown that microwave breast cancer detection has potential to become a successful clinical complement to the conventional X-ray mammography. Microwave breast imaging is performed by illuminating the breast tissues with an electromagnetic waveform and recording its reflections (backscatters) emanating from variations in the normal breast tissues and tumour cells, if present, using an antenna array. These backscatters, referred to as the overall (tumour and clutter) response, are processed to estimate the tumour response, which is applied as input to array imaging algorithms used to estimate the location of the tumour. Due to changes in the breast profile over time, the commonly utilized background subtraction procedures used to estimate the target (tumour) response in array processing are impractical for breast cancer detection. The thesis proposes a new tumour estimation algorithm based on a combination of the data adaptive filter with the envelope detection filter (DAF/EDF), which collectively do not require a training step. After establishing the superiority of the DAF/EDF based approach, the thesis shows that the time reversal (TR) array imaging algorithms outperform their conventional conterparts in detecting and localizing tumour cells in breast tissues at SNRs ranging from 15 to 30dB.

Automatic image equalization and contrast enhancement using Gaussian mixture modeling.

PubMed

Celik, Turgay; Tjahjadi, Tardi

2012-01-01

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

A Revised Trajectory Algorithm to Support En Route and Terminal Area Self-Spacing Concepts

NASA Technical Reports Server (NTRS)

Abbott, Terence S.

2010-01-01

This document describes an algorithm for the generation of a four dimensional trajectory. Input data for this algorithm are similar to an augmented Standard Terminal Arrival (STAR) with the augmentation in the form of altitude or speed crossing restrictions at waypoints on the route. This version of the algorithm accommodates descent Mach values that are different from the cruise Mach values. Wind data at each waypoint are also inputs into this algorithm. The algorithm calculates the altitude, speed, along path distance, and along path time for each waypoint.

An improved optimization algorithm of the three-compartment model with spillover and partial volume corrections for dynamic FDG PET images of small animal hearts in vivo

NASA Astrophysics Data System (ADS)

Li, Yinlin; Kundu, Bijoy K.

2018-03-01

The three-compartment model with spillover (SP) and partial volume (PV) corrections has been widely used for noninvasive kinetic parameter studies of dynamic 2-[18F] fluoro-2deoxy-D-glucose (FDG) positron emission tomography images of small animal hearts in vivo. However, the approach still suffers from estimation uncertainty or slow convergence caused by the commonly used optimization algorithms. The aim of this study was to develop an improved optimization algorithm with better estimation performance. Femoral artery blood samples, image-derived input functions from heart ventricles and myocardial time-activity curves (TACs) were derived from data on 16 C57BL/6 mice obtained from the UCLA Mouse Quantitation Program. Parametric equations of the average myocardium and the blood pool TACs with SP and PV corrections in a three-compartment tracer kinetic model were formulated. A hybrid method integrating artificial immune-system and interior-reflective Newton methods were developed to solve the equations. Two penalty functions and one late time-point tail vein blood sample were used to constrain the objective function. The estimation accuracy of the method was validated by comparing results with experimental values using the errors in the areas under curves (AUCs) of the model corrected input function (MCIF) and the 18F-FDG influx constant K i . Moreover, the elapsed time was used to measure the convergence speed. The overall AUC error of MCIF for the 16 mice averaged  -1.4  ±  8.2%, with correlation coefficients of 0.9706. Similar results can be seen in the overall K i error percentage, which was 0.4  ±  5.8% with a correlation coefficient of 0.9912. The t-test P value for both showed no significant difference. The mean and standard deviation of the MCIF AUC and K i percentage errors have lower values compared to the previously published methods. The computation time of the hybrid method is also several times lower than using just a stochastic algorithm. The proposed method significantly improved the model estimation performance in terms of the accuracy of the MCIF and K i , as well as the convergence speed.

Retrieving the Height of Smoke and Dust Aerosols by Synergistic Use of Multiple Satellite Sensors

NASA Technical Reports Server (NTRS)

Lee, Jaehwa; Hsu, N. Christina; Bettenhausen, Corey; Sayer, Andrew M.; Seftor, Colin J.; Jeong, Myeong-Jae

2016-01-01

The Aerosol Single scattering albedo and Height Estimation (ASHE) algorithm was first introduced in Jeong and Hsu (2008) to provide aerosol layer height and single scattering albedo (SSA) for biomass burning smoke aerosols. By using multiple satellite sensors synergistically, ASHE can provide the height information over much broader areas than lidar observations alone. The complete ASHE algorithm uses aerosol data from MODIS or VIIRS, OMI or OMPS, and CALIOP. A simplified algorithm also exists that does not require CALIOP data as long as the SSA of the aerosol layer is provided by another source. Several updates have recently been made: inclusion of dust layers in the retrieval process, better determination of the input aerosol layer height from CALIOP, improvement in aerosol optical depth (AOD) for nonspherical dust, development of quality assurance (QA) procedure, etc.

Signal Prediction With Input Identification

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan; Chen, Ya-Chin

1999-01-01

A novel coding technique is presented for signal prediction with applications including speech coding, system identification, and estimation of input excitation. The approach is based on the blind equalization method for speech signal processing in conjunction with the geometric subspace projection theory to formulate the basic prediction equation. The speech-coding problem is often divided into two parts, a linear prediction model and excitation input. The parameter coefficients of the linear predictor and the input excitation are solved simultaneously and recursively by a conventional recursive least-squares algorithm. The excitation input is computed by coding all possible outcomes into a binary codebook. The coefficients of the linear predictor and excitation, and the index of the codebook can then be used to represent the signal. In addition, a variable-frame concept is proposed to block the same excitation signal in sequence in order to reduce the storage size and increase the transmission rate. The results of this work can be easily extended to the problem of disturbance identification. The basic principles are outlined in this report and differences from other existing methods are discussed. Simulations are included to demonstrate the proposed method.

Computing and analyzing the sensitivity of MLP due to the errors of the i.i.d. inputs and weights based on CLT.

PubMed

Yang, Sheng-Sung; Ho, Chia-Lu; Siu, Sammy

2010-12-01

In this paper, we propose an algorithm based on the central limit theorem to compute the sensitivity of the multilayer perceptron (MLP) due to the errors of the inputs and weights. For simplicity and practicality, all inputs and weights studied here are independently identically distributed (i.i.d.). The theoretical results derived from the proposed algorithm show that the sensitivity of the MLP is affected by the number of layers and the number of neurons adopted in each layer. To prove the reliability of the proposed algorithm, some experimental results of the sensitivity are also presented, and they match the theoretical ones. The good agreement between the theoretical results and the experimental results verifies the reliability and feasibility of the proposed algorithm. Furthermore, the proposed algorithm can also be applied to compute precisely the sensitivity of the MLP with any available activation functions and any types of i.i.d. inputs and weights.

Direct recovery of regional tracer kinetics from temporally inconsistent dynamic ECT projections using dimension-reduced time-activity basis

NASA Astrophysics Data System (ADS)

Maltz, Jonathan S.

2000-11-01

We present an algorithm of reduced computational cost which is able to estimate kinetic model parameters directly from dynamic ECT sinograms made up of temporally inconsistent projections. The algorithm exploits the extreme degree of parameter redundancy inherent in linear combinations of the exponential functions which represent the modes of first-order compartmental systems. The singular value decomposition is employed to find a small set of orthogonal functions, the linear combinations of which are able to accurately represent all modes within the physiologically anticipated range in a given study. The reduced-dimension basis is formed as the convolution of this orthogonal set with a measured input function. The Moore-Penrose pseudoinverse is used to find coefficients of this basis. Algorithm performance is evaluated at realistic count rates using MCAT phantom and clinical 99mTc-teboroxime myocardial study data. Phantom data are modelled as originating from a Poisson process. For estimates recovered from a single slice projection set containing 2.5×105 total counts, recovered tissue responses compare favourably with those obtained using more computationally intensive methods. The corresponding kinetic parameter estimates (coefficients of the new basis) exhibit negligible bias, while parameter variances are low, falling within 30% of the Cramér-Rao lower bound.

Intrusion-Tolerant Location Information Services in Intelligent Vehicular Networks

NASA Astrophysics Data System (ADS)

Yan, Gongjun; Yang, Weiming; Shaner, Earl F.; Rawat, Danda B.

Intelligent Vehicular Networks, known as Vehicle-to-Vehicle and Vehicle-to-Roadside wireless communications (also called Vehicular Ad hoc Networks), are revolutionizing our daily driving with better safety and more infortainment. Most, if not all, applications will depend on accurate location information. Thus, it is of importance to provide intrusion-tolerant location information services. In this paper, we describe an adaptive algorithm that detects and filters the false location information injected by intruders. Given a noisy environment of mobile vehicles, the algorithm estimates the high resolution location of a vehicle by refining low resolution location input. We also investigate results of simulations and evaluate the quality of the intrusion-tolerant location service.

The effect of model uncertainty on some optimal routing problems

NASA Technical Reports Server (NTRS)

Mohanty, Bibhu; Cassandras, Christos G.

1991-01-01

The effect of model uncertainties on optimal routing in a system of parallel queues is examined. The uncertainty arises in modeling the service time distribution for the customers (jobs, packets) to be served. For a Poisson arrival process and Bernoulli routing, the optimal mean system delay generally depends on the variance of this distribution. However, as the input traffic load approaches the system capacity the optimal routing assignment and corresponding mean system delay are shown to converge to a variance-invariant point. The implications of these results are examined in the context of gradient-based routing algorithms. An example of a model-independent algorithm using online gradient estimation is also included.

Optimal space communications techniques. [discussion of video signals and delta modulation

NASA Technical Reports Server (NTRS)

Schilling, D. L.

1974-01-01

The encoding of video signals using the Song Adaptive Delta Modulator (Song ADM) is discussed. The video signals are characterized as a sequence of pulses having arbitrary height and width. Although the ADM is suited to tracking signals having fast rise times, it was found that the DM algorithm (which permits an exponential rise for estimating an input step) results in a large overshoot and an underdamped response to the step. An overshoot suppression algorithm which significantly reduces the ringing while not affecting the rise time is presented along with formuli for the rise time and the settling time. Channel errors and their effect on the DM encoded bit stream were investigated.

A soft-computing methodology for noninvasive time-spatial temperature estimation.

PubMed

Teixeira, César A; Ruano, Maria Graça; Ruano, António E; Pereira, Wagner C A

2008-02-01

The safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 degrees C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 degrees C, being "elected" as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.

Estimating Parameters for the Earth-Ionosphere Waveguide Using VLF Narrowband Transmitters

NASA Astrophysics Data System (ADS)

Gross, N. C.; Cohen, M.

2017-12-01

Estimating the D-region (60 to 90 km altitude) ionospheric electron density profile has always been a challenge. The D-region's altitude is too high for aircraft and balloons to reach but is too low for satellites to orbit at. Sounding rocket measurements have been a useful tool for directly measuring the ionosphere, however, these types of measurements are infrequent and costly. A more sustainable type of measurement, for characterizing the D-region, is remote sensing with very low frequency (VLF) waves. Both the lower ionosphere and Earth's ground strongly reflect VLF waves. These two spherical reflectors form what is known as the Earth-ionosphere waveguide. As VLF waves propagate within the waveguide, they interact with the D-region ionosphere, causing amplitude and phase changes that are polarization dependent. These changes can be monitored with a spatially distributed array of receivers and D-region properties can be inferred from these measurements. Researchers have previously used VLF remote sensing techniques, from either narrowband transmitters or sferics, to estimate the density profile, but these estimations are typically during a short time frame and over a narrow propagation region. We report on an effort to improve the understanding of VLF wave propagation by estimating the commonly known h' and beta two parameter exponential electron density profile. Measurements from multiple narrowband transmitters at multiple receivers are taken, concurrently, and input into an algorithm. The cornerstone of the algorithm is an artificial neural network (ANN), where input values are the received narrowband amplitude and phase and the outputs are the estimated h' and beta parameters. Training data for the ANN is generated using the Navy's Long-Wavelength Propagation Capability (LWPC) model. Emphasis is placed on profiling the daytime ionosphere, which has a more stable and predictable profile than the nighttime. Daytime ionospheric disturbances, from high solar activity, are also analyzed.

Method and apparatus for fault tolerance

NASA Technical Reports Server (NTRS)

Masson, Gerald M. (Inventor); Sullivan, Gregory F. (Inventor)

1993-01-01

A method and apparatus for achieving fault tolerance in a computer system having at least a first central processing unit and a second central processing unit. The method comprises the steps of first executing a first algorithm in the first central processing unit on input which produces a first output as well as a certification trail. Next, executing a second algorithm in the second central processing unit on the input and on at least a portion of the certification trail which produces a second output. The second algorithm has a faster execution time than the first algorithm for a given input. Then, comparing the first and second outputs such that an error result is produced if the first and second outputs are not the same. The step of executing a first algorithm and the step of executing a second algorithm preferably takes place over essentially the same time period.

State-Space System Realization with Input- and Output-Data Correlation

NASA Technical Reports Server (NTRS)

Juang, Jer-Nan

1997-01-01

This paper introduces a general version of the information matrix consisting of the autocorrelation and cross-correlation matrices of the shifted input and output data. Based on the concept of data correlation, a new system realization algorithm is developed to create a model directly from input and output data. The algorithm starts by computing a special type of correlation matrix derived from the information matrix. The special correlation matrix provides information on the system-observability matrix and the state-vector correlation. A system model is then developed from the observability matrix in conjunction with other algebraic manipulations. This approach leads to several different algorithms for computing system matrices for use in representing the system model. The relationship of the new algorithms with other realization algorithms in the time and frequency domains is established with matrix factorization of the information matrix. Several examples are given to illustrate the validity and usefulness of these new algorithms.

Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network.

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

Jones, Christian Birk; Robinson, Matt; Yasaei, Yasser

Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often beenmore » perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.« less

Intelligent vehicle safety control strategy in various driving situations

NASA Astrophysics Data System (ADS)

Moon, Seungwuk; Cho, Wanki; Yi, Kyongsu

2010-12-01

This paper describes a safety control strategy for intelligent vehicles with the objective of optimally coordinating the throttle, brake, and active front steering actuator inputs to obtain both lateral stability and longitudinal safety. The control system consists of a supervisor, control algorithms, and a coordinator. From the measurement and estimation signals, the supervisor determines the active control modes among normal driving, longitudinal safety, lateral stability, and integrated safety control mode. The control algorithms consist of longitudinal and lateral stability controllers. The longitudinal controller is designed to improve the driver's comfort during normal, safe-driving situations, and to avoid rear-end collision in vehicle-following situations. The lateral stability controller is designed to obtain the required manoeuvrability and to limit the vehicle body's side-slip angle. To obtain both longitudinal safety and lateral stability control in various driving situations, the coordinator optimally determines the throttle, brake, and active front steering inputs based on the current status of the subject vehicle. Closed-loop simulations with the driver-vehicle-controller system are conducted to investigate the performance of the proposed control strategy. From these simulation results, it is shown that the proposed control algorithm assists the driver in combined severe braking/large steering manoeuvring so that the driver can maintain good manoeuvrability and prevent the vehicle from crashing in vehicle-following situations.

Estimation of seismic quality factor: Artificial neural networks and current approaches

NASA Astrophysics Data System (ADS)

Yıldırım, Eray; Saatçılar, Ruhi; Ergintav, Semih

2017-01-01

The aims of this study are to estimate soil attenuation using alternatives to traditional methods, to compare results of using these methods, and to examine soil properties using the estimated results. The performances of all methods, amplitude decay, spectral ratio, Wiener filter, and artificial neural network (ANN) methods, are examined on field and synthetic data with noise and without noise. High-resolution seismic reflection field data from Yeniköy (Arnavutköy, İstanbul) was used as field data, and 424 estimations of Q values were made for each method (1,696 total). While statistical tests on synthetic and field data are quite close to the Q value estimation results of ANN, Wiener filter, and spectral ratio methods, the amplitude decay methods showed a higher estimation error. According to previous geological and geophysical studies in this area, the soil is water-saturated, quite weak, consisting of clay and sandy units, and, because of current and past landslides in the study area and its vicinity, researchers reported heterogeneity in the soil. Under the same physical conditions, Q value calculated on field data can be expected to be 7.9 and 13.6. ANN models with various structures, training algorithm, input, and number of neurons are investigated. A total of 480 ANN models were generated consisting of 60 models for noise-free synthetic data, 360 models for different noise content synthetic data and 60 models to apply to the data collected in the field. The models were tested to determine the most appropriate structure and training algorithm. In the final ANN, the input vectors consisted of the difference of the width, energy, and distance of seismic traces, and the output was Q value. Success rate of both ANN methods with noise-free and noisy synthetic data were higher than the other three methods. Also according to the statistical tests on estimated Q value from field data, the method showed results that are more suitable. The Q value can be estimated practically and quickly by processing the traces with the recommended ANN model. Consequently, the ANN method could be used for estimating Q value from seismic data.

Cybernetic group method of data handling (GMDH) statistical learning for hyperspectral remote sensing inverse problems in coastal ocean optics

NASA Astrophysics Data System (ADS)

Filippi, Anthony Matthew

For complex systems, sufficient a priori knowledge is often lacking about the mathematical or empirical relationship between cause and effect or between inputs and outputs of a given system. Automated machine learning may offer a useful solution in such cases. Coastal marine optical environments represent such a case, as the optical remote sensing inverse problem remains largely unsolved. A self-organizing, cybernetic mathematical modeling approach known as the group method of data handling (GMDH), a type of statistical learning network (SLN), was used to generate explicit spectral inversion models for optically shallow coastal waters. Optically shallow water light fields represent a particularly difficult challenge in oceanographic remote sensing. Several algorithm-input data treatment combinations were utilized in multiple experiments to automatically generate inverse solutions for various inherent optical property (IOP), bottom optical property (BOP), constituent concentration, and bottom depth estimations. The objective was to identify the optimal remote-sensing reflectance Rrs(lambda) inversion algorithm. The GMDH also has the potential of inductive discovery of physical hydro-optical laws. Simulated data were used to develop generalized, quasi-universal relationships. The Hydrolight numerical forward model, based on radiative transfer theory, was used to compute simulated above-water remote-sensing reflectance Rrs(lambda) psuedodata, matching the spectral channels and resolution of the experimental Naval Research Laboratory Ocean PHILLS (Portable Hyperspectral Imager for Low-Light Spectroscopy) sensor. The input-output pairs were for GMDH and artificial neural network (ANN) model development, the latter of which was used as a baseline, or control, algorithm. Both types of models were applied to in situ and aircraft data. Also, in situ spectroradiometer-derived Rrs(lambda) were used as input to an optimization-based inversion procedure. Target variables included bottom depth z b, chlorophyll a concentration [chl- a], spectral bottom irradiance reflectance Rb(lambda), and spectral total absorption a(lambda) and spectral total backscattering bb(lambda) coefficients. When applying the cybernetic and neural models to in situ HyperTSRB-derived Rrs, the difference in the means of the absolute error of the inversion estimates for zb was significant (alpha = 0.05). GMDH yielded significantly better zb than the ANN. The ANN model posted a mean absolute error (MAE) of 0.62214 m, compared with 0.55161 m for GMDH.

Simultaneous reconstruction of temperature distribution and radiative properties in participating media using a hybrid LSQR-PSO algorithm

NASA Astrophysics Data System (ADS)

Niu, Chun-Yang; Qi, Hong; Huang, Xing; Ruan, Li-Ming; Wang, Wei; Tan, He-Ping

2015-11-01

A hybrid least-square QR decomposition (LSQR)-particle swarm optimization (LSQR-PSO) algorithm was developed to estimate the three-dimensional (3D) temperature distributions and absorption coefficients simultaneously. The outgoing radiative intensities at the boundary surface of the absorbing media were simulated by the line-of-sight (LOS) method, which served as the input for the inverse analysis. The retrieval results showed that the 3D temperature distributions of the participating media with known radiative properties could be retrieved accurately using the LSQR algorithm, even with noisy data. For the participating media with unknown radiative properties, the 3D temperature distributions and absorption coefficients could be retrieved accurately using the LSQR-PSO algorithm even with measurement errors. It was also found that the temperature field could be estimated more accurately than the absorption coefficients. In order to gain insight into the effects on the accuracy of temperature distribution reconstruction, the selection of the detection direction and the angle between two detection directions was also analyzed. Project supported by the Major National Scientific Instruments and Equipment Development Special Foundation of China (Grant No. 51327803), the National Natural Science Foundation of China (Grant No. 51476043), and the Fund of Tianjin Key Laboratory of Civil Aircraft Airworthiness and Maintenance in Civil Aviation University of China.

Surface Downward Longwave Radiation Retrieval Algorithm for GEO-KOMPSAT-2A/AMI

NASA Astrophysics Data System (ADS)

Ahn, Seo-Hee; Lee, Kyu-Tae; Rim, Se-Hun; Zo, Il-Sung; Kim, Bu-Yo

2018-05-01

This study contributes to the development of an algorithm to retrieve the Earth's surface downward longwave radiation (DLR) for 2nd Geostationary Earth Orbit KOrea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A)/Advanced Meteorological Imager (AMI). Regarding simulation data for algorithm development, we referred to Clouds and the Earth's Radiant Energy System (CERES), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis data. The clear sky DLR calculations were in good agreement with the Gangneung-Wonju National University (GWNU) Line-By-Line (LBL) model. Compared with CERES data, the Root Mean Square Error (RMSE) was 10.14Wm-2. In the case of cloudy sky DLR, we estimated the cloud base temperature empirically by utilizing cloud liquid water content (LWC) according to the cloud type. As a result, the correlation coefficients with CERES all sky DLRs were greater than 0.99. However, the RMSE between calculated DLR and CERES data was about 16.67Wm-2, due to ice clouds and problems of mismatched spatial and temporal resolutions for input data. This error may be reduced when GK-2A is launched and its products can be used as input data. Accordingly, further study is needed to improve the accuracy of DLR calculation by using high-resolution input data. In addition, when compared with BSRN surface-based observational data and retrieved DLR for all sky, the correlation coefficient was 0.86 and the RMSE was 31.55 Wm-2, which indicates relatively high accuracy. It is expected that increasing the number of experimental Cases will reduce the error.

A Novel Angle Computation and Calibration Algorithm of Bio-Inspired Sky-Light Polarization Navigation Sensor

PubMed Central

Xian, Zhiwen; Hu, Xiaoping; Lian, Junxiang; Zhang, Lilian; Cao, Juliang; Wang, Yujie; Ma, Tao

2014-01-01

Navigation plays a vital role in our daily life. As traditional and commonly used navigation technologies, Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) can provide accurate location information, but suffer from the accumulative error of inertial sensors and cannot be used in a satellite denied environment. The remarkable navigation ability of animals shows that the pattern of the polarization sky can be used for navigation. A bio-inspired POLarization Navigation Sensor (POLNS) is constructed to detect the polarization of skylight. Contrary to the previous approach, we utilize all the outputs of POLNS to compute input polarization angle, based on Least Squares, which provides optimal angle estimation. In addition, a new sensor calibration algorithm is presented, in which the installation angle errors and sensor biases are taken into consideration. Derivation and implementation of our calibration algorithm are discussed in detail. To evaluate the performance of our algorithms, simulation and real data test are done to compare our algorithms with several exiting algorithms. Comparison results indicate that our algorithms are superior to the others and are more feasible and effective in practice. PMID:25225872

Real-Time Feedback Control of Flow-Induced Cavity Tones. Part 2; Adaptive Control

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cabell, R. H.; Cattafesta, L. N., III

2006-01-01

An adaptive generalized predictive control (GPC) algorithm was formulated and applied to the cavity flow-tone problem. The algorithm employs gradient descent to update the GPC coefficients at each time step. Past input-output data and an estimate of the open-loop pulse response sequence are all that is needed to implement the algorithm for application at fixed Mach numbers. Transient measurements made during controller adaptation revealed that the controller coefficients converged to a steady state in the mean, and this implies that adaptation can be turned off at some point with no degradation in control performance. When converged, the control algorithm demonstrated multiple Rossiter mode suppression at fixed Mach numbers ranging from 0.275 to 0.38. However, as in the case of fixed-gain GPC, the adaptive GPC performance was limited by spillover in sidebands around the suppressed Rossiter modes. The algorithm was also able to maintain suppression of multiple cavity tones as the freestream Mach number was varied over a modest range (0.275 to 0.29). Beyond this range, stable operation of the control algorithm was not possible due to the fixed plant model in the algorithm.

Formulation d'un modele mathematique par des techniques d'estimation de parametres a partir de donnees de vol pour l'helicoptere Bell 427 et l'avion F/A-18 servant a la recherches en aeroservoelasticite

NASA Astrophysics Data System (ADS)

Nadeau-Beaulieu, Michel

In this thesis, three mathematical models are built from flight test data for different aircraft design applications: a ground dynamics model for the Bell 427 helicopter, a prediction model for the rotor and engine parameters for the same helicopter type and a simulation model for the aeroelastic deflections of the F/A-18. In the ground dynamics application, the model structure is derived from physics where the normal force between the helicopter and the ground is modelled as a vertical spring and the frictional force is modelled with static and dynamic friction coefficients. The ground dynamics model coefficients are optimized to ensure that the model matches the landing data within the FAA (Federal Aviation Administration) tolerance bands for a level D flight simulator. In the rotor and engine application, rotors torques (main and tail), the engine torque and main rotor speed are estimated using a state-space model. The model inputs are nonlinear terms derived from the pilot control inputs and the helicopter states. The model parameters are identified using the subspace method and are further optimised with the Levenberg-Marquardt minimisation algorithm. The model built with the subspace method provides an excellent estimate of the outputs within the FAA tolerance bands. The F/A-18 aeroelastic state-space model is built from flight test. The research concerning this model is divided in two parts. Firstly, the deflection of a given structural surface on the aircraft following a differential ailerons control input is represented by a Multiple Inputs Single Outputs linear model whose inputs are the ailerons positions and the structural surfaces deflections. Secondly, a single state-space model is used to represent the deflection of the aircraft wings and trailing edge flaps following any control input. In this case the model is made non-linear by multiplying model inputs into higher order terms and using these terms as the inputs of the state-space equations. In both cases, the identification method is the subspace method. Most fit coefficients between the estimated and the measured signals are above 73% and most correlation coefficient are higher than 90%.

Modeling Input Errors to Improve Uncertainty Estimates for Sediment Transport Model Predictions

NASA Astrophysics Data System (ADS)

Jung, J. Y.; Niemann, J. D.; Greimann, B. P.

2016-12-01

Bayesian methods using Markov chain Monte Carlo algorithms have recently been applied to sediment transport models to assess the uncertainty in the model predictions due to the parameter values. Unfortunately, the existing approaches can only attribute overall uncertainty to the parameters. This limitation is critical because no model can produce accurate forecasts if forced with inaccurate input data, even if the model is well founded in physical theory. In this research, an existing Bayesian method is modified to consider the potential errors in input data during the uncertainty evaluation process. The input error is modeled using Gaussian distributions, and the means and standard deviations are treated as uncertain parameters. The proposed approach is tested by coupling it to the Sedimentation and River Hydraulics - One Dimension (SRH-1D) model and simulating a 23-km reach of the Tachia River in Taiwan. The Wu equation in SRH-1D is used for computing the transport capacity for a bed material load of non-cohesive material. Three types of input data are considered uncertain: (1) the input flowrate at the upstream boundary, (2) the water surface elevation at the downstream boundary, and (3) the water surface elevation at a hydraulic structure in the middle of the reach. The benefits of modeling the input errors in the uncertainty analysis are evaluated by comparing the accuracy of the most likely forecast and the coverage of the observed data by the credible intervals to those of the existing method. The results indicate that the internal boundary condition has the largest uncertainty among those considered. Overall, the uncertainty estimates from the new method are notably different from those of the existing method for both the calibration and forecast periods.

An innovative localisation algorithm for railway vehicles

NASA Astrophysics Data System (ADS)

Allotta, B.; D'Adamio, P.; Malvezzi, M.; Pugi, L.; Ridolfi, A.; Rindi, A.; Vettori, G.

2014-11-01

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.

Is this the right normalization? A diagnostic tool for ChIP-seq normalization.

PubMed

Angelini, Claudia; Heller, Ruth; Volkinshtein, Rita; Yekutieli, Daniel

2015-05-09

Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases. Despite the substantial impact that the choice of the normalization method can have on the results of a ChIP-seq data analysis, their assessment is not fully explored in the literature. In particular, there are no diagnostic tools that show whether the applied normalization is indeed appropriate for the data being analyzed. In this work we propose a novel diagnostic tool to examine the appropriateness of the estimated normalization procedure. By plotting the empirical densities of log relative risks in bins of equal read count, along with the estimated normalization constant, after logarithmic transformation, the researcher is able to assess the appropriateness of the estimated normalization constant. We use the diagnostic plot to evaluate the appropriateness of the estimates obtained by CisGenome, NCIS and CCAT on several real data examples. Moreover, we show the impact that the choice of the normalization constant can have on standard tools for peak calling such as MACS or SICER. Finally, we propose a novel procedure for controlling the FDR using sample swapping. This procedure makes use of the estimated normalization constant in order to gain power over the naive choice of constant (used in MACS and SICER), which is the ratio of the total number of reads in the ChIP and Input samples. Linear normalization approaches aim to estimate a scale factor, r, to adjust for different sequencing depths when comparing ChIP versus Input samples. The estimated scaling factor can easily be incorporated in many peak caller algorithms to improve the accuracy of the peak identification. The diagnostic plot proposed in this paper can be used to assess how adequate ChIP/Input normalization constants are, and thus it allows the user to choose the most adequate estimate for the analysis.

A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing

NASA Astrophysics Data System (ADS)

Zhu, Yansong; Jha, Abhinav K.; Dreyer, Jakob K.; Le, Hanh N. D.; Kang, Jin U.; Roland, Per E.; Wong, Dean F.; Rahmim, Arman

2017-02-01

Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via l1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm-1, absorption coefficient: 0.1 cm-1 and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional l2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.

Algorithm for fast event parameters estimation on GEM acquired data

NASA Astrophysics Data System (ADS)

Linczuk, Paweł; Krawczyk, Rafał D.; Poźniak, Krzysztof T.; Kasprowicz, Grzegorz; Wojeński, Andrzej; Chernyshova, Maryna; Czarski, Tomasz

2016-09-01

We present study of a software-hardware environment for developing fast computation with high throughput and low latency methods, which can be used as back-end in High Energy Physics (HEP) and other High Performance Computing (HPC) systems, based on high amount of input from electronic sensor based front-end. There is a parallelization possibilities discussion and testing on Intel HPC solutions with consideration of applications with Gas Electron Multiplier (GEM) measurement systems presented in this paper.

Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization

PubMed Central

Spielman, Seth E.; Folch, David C.

2015-01-01

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold. PMID:25723176

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

PubMed Central

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

2014-01-01

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

Reducing uncertainty in the american community survey through data-driven regionalization.

PubMed

Spielman, Seth E; Folch, David C

2015-01-01

The American Community Survey (ACS) is the largest survey of US households and is the principal source for neighborhood scale information about the US population and economy. The ACS is used to allocate billions in federal spending and is a critical input to social scientific research in the US. However, estimates from the ACS can be highly unreliable. For example, in over 72% of census tracts, the estimated number of children under 5 in poverty has a margin of error greater than the estimate. Uncertainty of this magnitude complicates the use of social data in policy making, research, and governance. This article presents a heuristic spatial optimization algorithm that is capable of reducing the margins of error in survey data via the creation of new composite geographies, a process called regionalization. Regionalization is a complex combinatorial problem. Here rather than focusing on the technical aspects of regionalization we demonstrate how to use a purpose built open source regionalization algorithm to process survey data in order to reduce the margins of error to a user-specified threshold.

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

PubMed

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

2014-02-25

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

Competitive repetition suppression (CoRe) clustering: a biologically inspired learning model with application to robust clustering.

PubMed

Bacciu, Davide; Starita, Antonina

2008-11-01

Determining a compact neural coding for a set of input stimuli is an issue that encompasses several biological memory mechanisms as well as various artificial neural network models. In particular, establishing the optimal network structure is still an open problem when dealing with unsupervised learning models. In this paper, we introduce a novel learning algorithm, named competitive repetition-suppression (CoRe) learning, inspired by a cortical memory mechanism called repetition suppression (RS). We show how such a mechanism is used, at various levels of the cerebral cortex, to generate compact neural representations of the visual stimuli. From the general CoRe learning model, we derive a clustering algorithm, named CoRe clustering, that can automatically estimate the unknown cluster number from the data without using a priori information concerning the input distribution. We illustrate how CoRe clustering, besides its biological plausibility, posses strong theoretical properties in terms of robustness to noise and outliers, and we provide an error function describing CoRe learning dynamics. Such a description is used to analyze CoRe relationships with the state-of-the art clustering models and to highlight CoRe similitude with rival penalized competitive learning (RPCL), showing how CoRe extends such a model by strengthening the rival penalization estimation by means of loss functions from robust statistics.

Toward a Real-Time Measurement-Based System for Estimation of Helicopter Engine Degradation Due to Compressor Erosion

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Simo, Donald L.

2007-01-01

This paper presents a preliminary demonstration of an automated health assessment tool, capable of real-time on-board operation using existing engine control hardware. The tool allows operators to discern how rapidly individual turboshaft engines are degrading. As the compressor erodes, performance is lost, and with it the ability to generate power. Thus, such a tool would provide an instant assessment of the engine s fitness to perform a mission, and would help to pinpoint any abnormal wear or performance anomalies before they became serious, thereby decreasing uncertainty and enabling improved maintenance scheduling. The research described in the paper utilized test stand data from a T700-GE-401 turboshaft engine that underwent sand-ingestion testing to scale a model-based compressor efficiency degradation estimation algorithm. This algorithm was then applied to real-time Health Usage and Monitoring System (HUMS) data from a T700-GE-701C to track compressor efficiency on-line. The approach uses an optimal estimator called a Kalman filter. The filter is designed to estimate the compressor efficiency using only data from the engine s sensors as input.

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

NASA Astrophysics Data System (ADS)

Wentworth, Mami Tonoe

Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

Blind equalization with criterion with memory nonlinearity

NASA Astrophysics Data System (ADS)

Chen, Yuanjie; Nikias, Chrysostomos L.; Proakis, John G.

1992-06-01

Blind equalization methods usually combat the linear distortion caused by a nonideal channel via a transversal filter, without resorting to the a priori known training sequences. We introduce a new criterion with memory nonlinearity (CRIMNO) for the blind equalization problem. The basic idea of this criterion is to augment the Godard [or constant modulus algorithm (CMA)] cost function with additional terms that penalize the autocorrelations of the equalizer outputs. Several variations of the CRIMNO algorithms are derived, with the variations dependent on (1) whether the empirical averages or the single point estimates are used to approximate the expectations, (2) whether the recent or the delayed equalizer coefficients are used, and (3) whether the weights applied to the autocorrelation terms are fixed or are allowed to adapt. Simulation experiments show that the CRIMNO algorithm, and especially its adaptive weight version, exhibits faster convergence speed than the Godard (or CMA) algorithm. Extensions of the CRIMNO criterion to accommodate the case of correlated inputs to the channel are also presented.

Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.

Noise suppression methods for robust speech processing

NASA Astrophysics Data System (ADS)

Boll, S. F.; Ravindra, H.; Randall, G.; Armantrout, R.; Power, R.

1980-05-01

Robust speech processing in practical operating environments requires effective environmental and processor noise suppression. This report describes the technical findings and accomplishments during this reporting period for the research program funded to develop real time, compressed speech analysis synthesis algorithms whose performance in invariant under signal contamination. Fulfillment of this requirement is necessary to insure reliable secure compressed speech transmission within realistic military command and control environments. Overall contributions resulting from this research program include the understanding of how environmental noise degrades narrow band, coded speech, development of appropriate real time noise suppression algorithms, and development of speech parameter identification methods that consider signal contamination as a fundamental element in the estimation process. This report describes the current research and results in the areas of noise suppression using the dual input adaptive noise cancellation using the short time Fourier transform algorithms, articulation rate change techniques, and a description of an experiment which demonstrated that the spectral subtraction noise suppression algorithm can improve the intelligibility of 2400 bps, LPC 10 coded, helicopter speech by 10.6 point.

Digital Terrain from a Two-Step Segmentation and Outlier-Based Algorithm

NASA Astrophysics Data System (ADS)

Hingee, Kassel; Caccetta, Peter; Caccetta, Louis; Wu, Xiaoliang; Devereaux, Drew

2016-06-01

We present a novel ground filter for remotely sensed height data. Our filter has two phases: the first phase segments the DSM with a slope threshold and uses gradient direction to identify candidate ground segments; the second phase fits surfaces to the candidate ground points and removes outliers. Digital terrain is obtained by a surface fit to the final set of ground points. We tested the new algorithm on digital surface models (DSMs) for a 9600km2 region around Perth, Australia. This region contains a large mix of land uses (urban, grassland, native forest and plantation forest) and includes both a sandy coastal plain and a hillier region (elevations up to 0.5km). The DSMs are captured annually at 0.2m resolution using aerial stereo photography, resulting in 1.2TB of input data per annum. Overall accuracy of the filter was estimated to be 89.6% and on a small semi-rural subset our algorithm was found to have 40% fewer errors compared to Inpho's Match-T algorithm.

An Online Observer for Minimization of Pulsating Torque in SMPM Motors

PubMed Central

Roșca, Lucian

2016-01-01

A persistent problem of surface mounted permanent magnet (SMPM) motors is the non-uniformity of the developed torque. Either the motor design or the motor control needs to be improved in order to minimize the periodic disturbances. This paper proposes a new control technique for reducing periodic disturbances in permanent magnet (PM) electro-mechanical actuators, by advancing a new observer/estimator paradigm. A recursive estimation algorithm is implemented for online control. The compensating signal is identified and added as feedback to the control signal of the servo motor. Compensation is evaluated for different values of the input signal, to show robustness of the proposed method. PMID:27089182

Identifiability and Performance Analysis of Output Over-sampling Approach to Direct Closed-loop Identification

NASA Astrophysics Data System (ADS)

Sun, Lianming; Sano, Akira

Output over-sampling based closed-loop identification algorithm is investigated in this paper. Some instinct properties of the continuous stochastic noise and the plant input, output in the over-sampling approach are analyzed, and they are used to demonstrate the identifiability in the over-sampling approach and to evaluate its identification performance. Furthermore, the selection of plant model order, the asymptotic variance of estimated parameters and the asymptotic variance of frequency response of the estimated model are also explored. It shows that the over-sampling approach can guarantee the identifiability and improve the performance of closed-loop identification greatly.

A new analytical method for characterizing nonlinear visual processes with stimuli of arbitrary distribution: Theory and applications.

PubMed

Hayashi, Ryusuke; Watanabe, Osamu; Yokoyama, Hiroki; Nishida, Shin'ya

2017-06-01

Characterization of the functional relationship between sensory inputs and neuronal or observers' perceptual responses is one of the fundamental goals of systems neuroscience and psychophysics. Conventional methods, such as reverse correlation and spike-triggered data analyses are limited in their ability to resolve complex and inherently nonlinear neuronal/perceptual processes because these methods require input stimuli to be Gaussian with a zero mean. Recent studies have shown that analyses based on a generalized linear model (GLM) do not require such specific input characteristics and have advantages over conventional methods. GLM, however, relies on iterative optimization algorithms and its calculation costs become very expensive when estimating the nonlinear parameters of a large-scale system using large volumes of data. In this paper, we introduce a new analytical method for identifying a nonlinear system without relying on iterative calculations and yet also not requiring any specific stimulus distribution. We demonstrate the results of numerical simulations, showing that our noniterative method is as accurate as GLM in estimating nonlinear parameters in many cases and outperforms conventional, spike-triggered data analyses. As an example of the application of our method to actual psychophysical data, we investigated how different spatiotemporal frequency channels interact in assessments of motion direction. The nonlinear interaction estimated by our method was consistent with findings from previous vision studies and supports the validity of our method for nonlinear system identification.

The Day-1 GPM Combined Precipitation Algorithm: IMERG

NASA Astrophysics Data System (ADS)

Huffman, G. J.; Bolvin, D. T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Sorooshian, S.; Xie, P.

2012-12-01

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) algorithm will provide the at-launch combined-sensor precipitation dataset being produced by the U.S. GPM Science Team. IMERG is being developed as a unified U.S. algorithm that takes advantage of strengths in three current U.S. algorithms: - the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses inter-satellite calibration of precipitation estimates and monthly scale combination of satellite and gauge analyses; - the CPC Morphing algorithm with Kalman Filtering (KF-CMORPH), which provides quality-weighted time interpolation of precipitation patterns following storm motion; and - the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks using a Cloud Classification System (PERSIANN-CCS), which provides a neural-network-based scheme for generating microwave-calibrated precipitation estimates from geosynchronous infrared brightness temperatures, and filters out some non-raining cold clouds. The goal is to provide a long-term, fine-scale record of global precipitation from the entire constellation of precipitation-relevant satellite sensors, with input from surface precipitation gauges. The record will begin January 1998 at the start of the Tropical Rainfall Measuring Mission (TRMM) and extend as GPM records additional data. Although homogeneity is considered desirable, the use of diverse and evolving data sources works against the strict long-term homogeneity that characterizes a Climate Data Record (CDR). This talk will briefly review the design requirements for IMERG, including multiple runs at different latencies (most likely around 4 hours, 12 hours, and 2 months after observation time), various intermediate data fields as part of the IMERG data file, and the plans to bring up IMERG with calibration by TRMM initially, transitioning to GPM when its individual-sensor precipitation algorithms are fully functional. Then we will present some early examples of IMERG data products and compare them with existing products to illustrate how the design of IMERG affects the overall performance of the algorithm.

Planck 2015 results. VI. LFI mapmaking

NASA Astrophysics Data System (ADS)

Planck Collaboration; Ade, P. A. R.; Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoît, A.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Bucher, M.; Burigana, C.; Butler, R. C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Chamballu, A.; Chary, R.-R.; Christensen, P. R.; Colombi, S.; Colombo, L. P. L.; Crill, B. P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R. D.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Dickinson, C.; Diego, J. M.; Dole, H.; Donzelli, S.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Efstathiou, G.; Elsner, F.; Enßlin, T. A.; Eriksen, H. K.; Fergusson, J.; Finelli, F.; Forni, O.; Frailis, M.; Franceschi, E.; Frejsel, A.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giraud-Héraud, Y.; Gjerløw, E.; González-Nuevo, J.; Górski, K. M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Hansen, F. K.; Hanson, D.; Harrison, D. L.; Henrot-Versillé, S.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Holmes, W. A.; Hornstrup, A.; Hovest, W.; Huffenberger, K. M.; Hurier, G.; Jaffe, A. H.; Jaffe, T. R.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kiiveri, K.; Kisner, T. S.; Knoche, J.; Kunz, M.; Kurki-Suonio, H.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Leahy, J. P.; Leonardi, R.; Lesgourgues, J.; Levrier, F.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; Lindholm, V.; López-Caniego, M.; Lubin, P. M.; Macías-Pérez, J. F.; Maggio, G.; Maino, D.; Mandolesi, N.; Mangilli, A.; Martin, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; Mazzotta, P.; McGehee, P.; Meinhold, P. R.; Melchiorri, A.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Murphy, J. A.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C. B.; Nørgaard-Nielsen, H. U.; Novikov, D.; Novikov, I.; Paci, F.; Pagano, L.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Pearson, T. J.; Perdereau, O.; Perotto, L.; Perrotta, F.; Pettorino, V.; Pierpaoli, E.; Pietrobon, D.; Pointecouteau, E.; Polenta, G.; Pratt, G. W.; Prézeau, G.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renzi, A.; Rocha, G.; Rosset, C.; Rossetti, M.; Roudier, G.; Rubiño-Martín, J. A.; Rusholme, B.; Sandri, M.; Santos, D.; Savelainen, M.; Scott, D.; Seiffert, M. D.; Shellard, E. P. S.; Spencer, L. D.; Stolyarov, V.; Stompor, R.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Vassallo, T.; Vielva, P.; Villa, F.; Wade, L. A.; Wandelt, B. D.; Watson, R.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.

2016-09-01

This paper describes the mapmaking procedure applied to Planck Low Frequency Instrument (LFI) data. The mapmaking step takes as input the calibrated timelines and pointing information. The main products are sky maps of I, Q, and U Stokes components. For the first time, we present polarization maps at LFI frequencies. The mapmaking algorithm is based on a destriping technique, which is enhanced with a noise prior. The Galactic region is masked to reduce errors arising from bandpass mismatch and high signal gradients. We apply horn-uniform radiometer weights to reduce the effects of beam-shape mismatch. The algorithm is the same as used for the 2013 release, apart from small changes in parameter settings. We validate the procedure through simulations. Special emphasis is put on the control of systematics, which is particularly important for accurate polarization analysis. We also produce low-resolution versions of the maps and corresponding noise covariance matrices. These serve as input in later analysis steps and parameter estimation. The noise covariance matrices are validated through noise Monte Carlo simulations. The residual noise in the map products is characterized through analysis of half-ring maps, noise covariance matrices, and simulations.

[Optimization of the pseudorandom input signals used for the forced oscillation technique].

PubMed

Liu, Xiaoli; Zhang, Nan; Liang, Hong; Zhang, Zhengbo; Li, Deyu; Wang, Weidong

2017-10-01

The forced oscillation technique (FOT) is an active pulmonary function measurement technique that was applied to identify the mechanical properties of the respiratory system using external excitation signals. FOT commonly includes single frequency sine, pseudorandom and periodic impulse excitation signals. Aiming at preventing the time-domain amplitude overshoot that might exist in the acquisition of combined multi sinusoidal pseudorandom signals, this paper studied the phase optimization of pseudorandom signals. We tried two methods including the random phase combination and time-frequency domain swapping algorithm to solve this problem, and used the crest factor to estimate the effect of optimization. Furthermore, in order to make the pseudorandom signals met the requirement of the respiratory system identification in 4-40 Hz, we compensated the input signals' amplitudes at the low frequency band (4-18 Hz) according to the frequency-response curve of the oscillation unit. Resuts showed that time-frequency domain swapping algorithm could effectively optimize the phase combination of pseudorandom signals. Moreover, when the amplitudes at low frequencies were compensated, the expected stimulus signals which met the performance requirements were obtained eventually.

GBA manager: an online tool for querying low-complexity regions in proteins.

PubMed

Bandyopadhyay, Nirmalya; Kahveci, Tamer

2010-01-01

Abstract We developed GBA Manager, an online software that facilitates the Graph-Based Algorithm (GBA) we proposed in our earlier work. GBA identifies the low-complexity regions (LCR) of protein sequences. GBA exploits a similarity matrix, such as BLOSUM62, to compute the complexity of the subsequences of the input protein sequence. It uses a graph-based algorithm to accurately compute the regions that have low complexities. GBA Manager is a user friendly web-service that enables online querying of protein sequences using GBA. In addition to querying capabilities of the existing GBA algorithm, GBA Manager computes the p-values of the LCR identified. The p-value gives an estimate of the possibility that the region appears by chance. GBA Manager presents the output in three different understandable formats. GBA Manager is freely accessible at http://bioinformatics.cise.ufl.edu/GBA/GBA.htm .

Salient object detection: manifold-based similarity adaptation approach

NASA Astrophysics Data System (ADS)

Zhou, Jingbo; Ren, Yongfeng; Yan, Yunyang; Gao, Shangbing

2014-11-01

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

Development of an Algorithm for Automatic Analysis of the Impedance Spectrum Based on a Measurement Model

NASA Astrophysics Data System (ADS)

Kobayashi, Kiyoshi; Suzuki, Tohru S.

2018-03-01

A new algorithm for the automatic estimation of an equivalent circuit and the subsequent parameter optimization is developed by combining the data-mining concept and complex least-squares method. In this algorithm, the program generates an initial equivalent-circuit model based on the sampling data and then attempts to optimize the parameters. The basic hypothesis is that the measured impedance spectrum can be reproduced by the sum of the partial-impedance spectra presented by the resistor, inductor, resistor connected in parallel to a capacitor, and resistor connected in parallel to an inductor. The adequacy of the model is determined by using a simple artificial-intelligence function, which is applied to the output function of the Levenberg-Marquardt module. From the iteration of model modifications, the program finds an adequate equivalent-circuit model without any user input to the equivalent-circuit model.

Benchmarking wide swath altimetry-based river discharge estimation algorithms for the Ganges river system

NASA Astrophysics Data System (ADS)

Bonnema, Matthew G.; Sikder, Safat; Hossain, Faisal; Durand, Michael; Gleason, Colin J.; Bjerklie, David M.

2016-04-01

The objective of this study is to compare the effectiveness of three algorithms that estimate discharge from remotely sensed observables (river width, water surface height, and water surface slope) in anticipation of the forthcoming NASA/CNES Surface Water and Ocean Topography (SWOT) mission. SWOT promises to provide these measurements simultaneously, and the river discharge algorithms included here are designed to work with these data. Two algorithms were built around Manning's equation, the Metropolis Manning (MetroMan) method, and the Mean Flow and Geomorphology (MFG) method, and one approach uses hydraulic geometry to estimate discharge, the at-many-stations hydraulic geometry (AMHG) method. A well-calibrated and ground-truthed hydrodynamic model of the Ganges river system (HEC-RAS) was used as reference for three rivers from the Ganges River Delta: the main stem of Ganges, the Arial-Khan, and the Mohananda Rivers. The high seasonal variability of these rivers due to the Monsoon presented a unique opportunity to thoroughly assess the discharge algorithms in light of typical monsoon regime rivers. It was found that the MFG method provides the most accurate discharge estimations in most cases, with an average relative root-mean-squared error (RRMSE) across all three reaches of 35.5%. It is followed closely by the Metropolis Manning algorithm, with an average RRMSE of 51.5%. However, the MFG method's reliance on knowledge of prior river discharge limits its application on ungauged rivers. In terms of input data requirement at ungauged regions with no prior records, the Metropolis Manning algorithm provides a more practical alternative over a region that is lacking in historical observations as the algorithm requires less ancillary data. The AMHG algorithm, while requiring the least prior river data, provided the least accurate discharge measurements with an average wet and dry season RRMSE of 79.8% and 119.1%, respectively, across all rivers studied. This poor performance is directly traced to poor estimation of AMHG via a remotely sensed proxy, and results improve commensurate with MFG and MetroMan when prior AMHG information is given to the method. Therefore, we cannot recommend use of AMHG without inclusion of this prior information, at least for the studied rivers. The dry season discharge (within-bank flow) was captured well by all methods, while the wet season (floodplain flow) appeared more challenging. The picture that emerges from this study is that a multialgorithm approach may be appropriate during flood inundation periods in Ganges Delta.

Affine Projection Algorithm with Improved Data-Selective Method Using the Condition Number

NASA Astrophysics Data System (ADS)

Ban, Sung Jun; Lee, Chang Woo; Kim, Sang Woo

Recently, a data-selective method has been proposed to achieve low misalignment in affine projection algorithm (APA) by keeping the condition number of an input data matrix small. We present an improved method, and a complexity reduction algorithm for the APA with the data-selective method. Experimental results show that the proposed algorithm has lower misalignment and a lower condition number for an input data matrix than both the conventional APA and the APA with the previous data-selective method.

Soil clay content controls the turnover of slow soil carbon across Chinese cropland

NASA Astrophysics Data System (ADS)

Feng, W.; Jiang, J.; Li, J.

2017-12-01

Improving the prediction of changes in global soil organic carbon (SOC) lies in accurate estimate of C inputs to soils and SOC turnover time. Since C inputs to soils in cropland can be estimated due to well documented data of crop yields, SOC turnover rate becomes critical for accurate prediction of changes in SOC. The laboratory incubation is widely used but cannot well represent the turnover of slow soil C that accounts for the majority of total SOC, while the long-term observation of temporal changes in SOC stock offers an opportunity to estimate the turnover of slow soil C. Using time series data of SOC stock of twenty long-term agricultural trials that have initiated since 1990 in China, we estimated SOC turnover rates based on changes in soil C pool size and aimed to identify the dominant controls on SOC turnover rate across Chinese cropland. We used the two-pool first-order kinetic soil C model and the inverse modeling with Markov chain the Monte Carlo algorithm, and estimated humification coefficient (h) of C inputs to soils, turnover rates of fast and slow soil C pools, and the transfer coefficient between these two soil C pools. The preliminary results show that the turnover rate of slow soil C is positively correlated with climate (i.e. mean annual temperature and precipitation) but negatively correlated with the clay content, demonstrating that the clay content is important in regulating SOC turnover rates. The ratio of humification coefficient to C turnover rate (h/k) that indicates soil C sequestration efficiency, is negatively correlated with climate and positively correlated with the clay content. In addition, the quantity of C inputs is correlated with h/k and the turnover rate of slow soil C, suggesting that the quantity of C inputs plays an important role in mediating C sequestration efficiency. Further results will inform us the main controls on SOC turnover in Chinese cropland. Keywords: SOC; turnover; long-term trial; temporal change; clay content; inverse modeling

PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization.

PubMed

Antanasijević, Davor Z; Pocajt, Viktor V; Povrenović, Dragan S; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A

2013-01-15

This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables. Copyright © 2012 Elsevier B.V. All rights reserved.

Genetics-based control of a mimo boiler-turbine plant

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

Dimeo, R.M.; Lee, K.Y.

1994-12-31

A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.

Fuzzy-TLX: using fuzzy integrals for evaluating human mental workload with NASA-Task Load indeX in laboratory and field studies.

PubMed

Mouzé-Amady, Marc; Raufaste, Eric; Prade, Henri; Meyer, Jean-Pierre

2013-01-01

The aim of this study was to assess mental workload in which various load sources must be integrated to derive reliable workload estimates. We report a new algorithm for computing weights from qualitative fuzzy integrals and apply it to the National Aeronautics and Space Administration -Task Load indeX (NASA-TLX) subscales in order to replace the standard pair-wise weighting technique (PWT). In this paper, two empirical studies were reported: (1) In a laboratory experiment, age- and task-related variables were investigated in 53 male volunteers and (2) In a field study, task- and job-related variables were studied on aircrews during 48 commercial flights. The results found in this study were as follows: (i) in the experimental setting, fuzzy estimates were highly correlated with classical (using PWT) estimates; (ii) in real work conditions, replacing PWT by automated fuzzy treatments simplified the NASA-TLX completion; (iii) the algorithm for computing fuzzy estimates provides a new classification procedure sensitive to various variables of work environments and (iv) subjective and objective measures can be used for the fuzzy aggregation of NASA-TLX subscales. NASA-TLX, a classical tool for mental workload assessment, is based on a weighted sum of ratings from six subscales. A new algorithm, which impacts on input data collection and computes weights and indexes from qualitative fuzzy integrals, is evaluated through laboratory and field studies. Pros and cons are discussed.

An algorithm to estimate building heights from Google street-view imagery using single view metrology across a representational state transfer system

NASA Astrophysics Data System (ADS)

Díaz, Elkin; Arguello, Henry

2016-05-01

Urban ecosystem studies require monitoring, controlling and planning to analyze building density, urban density, urban planning, atmospheric modeling and land use. In urban planning, there are many methods for building height estimation using optical remote sensing images. These methods however, highly depend on sun illumination and cloud-free weather. In contrast, high resolution synthetic aperture radar provides images independent from daytime and weather conditions, although, these images rely on special hardware and expensive acquisition. Most of the biggest cities around the world have been photographed by Google street view under different conditions. Thus, thousands of images from the principal streets of a city can be accessed online. The availability of this and similar rich city imagery such as StreetSide from Microsoft, represents huge opportunities in computer vision because these images can be used as input in many applications such as 3D modeling, segmentation, recognition and stereo correspondence. This paper proposes a novel algorithm to estimate building heights using public Google Street-View imagery. The objective of this work is to obtain thousands of geo-referenced images from Google Street-View using a representational state transfer system, and estimate their average height using single view metrology. Furthermore, the resulting measurements and image metadata are used to derive a layer of heights in a Google map available online. The experimental results show that the proposed algorithm can estimate an accurate average building height map of thousands of images using Google Street-View Imagery of any city.

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro

2014-01-01

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

Parameter Estimation and Sensitivity Analysis of an Urban Surface Energy Balance Parameterization at a Tropical Suburban Site

NASA Astrophysics Data System (ADS)

Harshan, S.; Roth, M.; Velasco, E.

2014-12-01

Forecasting of the urban weather and climate is of great importance as our cities become more populated and considering the combined effects of global warming and local land use changes which make urban inhabitants more vulnerable to e.g. heat waves and flash floods. In meso/global scale models, urban parameterization schemes are used to represent the urban effects. However, these schemes require a large set of input parameters related to urban morphological and thermal properties. Obtaining all these parameters through direct measurements are usually not feasible. A number of studies have reported on parameter estimation and sensitivity analysis to adjust and determine the most influential parameters for land surface schemes in non-urban areas. Similar work for urban areas is scarce, in particular studies on urban parameterization schemes in tropical cities have so far not been reported. In order to address above issues, the town energy balance (TEB) urban parameterization scheme (part of the SURFEX land surface modeling system) was subjected to a sensitivity and optimization/parameter estimation experiment at a suburban site in, tropical Singapore. The sensitivity analysis was carried out as a screening test to identify the most sensitive or influential parameters. Thereafter, an optimization/parameter estimation experiment was performed to calibrate the input parameter. The sensitivity experiment was based on the "improved Sobol's global variance decomposition method" . The analysis showed that parameters related to road, roof and soil moisture have significant influence on the performance of the model. The optimization/parameter estimation experiment was performed using the AMALGM (a multi-algorithm genetically adaptive multi-objective method) evolutionary algorithm. The experiment showed a remarkable improvement compared to the simulations using the default parameter set. The calibrated parameters from this optimization experiment can be used for further model validation studies to identify inherent deficiencies in model physics.

Self-Tuning of Design Variables for Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Lin, Chaung; Juang, Jer-Nan

2000-01-01

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Fast Fourier transform-based Retinex and alpha-rooting color image enhancement

NASA Astrophysics Data System (ADS)

Grigoryan, Artyom M.; Agaian, Sos S.; Gonzales, Analysa M.

2015-05-01

Efficiency in terms of both accuracy and speed is highly important in any system, especially when it comes to image processing. The purpose of this paper is to improve an existing implementation of multi-scale retinex (MSR) by utilizing the fast Fourier transforms (FFT) within the illumination estimation step of the algorithm to improve the speed at which Gaussian blurring filters were applied to the original input image. In addition, alpha-rooting can be used as a separate technique to achieve a sharper image in order to fuse its results with those of the retinex algorithm for the sake of achieving the best image possible as shown by the values of the considered color image enhancement measure (EMEC).

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali

2017-01-01

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.

Global Surface Solar Energy Anomalies Including El Nino and La Nina Years

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Brown, D. E.; Chandler, W. S.; DiPasquale, R. C.; Ritchey, Nancy A.; Gupta, Shashi K.; Wilber, Anne C.; Kratz, David P.; Stackhouse, Paul W.

2001-01-01

This paper synthesizes past events in an attempt to define the general magnitude, duration, and location of large surface solar anomalies over the globe. Surface solar energy values are mostly a function of solar zenith angle, cloud conditions, column atmospheric water vapor, aerosols, and surface albedo. For this study, solar and meteorological parameters for the 10-yr period July 1983 through June 1993 are used. These data were generated as part of the Release 3 Surface meteorology and Solar Energy (SSE) activity under the NASA Earth Science Enterprise (ESE) effort. Release 3 SSE uses upgraded input data and methods relative to previous releases. Cloud conditions are based on recent NASA Version-D International Satellite Cloud Climatology Project (ISCCP) global satellite radiation and cloud data. Meteorological inputs are from Version-I Goddard Earth Observing System (GEOS) reanalysis data that uses both weather station and satellite information. Aerosol transmission for different regions and seasons are for an 'average' year based on historic solar energy data from over 1000 ground sites courtesy of Natural Resources Canada (NRCan). These data are input to a new Langley Parameterized Shortwave Algorithm (LPSA) that calculates surface albedo and surface solar energy. That algorithm is an upgraded version of the 'Staylor' algorithm. Calculations are performed for a 280X280 km equal-area grid system over the globe based on 3-hourly input data. A bi-linear interpolation process is used to estimate data output values on a 1 X 1 degree grid system over the globe. Maximum anomalies are examined relative to El Nino and La Nina events in the tropical Pacific Ocean. Maximum year-to-year anomalies over the globe are provided for a 10-year period. The data may assist in the design of systems with increased reliability. It may also allow for better planning for emergency assistance during some atypical events.

Simulation and Real-Time Verification of Video Algorithms on the TI C6400 Using Simulink

DTIC Science & Technology

2004-08-20

SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12 . DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release...plot estimates over time (scrolling data) Adjust detection threshold (click mouse on graph) Monitor video capture Input video frames Captured frames 12 ...Video App: Surveillance Recording 1 2 7 3 4 9 5 6 11 SL for video Explanation of GUI 12 Target Options8 Build Process 10 13 14 15 16 M-code snippet

A robust pseudo-inverse spectral filter applied to the Earth Radiation Budget Experiment (ERBE) scanning channels

NASA Technical Reports Server (NTRS)

Avis, L. M.; Green, R. N.; Suttles, J. T.; Gupta, S. K.

1984-01-01

Computer simulations of a least squares estimator operating on the ERBE scanning channels are discussed. The estimator is designed to minimize the errors produced by nonideal spectral response to spectrally varying and uncertain radiant input. The three ERBE scanning channels cover a shortwave band a longwave band and a ""total'' band from which the pseudo inverse spectral filter estimates the radiance components in the shortwave band and a longwave band. The radiance estimator draws on instantaneous field of view (IFOV) scene type information supplied by another algorithm of the ERBE software, and on a priori probabilistic models of the responses of the scanning channels to the IFOV scene types for given Sun scene spacecraft geometry. It is found that the pseudoinverse spectral filter is stable, tolerant of errors in scene identification and in channel response modeling, and, in the absence of such errors, yields minimum variance and essentially unbiased radiance estimates.

Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform.

PubMed

Tavallali, Peyman; Razavi, Marianne; Pahlevan, Niema M

2018-01-17

In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal processing inputs to this machine learning algorithm are sensor agnostic, the presented method can accompany any medical instrument that provides a calibrated or uncalibrated carotid pressure waveform. Our results show that, for an unseen hold back test set population in the age range of 20 to 69, our model can estimate PWV with a Root-Mean-Square Error (RMSE) of 1.12 m/sec compared to the reference method. The results convey the fact that this model is a reliable surrogate of PWV. Our study also showed that estimated PWV was significantly associated with an increased risk of CVDs.

Using State Estimation Residuals to Detect Abnormal SCADA Data

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

Ma, Jian; Chen, Yousu; Huang, Zhenyu

2010-06-14

Detection of manipulated supervisory control and data acquisition (SCADA) data is critically important for the safe and secure operation of modern power systems. In this paper, a methodology of detecting manipulated SCADA data based on state estimation residuals is presented. A 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 process. The BACON algorithm is applied to detect outliers in the state estimation residuals. The IEEE 118-bus system is used asmore » a test case 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

Wolf Attack Probability: A Theoretical Security Measure in Biometric Authentication Systems

NASA Astrophysics Data System (ADS)

Une, Masashi; Otsuka, Akira; Imai, Hideki

This paper will propose a wolf attack probability (WAP) as a new measure for evaluating security of biometric authentication systems. The wolf attack is an attempt to impersonate a victim by feeding “wolves” into the system to be attacked. The “wolf” means an input value which can be falsely accepted as a match with multiple templates. WAP is defined as a maximum success probability of the wolf attack with one wolf sample. In this paper, we give a rigorous definition of the new security measure which gives strength estimation of an individual biometric authentication system against impersonation attacks. We show that if one reestimates using our WAP measure, a typical fingerprint algorithm turns out to be much weaker than theoretically estimated by Ratha et al. Moreover, we apply the wolf attack to a finger-vein-pattern based algorithm. Surprisingly, we show that there exists an extremely strong wolf which falsely matches all templates for any threshold value.

Estimation of neutron spectrum in the low-level gamma spectroscopy system using unfolding procedure

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

Knežević, D., E-mail: david.knezevic@df.uns.ac.rs; Jovančević, N.; Krmar, M.

2016-03-25

The radiation resulting from neutron interactions with Ge nuclei in active volume of HPGe detectors is one of the main concerns in low-level gamma spectroscopy measurements [1,2]. It is usually not possible to measure directly spectrum of neutrons which strike detector. This paper explore the possibility of estimation of neutron spectrum using measured activities of certain Ge(n,γ) and Ge(n,n’) reactions (obtained from low-level gamma measurements), available ENDF cross section data and unfolding procedures. In this work HPGe detector with passive shield made from commercial low background lead was used for the measurement. The most important objective of this study wasmore » to reconstruct muon induced neutron spectrum created in the shield of the HPGe detector. MAXED [3] and GRAVEL [4] algorithms for neutron spectra unfolding were used. The results of those two algorithms were compared and we analyzed the sensitivity of the unfolding procedure to the various input parameters.« less

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States.

PubMed

Vargas-Melendez, Leandro; Boada, Beatriz L; Boada, Maria Jesus L; Gauchia, Antonio; Diaz, Vicente

2017-04-29

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

PubMed Central

Vargas-Melendez, Leandro; Boada, Beatriz L.; Boada, Maria Jesus L.; Gauchia, Antonio; Diaz, Vicente

2017-01-01

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm. PMID:28468252

Bellman Ford algorithm - in Routing Information Protocol (RIP)

NASA Astrophysics Data System (ADS)

Krianto Sulaiman, Oris; Mahmud Siregar, Amir; Nasution, Khairuddin; Haramaini, Tasliyah

2018-04-01

In a large scale network need a routing that can handle a lot number of users, one of the solutions to cope with large scale network is by using a routing protocol, There are 2 types of routing protocol that is static and dynamic, Static routing is manually route input based on network admin, while dynamic routing is automatically route input formed based on existing network. Dynamic routing is efficient used to network extensively because of the input of route automatic formed, Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellman-ford algorithm owned by RIP can optimize existing networks.

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm.

PubMed

Stanford, Tyman E; Bagley, Christopher J; Solomon, Patty J

2016-01-01

Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios ( m / z ), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel 'continuous' line segment algorithm that efficiently operates over data with a transformed m / z -axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m / z scale. The automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Several of the transformations investigated were able to reduce, if not entirely remove, the peak width and peak location relationship resulting in near-optimal baseline subtraction using the automated pipeline. The proposed novel 'continuous' line segment algorithm is shown to far outperform naive sliding window algorithms with regard to the computational time required. The improvement in computational time was at least four-fold on real MALDI TOF-MS data and at least an order of magnitude on many simulated datasets. The advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.

Parameter optimization on the convergence surface of path simulations

NASA Astrophysics Data System (ADS)

Chandrasekaran, Srinivas Niranj

Computational treatments of protein conformational changes tend to focus on the trajectories themselves, despite the fact that it is the transition state structures that contain information about the barriers that impose multi-state behavior. PATH is an algorithm that computes a transition pathway between two protein crystal structures, along with the transition state structure, by minimizing the Onsager-Machlup action functional. It is rapid but depends on several unknown input parameters whose range of different values can potentially generate different transition-state structures. Transition-state structures arising from different input parameters cannot be uniquely compared with those generated by other methods. I outline modifications that I have made to the PATH algorithm that estimates these input parameters in a manner that circumvents these difficulties, and describe two complementary tests that validate the transition-state structures found by the PATH algorithm. First, I show that although the PATH algorithm and two other approaches to computing transition pathways produce different low-energy structures connecting the initial and final ground-states with the transition state, all three methods agree closely on the configurations of their transition states. Second, I show that the PATH transition states are close to the saddle points of free-energy surfaces connecting initial and final states generated by replica-exchange Discrete Molecular Dynamics simulations. I show that aromatic side-chain rearrangements create similar potential energy barriers in the transition-state structures identified by PATH for a signaling protein, a contractile protein, and an enzyme. Finally, I observed, but cannot account for, the fact that trajectories obtained for all-atom and Calpha-only simulations identify transition state structures in which the Calpha atoms are in essentially the same positions. The consistency between transition-state structures derived by different algorithms for unrelated protein systems argues that although functionally important protein conformational change trajectories are to a degree stochastic, they nonetheless pass through a well-defined transition state whose detailed structural properties can rapidly be identified using PATH. In the end, I outline the strategies that could enhance the efficiency and applicability of PATH.

A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure.

PubMed

Chen, Xinjian; Tian, Jie; Yang, Xin

2006-03-01

Coping with nonlinear distortions in fingerprint matching is a challenging task. This paper proposes a novel algorithm, normalized fuzzy similarity measure (NFSM), to deal with the nonlinear distortions. The proposed algorithm has two main steps. First, the template and input fingerprints were aligned. In this process, the local topological structure matching was introduced to improve the robustness of global alignment. Second, the method NFSM was introduced to compute the similarity between the template and input fingerprints. The proposed algorithm was evaluated on fingerprints databases of FVC2004. Experimental results confirm that NFSM is a reliable and effective algorithm for fingerprint matching with nonliner distortions. The algorithm gives considerably higher matching scores compared to conventional matching algorithms for the deformed fingerprints.

Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

NASA Astrophysics Data System (ADS)

Rothenberger, Michael J.

This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input-output measurements, and is the approach used in this dissertation. Research in the literature studies optimal current input shaping for high-order electrochemical battery models and focuses on offline laboratory cycling. While this body of research highlights improvements in identifiability through optimal input shaping, each optimal input is a function of nominal parameters, which creates a tautology. The parameter values must be known a priori to determine the optimal input for maximizing estimation speed and accuracy. The system identification literature presents multiple studies containing methods that avoid the challenges of this tautology, but these methods are absent from the battery parameter estimation domain. The gaps in the above literature are addressed in this dissertation through the following five novel and unique contributions. First, this dissertation optimizes the parameter identifiability of a thermal battery model, which Sergio Mendoza experimentally validates through a close collaboration with this dissertation's author. Second, this dissertation extends input-shaping optimization to a linear and nonlinear equivalent-circuit battery model and illustrates the substantial improvements in Fisher identifiability for a periodic optimal signal when compared against automotive benchmark cycles. Third, this dissertation presents an experimental validation study of the simulation work in the previous contribution. The estimation study shows that the automotive benchmark cycles either converge slower than the optimized cycle, or not at all for certain parameters. Fourth, this dissertation examines how automotive battery packs with additional power electronic components that dynamically route current to individual cells/modules can be used for parameter identifiability optimization. While the user and vehicle supervisory controller dictate the current demand for these packs, the optimized internal allocation of current still improves identifiability. Finally, this dissertation presents a robust Bayesian sequential input shaping optimization study to maximize the conditional Fisher information of the battery model parameters without prior knowledge of the nominal parameter set. This iterative algorithm only requires knowledge of the prior parameter distributions to converge to the optimal input trajectory.

Direct estimation of land surface albedo from VIIRS data: Algorithm improvement and preliminary validation

NASA Astrophysics Data System (ADS)

Wang, Dongdong; Liang, Shunlin; He, Tao; Yu, Yunyue

2013-11-01

surface albedo (LSA), part of the Visible Infrared Imaging Radiometer Suite (VIIRS) surface albedo environmental data record (EDR), is an essential variable regulating shortwave energy exchange between the land surface and the atmosphere. Two sub-algorithms, the dark pixel sub-algorithm (DPSA) and the bright pixel sub-algorithm (BPSA), were proposed for retrieving LSA from VIIRS data. The BPSA estimates LSA directly from VIIRS top-of-atmosphere (TOA) reflectance through simulation of atmospheric radiative transfer. Several changes have been made to improve the BPSA since the deployment of VIIRS. A database of the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) is collected and converted to bidirectional reflectance at VIIRS bands. The converted reflectance is then used as input to the atmospheric radiative transfer model to generate a look-up table (LUT) of regression coefficients with consideration of surface BRDF. Before its implementation in the operational system, the new BPSA is tested on the local infrastructure. The incorporation of the surface BRDF improves the accuracy of LSA estimation and reduces the temporal variation of LSA over stable surfaces. VIIRS LSA retrievals agree well with the MODIS albedo products. Comparison with field measurements at seven Surface Radiation (SURFRAD) Network sites shows that VIIRS LSA retrieved from the LUT with surface BRDF has an R2 value of 0.80 and root mean square error of 0.049, better than MODIS albedo products. The VIIRS results have a slight negative bias of 0.004, whereas the MODIS albedo is underestimated with a larger negative bias of 0.026.

Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

NASA Astrophysics Data System (ADS)

Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao

2017-03-01

Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

USGS Publications Warehouse

Chen, Mingshi; Senay, Gabriel B.; Singh, Ramesh K.; Verdin, James P.

2016-01-01

Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET (ETo); and parameters, differential temperature (dT), and maximum ET scalar (Kmax), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables (ETo and LST) and two key parameters (Kmax and dT).

Area collapse algorithm computing new curve of 2D geometric objects

NASA Astrophysics Data System (ADS)

Buczek, Michał Mateusz

2017-06-01

The processing of cartographic data demands human involvement. Up-to-date algorithms try to automate a part of this process. The goal is to obtain a digital model, or additional information about shape and topology of input geometric objects. A topological skeleton is one of the most important tools in the branch of science called shape analysis. It represents topological and geometrical characteristics of input data. Its plot depends on using algorithms such as medial axis, skeletonization, erosion, thinning, area collapse and many others. Area collapse, also known as dimension change, replaces input data with lower-dimensional geometric objects like, for example, a polygon with a polygonal chain, a line segment with a point. The goal of this paper is to introduce a new algorithm for the automatic calculation of polygonal chains representing a 2D polygon. The output is entirely contained within the area of the input polygon, and it has a linear plot without branches. The computational process is automatic and repeatable. The requirements of input data are discussed. The author analyzes results based on the method of computing ends of output polygonal chains. Additional methods to improve results are explored. The algorithm was tested on real-world cartographic data received from BDOT/GESUT databases, and on point clouds from laser scanning. An implementation for computing hatching of embankment is described.

Design, Fabrication, and Modeling of a Novel Dual-Axis Control Input PZT Gyroscope.

PubMed

Chang, Cheng-Yang; Chen, Tsung-Lin

2017-10-31

Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control inputs. The aim of this study was to design a piezoelectric gyroscope equipped with a dual-axis control input so that researchers may experimentally verify those control algorithms in future. Designing a piezoelectric gyroscope with a dual-axis control input is more difficult than designing a conventional gyroscope because the control input must be effective over a broad frequency range to compensate for imperfections, and the multiple mode shapes in flexural deformations complicate the relation between flexural deformation and the proof mass position. This study solved these problems by using a lead zirconate titanate (PZT) material, introducing additional electrodes for shielding, developing an optimal electrode pattern, and performing calibrations of undesired couplings. The results indicated that the fabricated device could be operated at 5.5±1 kHz to perform dual-axis actuations and position measurements. The calibration of the fabricated device was completed by system identifications of a new dynamic model including gyroscopic motions, electromechanical coupling, mechanical coupling, electrostatic coupling, and capacitive output impedance. Finally, without the assistance of control algorithms, the "open loop sensitivity" of the fabricated gyroscope was 1.82 μV/deg/s with a nonlinearity of 9.5% full-scale output. This sensitivity is comparable with those of other PZT gyroscopes with single-axis control inputs.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

Sparse Reconstruction of Electric Fields from Radial Magnetic Data

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

Yeates, Anthony R.

2017-02-10

Accurate estimates of the horizontal electric field on the Sun’s visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal electric field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday’s law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of nonzero magnetic field, as would be expected from Ohm’s law. Our new method generates instead a localizedmore » solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux—usually arising from data assimilation—then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.« less

Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

NASA Astrophysics Data System (ADS)

Maginnis, P. A.; West, M.; Dullerud, G. E.

2016-10-01

We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a ;black-box;, i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

TOMS UV Algorithm: Problems and Enhancements. 2

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay; Herman, Jay; Bhartia, P. K.; Seftor, Colin; Arola, Antti; Kaurola, Jussi; Kroskinen, Lasse; Kalliskota, S.; Taalas, Petteri; Geogdzhaev, I.

2002-01-01

Satellite instruments provide global maps of surface ultraviolet (UV) irradiance by combining backscattered radiance measurements with radiative transfer models. The models are limited by uncertainties in input parameters of the atmosphere and the surface. We evaluate the effects of possible enhancements of the current Total Ozone Mapping Spectrometer (TOMS) surface UV irradiance algorithm focusing on effects of diurnal variation of cloudiness and improved treatment of snow/ice. The emphasis is on comparison between the results of the current (version 1) TOMS UV algorithm and each of the changes proposed. We evaluate different approaches for improved treatment of pixel average cloud attenuation, with and without snow/ice on the ground. In addition to treating clouds based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate attenuation of the incident UV irradiance throughout the day. A new method is proposed to obtain a more realistic treatment of snow covered terrain. The method is based on a statistical relation between UV reflectivity and snow depth. The new method reduced the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. The improved (version 2) algorithm will be applied to re-process the existing TOMS UV data record (since 1978) and to the future satellite sensors (e.g., Quik/TOMS, GOME, OMI on EOS/Aura and Triana/EPIC).

Blind separation of overlapping partials in harmonic musical notes using amplitude and phase reconstruction

NASA Astrophysics Data System (ADS)

de León, Jesús Ponce; Beltrán, José Ramón

2012-12-01

In this study, a new method of blind audio source separation (BASS) of monaural musical harmonic notes is presented. The input (mixed notes) signal is processed using a flexible analysis and synthesis algorithm (complex wavelet additive synthesis, CWAS), which is based on the complex continuous wavelet transform. When the harmonics from two or more sources overlap in a certain frequency band (or group of bands), a new technique based on amplitude similarity criteria is used to obtain an approximation to the original partial information. The aim is to show that the CWAS algorithm can be a powerful tool in BASS. Compared with other existing techniques, the main advantages of the proposed algorithm are its accuracy in the instantaneous phase estimation, its synthesis capability and that the only input information needed is the mixed signal itself. A set of synthetically mixed monaural isolated notes have been analyzed using this method, in eight different experiments: the same instrument playing two notes within the same octave and two harmonically related notes (5th and 12th intervals), two different musical instruments playing 5th and 12th intervals, two different instruments playing non-harmonic notes, major and minor chords played by the same musical instrument, three different instruments playing non-harmonically related notes and finally the mixture of a inharmonic instrument (piano) and one harmonic instrument. The results obtained show the strength of the technique.

Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU).

PubMed

Antila, Kari; Nieminen, Heikki J; Sequeiros, Roberto Blanco; Ehnholm, Gösta

2014-07-01

Up to 25% of women suffer from uterine fibroids (UF) that cause infertility, pain, and discomfort. MR-guided high intensity focused ultrasound (MR-HIFU) is an emerging technique for noninvasive, computer-guided thermal ablation of UFs. The volume of induced necrosis is a predictor of the success of the treatment. However, accurate volume assessment by hand can be time consuming, and quick tools produce biased results. Therefore, fast and reliable tools are required in order to estimate the technical treatment outcome during the therapy event so as to predict symptom relief. A novel technique has been developed for the segmentation and volume assessment of the treated region. Conventional algorithms typically require user interaction ora priori knowledge of the target. The developed algorithm exploits the treatment plan, the coordinates of the intended ablation, for fully automatic segmentation with no user input. A good similarity to an expert-segmented manual reference was achieved (Dice similarity coefficient = 0.880 ± 0.074). The average automatic segmentation time was 1.6 ± 0.7 min per patient against an order of tens of minutes when done manually. The results suggest that the segmentation algorithm developed, requiring no user-input, provides a feasible and practical approach for the automatic evaluation of the boundary and volume of the HIFU-treated region.

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

SU-E-J-92: Validating Dose Uncertainty Estimates Produced by AUTODIRECT, An Automated Program to Evaluate Deformable Image Registration Accuracy

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

Kim, H; Chen, J; Pouliot, J

2015-06-15

Purpose: Deformable image registration (DIR) is a powerful tool with the potential to deformably map dose from one computed-tomography (CT) image to another. Errors in the DIR, however, will produce errors in the transferred dose distribution. We have proposed a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), which predicts voxel-specific dose mapping errors on a patient-by-patient basis. This work validates the effectiveness of AUTODIRECT to predict dose mapping errors with virtual and physical phantom datasets. Methods: AUTODIRECT requires 4 inputs: moving and fixed CT images and two noise scans of a water phantom (for noise characterization). Then,more » AUTODIRECT uses algorithms to generate test deformations and applies them to the moving and fixed images (along with processing) to digitally create sets of test images, with known ground-truth deformations that are similar to the actual one. The clinical DIR algorithm is then applied to these test image sets (currently 4) . From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. This work compares these uncertainty estimates to the actual errors made by the Velocity Deformable Multi Pass algorithm on 11 virtual and 1 physical phantom datasets. Results: For 11 of the 12 tests, the predicted dose error distributions from AUTODIRECT are well matched to the actual error distributions within 1–6% for 10 virtual phantoms, and 9% for the physical phantom. For one of the cases though, the predictions underestimated the errors in the tail of the distribution. Conclusion: Overall, the AUTODIRECT algorithm performed well on the 12 phantom cases for Velocity and was shown to generate accurate estimates of dose warping uncertainty. AUTODIRECT is able to automatically generate patient-, organ- , and voxel-specific DIR uncertainty estimates. This ability would be useful for patient-specific DIR quality assurance.« less

Nonlinear Algorithms for Channel Equalization and Map Symbol Detection.

NASA Astrophysics Data System (ADS)

Giridhar, K.

The transfer of information through a communication medium invariably results in various kinds of distortion to the transmitted signal. In this dissertation, a feed -forward neural network-based equalizer, and a family of maximum a posteriori (MAP) symbol detectors are proposed for signal recovery in the presence of intersymbol interference (ISI) and additive white Gaussian noise. The proposed neural network-based equalizer employs a novel bit-mapping strategy to handle multilevel data signals in an equivalent bipolar representation. It uses a training procedure to learn the channel characteristics, and at the end of training, the multilevel symbols are recovered from the corresponding inverse bit-mapping. When the channel characteristics are unknown and no training sequences are available, blind estimation of the channel (or its inverse) and simultaneous data recovery is required. Convergence properties of several existing Bussgang-type blind equalization algorithms are studied through computer simulations, and a unique gain independent approach is used to obtain a fair comparison of their rates of convergence. Although simple to implement, the slow convergence of these Bussgang-type blind equalizers make them unsuitable for many high data-rate applications. Rapidly converging blind algorithms based on the principle of MAP symbol-by -symbol detection are proposed, which adaptively estimate the channel impulse response (CIR) and simultaneously decode the received data sequence. Assuming a linear and Gaussian measurement model, the near-optimal blind MAP symbol detector (MAPSD) consists of a parallel bank of conditional Kalman channel estimators, where the conditioning is done on each possible data subsequence that can convolve with the CIR. This algorithm is also extended to the recovery of convolutionally encoded waveforms in the presence of ISI. Since the complexity of the MAPSD algorithm increases exponentially with the length of the assumed CIR, a suboptimal decision-feedback mechanism is introduced to truncate the channel memory "seen" by the MAPSD section. Also, simpler gradient-based updates for the channel estimates, and a metric pruning technique are used to further reduce the MAPSD complexity. Spatial diversity MAP combiners are developed to enhance the error rate performance and combat channel fading. As a first application of the MAPSD algorithm, dual-mode recovery techniques for TDMA (time-division multiple access) mobile radio signals are presented. Combined estimation of the symbol timing and the multipath parameters is proposed, using an auxiliary extended Kalman filter during the training cycle, and then tracking of the fading parameters is performed during the data cycle using the blind MAPSD algorithm. For the second application, a single-input receiver is employed to jointly recover cochannel narrowband signals. Assuming known channels, this two-stage joint MAPSD (JMAPSD) algorithm is compared to the optimal joint maximum likelihood sequence estimator, and to the joint decision-feedback detector. A blind MAPSD algorithm for the joint recovery of cochannel signals is also presented. Computer simulation results are provided to quantify the performance of the various algorithms proposed in this dissertation.

Modified fast frequency acquisition via adaptive least squares algorithm

NASA Technical Reports Server (NTRS)

Kumar, Rajendra (Inventor)

1992-01-01

A method and the associated apparatus for estimating the amplitude, frequency, and phase of a signal of interest are presented. The method comprises the following steps: (1) inputting the signal of interest; (2) generating a reference signal with adjustable amplitude, frequency and phase at an output thereof; (3) mixing the signal of interest with the reference signal and a signal 90 deg out of phase with the reference signal to provide a pair of quadrature sample signals comprising respectively a difference between the signal of interest and the reference signal and a difference between the signal of interest and the signal 90 deg out of phase with the reference signal; (4) using the pair of quadrature sample signals to compute estimates of the amplitude, frequency, and phase of an error signal comprising the difference between the signal of interest and the reference signal employing a least squares estimation; (5) adjusting the amplitude, frequency, and phase of the reference signal from the numerically controlled oscillator in a manner which drives the error signal towards zero; and (6) outputting the estimates of the amplitude, frequency, and phase of the error signal in combination with the reference signal to produce a best estimate of the amplitude, frequency, and phase of the signal of interest. The preferred method includes the step of providing the error signal as a real time confidence measure as to the accuracy of the estimates wherein the closer the error signal is to zero, the higher the probability that the estimates are accurate. A matrix in the estimation algorithm provides an estimate of the variance of the estimation error.

Does Sentinel multi sensor data offer synergy in Improving Accuracy of Aboveground Biomass Estimate of Dense Tropical Forest? - Utility of Decision Tree Based Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Ghosh, S. M.; Behera, M. D.

2017-12-01

Forest aboveground biomass (AGB) is an important factor for preparation of global policy making decisions to tackle the impact of climate change. Several previous studies has concluded that remote sensing methods are more suitable for estimating forest biomass on regional scale. Among all available remote sensing data and methods, Synthetic Aperture Radar (SAR) data in combination with decision tree based machine learning algorithms has shown better promise in estimating higher biomass values. There aren't many studies done for biomass estimation of dense Indian tropical forests with high biomass density. In this study aboveground biomass was estimated for two major tree species, Sal (Shorea robusta) and Teak (Tectona grandis), of Katerniaghat Wildlife Sanctuary, a tropical forest situated in northern India. Biomass was estimated by combining C-band SAR data from Sentinel-1A satellite, vegetation indices produced using Sentinel-2A data and ground inventory plots. Along with SAR backscatter value, SAR texture images were also used as input as earlier studies had found that image texture has a correlation with vegetation biomass. Decision tree based nonlinear machine learning algorithms were used in place of parametric regression models for establishing relationship between fields measured values and remotely sensed parameters. Using random forest model with a combination of vegetation indices with SAR backscatter as predictor variables shows best result for Sal forest, with a coefficient of determination value of 0.71 and a RMSE value of 105.027 t/ha. In teak forest also best result can be found in the same combination but for stochastic gradient boosted model with a coefficient of determination value of 0.6 and a RMSE value of 79.45 t/ha. These results are mostly better than the results of other studies done for similar kind of forests. This study shows that Sentinel series satellite data has exceptional capabilities in estimating dense forest AGB and machine learning algorithms are better means to do so than parametric regression models.

AATSR land surface temperature product algorithm verification over a WATERMED site

NASA Astrophysics Data System (ADS)

Noyes, E. J.; Sòria, G.; Sobrino, J. A.; Remedios, J. J.; Llewellyn-Jones, D. T.; Corlett, G. K.

A new operational Land Surface Temperature (LST) product generated from data acquired by the Advanced Along-Track Scanning Radiometer (AATSR) provides the opportunity to measure LST on a global scale with a spatial resolution of 1 km2. The target accuracy of the product, which utilises nadir data from the AATSR thermal channels at 11 and 12 μm, is 2.5 K for daytime retrievals and 1.0 K at night. We present the results of an experiment where the performance of the algorithm has been assessed for one daytime and one night time overpass occurring over the WATERMED field site near Marrakech, Morocco, on 05 March 2003. Top of atmosphere (TOA) brightness temperatures (BTs) are simulated for 12 pixels from each overpass using a radiative transfer model, with the LST product and independent emissivity values and atmospheric data as inputs. We have estimated the error in the LST product over this biome for this set of conditions by applying the operational AATSR LST retrieval algorithm to the modelled BTs and comparing the results with the original AATSR LSTs input into the model. An average bias of -1.00 K (standard deviation 0.07 K) for the daytime data, and -1.74 K (standard deviation 0.02 K) for the night time data is obtained, which indicates that the algorithm is yielding an LST that is too cold under these conditions. While these results are within specification for daytime retrievals, this suggests that the target accuracy of 1.0 K at night is not being met within this biome.

Estimating wheat and maize daily evapotranspiration using artificial neural network

NASA Astrophysics Data System (ADS)

Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

2018-02-01

In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.

A Stochastic Total Least Squares Solution of Adaptive Filtering Problem

PubMed Central

Ahmad, Noor Atinah

2014-01-01

An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs. PMID:24688412

Active Control of Wind Tunnel Noise

NASA Technical Reports Server (NTRS)

Hollis, Patrick (Principal Investigator)

1991-01-01

The need for an adaptive active control system was realized, since a wind tunnel is subjected to variations in air velocity, temperature, air turbulence, and some other factors such as nonlinearity. Among many adaptive algorithms, the Least Mean Squares (LMS) algorithm, which is the simplest one, has been used in an Active Noise Control (ANC) system by some researchers. However, Eriksson's results, Eriksson (1985), showed instability in the ANC system with an ER filter for random noise input. The Restricted Least Squares (RLS) algorithm, although computationally more complex than the LMS algorithm, has better convergence and stability properties. The ANC system in the present work was simulated by using an FIR filter with an RLS algorithm for different inputs and for a number of plant models. Simulation results for the ANC system with acoustic feedback showed better robustness when used with the RLS algorithm than with the LMS algorithm for all types of inputs. Overall attenuation in the frequency domain was better in the case of the RLS adaptive algorithm. Simulation results with a more realistic plant model and an RLS adaptive algorithm showed a slower convergence rate than the case with an acoustic plant as a delay plant. However, the attenuation properties were satisfactory for the simulated system with the modified plant. The effect of filter length on the rate of convergence and attenuation was studied. It was found that the rate of convergence decreases with increase in filter length, whereas the attenuation increases with increase in filter length. The final design of the ANC system was simulated and found to have a reasonable convergence rate and good attenuation properties for an input containing discrete frequencies and random noise.

An Application of UAV Attitude Estimation Using a Low-Cost Inertial Navigation System

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Quach, Cuong Chi; Vazquez, Sixto L.; Hogge, Edward F.; Hill, Boyd L.

2013-01-01

Unmanned Aerial Vehicles (UAV) are playing an increasing role in aviation. Various methods exist for the computation of UAV attitude based on low cost microelectromechanical systems (MEMS) and Global Positioning System (GPS) receivers. There has been a recent increase in UAV autonomy as sensors are becoming more compact and onboard processing power has increased significantly. Correct UAV attitude estimation will play a critical role in navigation and separation assurance as UAVs share airspace with civil air traffic. This paper describes attitude estimation derived by post-processing data from a small low cost Inertial Navigation System (INS) recorded during the flight of a subscale commercial off the shelf (COTS) UAV. Two discrete time attitude estimation schemes are presented here in detail. The first is an adaptation of the Kalman Filter to accommodate nonlinear systems, the Extended Kalman Filter (EKF). The EKF returns quaternion estimates of the UAV attitude based on MEMS gyro, magnetometer, accelerometer, and pitot tube inputs. The second scheme is the complementary filter which is a simpler algorithm that splits the sensor frequency spectrum based on noise characteristics. The necessity to correct both filters for gravity measurement errors during turning maneuvers is demonstrated. It is shown that the proposed algorithms may be used to estimate UAV attitude. The effects of vibration on sensor measurements are discussed. Heuristic tuning comments pertaining to sensor filtering and gain selection to achieve acceptable performance during flight are given. Comparisons of attitude estimation performance are made between the EKF and the complementary filter.

Fully Self-Contained Vision-Aided Navigation and Landing of a Micro Air Vehicle Independent from External Sensor Inputs

NASA Technical Reports Server (NTRS)

Brockers, Roland; Susca, Sara; Zhu, David; Matthies, Larry

2012-01-01

Direct-lift micro air vehicles have important applications in reconnaissance. In order to conduct persistent surveillance in urban environments, it is essential that these systems can perform autonomous landing maneuvers on elevated surfaces that provide high vantage points without the help of any external sensor and with a fully contained on-board software solution. In this paper, we present a micro air vehicle that uses vision feedback from a single down looking camera to navigate autonomously and detect an elevated landing platform as a surrogate for a roof top. Our method requires no special preparation (labels or markers) of the landing location. Rather, leveraging the planar character of urban structure, the landing platform detection system uses a planar homography decomposition to detect landing targets and produce approach waypoints for autonomous landing. The vehicle control algorithm uses a Kalman filter based approach for pose estimation to fuse visual SLAM (PTAM) position estimates with IMU data to correct for high latency SLAM inputs and to increase the position estimate update rate in order to improve control stability. Scale recovery is achieved using inputs from a sonar altimeter. In experimental runs, we demonstrate a real-time implementation running on-board a micro aerial vehicle that is fully self-contained and independent from any external sensor information. With this method, the vehicle is able to search autonomously for a landing location and perform precision landing maneuvers on the detected targets.

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm or conceivably lead to a radically different approach to earthquake prediction.

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

Solar Irradiance from GOES Albedo performance in a Hydrologic Model Simulation of Snowmelt Runoff

NASA Astrophysics Data System (ADS)

Sumargo, E.; Cayan, D. R.; McGurk, B. J.

2015-12-01

In many hydrologic modeling applications, solar radiation has been parameterized using commonly available measures, such as the daily temperature range, due to scarce in situ solar radiation measurement network. However, these parameterized estimates often produce significant biases. Here we test hourly solar irradiance derived from the Geostationary Operational Environmental Satellite (GOES) visible albedo product, using several established algorithms. Focusing on the Sierra Nevada and White Mountain in California, we compared the GOES irradiance and that from a traditional temperature-based algorithm with incoming irradiance from pyranometers at 19 stations. The GOES based estimates yielded 21-27% reduction in root-mean-squared error (average over 19 sites). The derived irradiance is then prescribed as an input to Precipitation-Runoff Modeling System (PRMS). We constrain our experiment to the Tuolumne River watershed and focus our attention on the winter and spring of 1996-2014. A root-mean-squared error reduction of 2-6% in daily inflow to Hetch Hetchy at the lower end of the Tuolumne catchment was achieved by incorporating the insolation estimates at only 8 out of 280 Hydrologic Response Units (HRUs) within the basin. Our ongoing work endeavors to apply satellite-derived irradiance at each individual HRU.

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH.

PubMed

Volk, Jochen; Herrmann, Torsten; Wüthrich, Kurt

2008-07-01

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness.

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

NASA Astrophysics Data System (ADS)

Wang, Yunhua; DeBrunner, Linda; DeBrunner, Victor; Zhou, Dayong

2008-12-01

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

Computer program documentation: ISOCLS iterative self-organizing clustering program, program C094

NASA Technical Reports Server (NTRS)

Minter, R. T. (Principal Investigator)

1972-01-01

The author has identified the following significant results. This program implements an algorithm which, ideally, sorts a given set of multivariate data points into similar groups or clusters. The program is intended for use in the evaluation of multispectral scanner data; however, the algorithm could be used for other data types as well. The user may specify a set of initial estimated cluster means to begin the procedure, or he may begin with the assumption that all the data belongs to one cluster. The procedure is initiatized by assigning each data point to the nearest (in absolute distance) cluster mean. If no initial cluster means were input, all of the data is assigned to cluster 1. The means and standard deviations are calculated for each cluster.

Molecular system identification for enzyme directed evolution and design

NASA Astrophysics Data System (ADS)

Guan, Xiangying; Chakrabarti, Raj

2017-09-01

The rational design of chemical catalysts requires methods for the measurement of free energy differences in the catalytic mechanism for any given catalyst Hamiltonian. The scope of experimental learning algorithms that can be applied to catalyst design would also be expanded by the availability of such methods. Methods for catalyst characterization typically either estimate apparent kinetic parameters that do not necessarily correspond to free energy differences in the catalytic mechanism or measure individual free energy differences that are not sufficient for establishing the relationship between the potential energy surface and catalytic activity. Moreover, in order to enhance the duty cycle of catalyst design, statistically efficient methods for the estimation of the complete set of free energy differences relevant to the catalytic activity based on high-throughput measurements are preferred. In this paper, we present a theoretical and algorithmic system identification framework for the optimal estimation of free energy differences in solution phase catalysts, with a focus on one- and two-substrate enzymes. This framework, which can be automated using programmable logic, prescribes a choice of feasible experimental measurements and manipulated input variables that identify the complete set of free energy differences relevant to the catalytic activity and minimize the uncertainty in these free energy estimates for each successive Hamiltonian design. The framework also employs decision-theoretic logic to determine when model reduction can be applied to improve the duty cycle of high-throughput catalyst design. Automation of the algorithm using fluidic control systems is proposed, and applications of the framework to the problem of enzyme design are discussed.

Estimating parameters with pre-specified accuracies in distributed parameter systems using optimal experiment design

NASA Astrophysics Data System (ADS)

Potters, M. G.; Bombois, X.; Mansoori, M.; Hof, Paul M. J. Van den

2016-08-01

Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the optimal input signal, and optimal sensor and actuator locations. Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments. We find good correspondence between numerical and theoretical results.

Modification of a rainfall-runoff model for distributed modeling in a GIS and its validation

NASA Astrophysics Data System (ADS)

Nyabeze, W. R.

A rainfall-runoff model, which can be inter-faced with a Geographical Information System (GIS) to integrate definition, measurement, calculating parameter values for spatial features, presents considerable advantages. The modification of the GWBasic Wits Rainfall-Runoff Erosion Model (GWBRafler) to enable parameter value estimation in a GIS (GISRafler) is presented in this paper. Algorithms are applied to estimate parameter values reducing the number of input parameters and the effort to populate them. The use of a GIS makes the relationship between parameter estimates and cover characteristics more evident. This paper has been produced as part of research to generalize the GWBRafler on a spatially distributed basis. Modular data structures are assumed and parameter values are weighted relative to the module area and centroid properties. Modifications to the GWBRafler enable better estimation of low flows, which are typical in drought conditions.

RGB-D SLAM Combining Visual Odometry and Extended Information Filter

PubMed Central

Zhang, Heng; Liu, Yanli; Tan, Jindong; Xiong, Naixue

2015-01-01

In this paper, we present a novel RGB-D SLAM system based on visual odometry and an extended information filter, which does not require any other sensors or odometry. In contrast to the graph optimization approaches, this is more suitable for online applications. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, we use a novel descriptor called binary robust appearance and normals descriptor (BRAND) to extract features from the RGB-D frame and use them as landmarks. Furthermore, considering both the 3D positions and the BRAND descriptors of the landmarks, our observation model avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all possible associations. Experimental validation is provided, which compares the proposed RGB-D SLAM algorithm with just RGB-D visual odometry and a graph-based RGB-D SLAM algorithm using the publicly-available RGB-D dataset. The results of the experiments demonstrate that our system is quicker than the graph-based RGB-D SLAM algorithm. PMID:26263990

Retrieving the Height of Smoke and Dust Aerosols by Synergistic Use of VIIRS, OMPS, and CALIOP Observations

NASA Technical Reports Server (NTRS)

Lee, Jaehwa; Hsu, N. Christina; Bettenhausen, Corey; Sayer, Andrew M.; Seftor, Colin J.; Jeong, Myeong-Jae

2015-01-01

Aerosol Single scattering albedo and Height Estimation (ASHE) algorithm was first introduced in Jeong and Hsu (2008) to provide aerosol layer height as well as single scattering albedo (SSA) for biomass burning smoke aerosols. One of the advantages of this algorithm was that the aerosol layer height can be retrieved over broad areas, which had not been available from lidar observations only. The algorithm utilized aerosol properties from three different satellite sensors, i.e., aerosol optical depth (AOD) and Ångström exponent (AE) from Moderate Resolution Imaging Spectroradiometer (MODIS), UV aerosol index (UVAI) from Ozone Monitoring Instrument (OMI), and aerosol layer height from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Here, we extend the application of the algorithm to Visible Infrared Imaging Radiometer Suite (VIIRS) and Ozone Mapping and Profiler Suite (OMPS) data. We also now include dust layers as well as smoke. Other updates include improvements in retrieving the AOD of nonspherical dust from VIIRS, better determination of the aerosol layer height from CALIOP, and more realistic input aerosol profiles in the forward model for better accuracy.

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

PubMed

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

A geometric approach to identify cavities in particle systems

NASA Astrophysics Data System (ADS)

Voyiatzis, Evangelos; Böhm, Michael C.; Müller-Plathe, Florian

2015-11-01

The implementation of a geometric algorithm to identify cavities in particle systems in an open-source python program is presented. The algorithm makes use of the Delaunay space tessellation. The present python software is based on platform-independent tools, leading to a portable program. Its successful execution provides information concerning the accessible volume fraction of the system, the size and shape of the cavities and the group of atoms forming each of them. The program can be easily incorporated into the LAMMPS software. An advantage of the present algorithm is that no a priori assumption on the cavity shape has to be made. As an example, the cavity size and shape distributions in a polyethylene melt system are presented for three spherical probe particles. This paper serves also as an introductory manual to the script. It summarizes the algorithm, its implementation, the required user-defined parameters as well as the format of the input and output files. Additionally, we demonstrate possible applications of our approach and compare its capability with the ones of well documented cavity size estimators.

An interaction algorithm for prediction of mean and fluctuating velocities in two-dimensional aerodynamic wake flows

NASA Technical Reports Server (NTRS)

Baker, A. J.; Orzechowski, J. A.

1980-01-01

A theoretical analysis is presented yielding sets of partial differential equations for determination of turbulent aerodynamic flowfields in the vicinity of an airfoil trailing edge. A four phase interaction algorithm is derived to complete the analysis. Following input, the first computational phase is an elementary viscous corrected two dimensional potential flow solution yielding an estimate of the inviscid-flow induced pressure distribution. Phase C involves solution of the turbulent two dimensional boundary layer equations over the trailing edge, with transition to a two dimensional parabolic Navier-Stokes equation system describing the near-wake merging of the upper and lower surface boundary layers. An iteration provides refinement of the potential flow induced pressure coupling to the viscous flow solutions. The final phase is a complete two dimensional Navier-Stokes analysis of the wake flow in the vicinity of a blunt-bases airfoil. A finite element numerical algorithm is presented which is applicable to solution of all partial differential equation sets of inviscid-viscous aerodynamic interaction algorithm. Numerical results are discussed.

ROMUSE 2.0 User Manual

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

Khuwaileh, Bassam; Turinsky, Paul; Williams, Brian J.

2016-10-04

ROMUSE (Reduced Order Modeling Based Uncertainty/Sensitivity Estimator) is an effort within the Consortium for Advanced Simulation of Light water reactors (CASL) to provide an analysis tool to be used in conjunction with reactor core simulators, especially the Virtual Environment for Reactor Applications (VERA). ROMUSE is written in C++ and is currently capable of performing various types of parameters perturbations, uncertainty quantification, surrogate models construction and subspace analysis. Version 2.0 has the capability to interface with DAKOTA which gives ROMUSE access to the various algorithms implemented within DAKOTA. ROMUSE is mainly designed to interface with VERA and the Comprehensive Modeling andmore » Simulation Suite for Nuclear Safety Analysis and Design (SCALE) [1,2,3], however, ROMUSE can interface with any general model (e.g. python and matlab) with Input/Output (I/O) format that follows the Hierarchical Data Format 5 (HDF5). In this brief user manual, the use of ROMUSE will be overviewed and example problems will be presented and briefly discussed. The algorithms provided here range from algorithms inspired by those discussed in Ref.[4] to nuclear-specific algorithms discussed in Ref. [3].« less

Estimation Filter for Alignment of the Spitzer Space Telescope

NASA Technical Reports Server (NTRS)

Bayard, David

2007-01-01

A document presents a summary of an onboard estimation algorithm now being used to calibrate the alignment of the Spitzer Space Telescope (formerly known as the Space Infrared Telescope Facility). The algorithm, denoted the S2P calibration filter, recursively generates estimates of the alignment angles between a telescope reference frame and a star-tracker reference frame. At several discrete times during the day, the filter accepts, as input, attitude estimates from the star tracker and observations taken by the Pointing Control Reference Sensor (a sensor in the field of view of the telescope). The output of the filter is a calibrated quaternion that represents the best current mean-square estimate of the alignment angles between the telescope and the star tracker. The S2P calibration filter incorporates a Kalman filter that tracks six states - two for each of three orthogonal coordinate axes. Although, in principle, one state per axis is sufficient, the use of two states per axis makes it possible to model both short- and long-term behaviors. Specifically, the filter properly models transient learning, characteristic times and bounds of thermomechanical drift, and long-term steady-state statistics, whether calibration measurements are taken frequently or infrequently. These properties ensure that the S2P filter performance is optimal over a broad range of flight conditions, and can be confidently run autonomously over several years of in-flight operation without human intervention.

A PCT algorithm for discontinuation of antibiotic therapy is a cost-effective way to reduce antibiotic exposure in adult intensive care patients with sepsis.

PubMed

Kip, Michelle M A; Kusters, Ron; IJzerman, Maarten J; Steuten, Lotte M G

2015-01-01

Procalcitonin (PCT) is a specific marker for differentiating bacterial from non-infective causes of inflammation. It can be used to guide initiation and duration of antibiotic therapy in intensive care unit (ICU) patients with suspected sepsis, and might reduce the duration of hospital stay. Limiting antibiotic treatment duration is highly important because antibiotic over-use may cause patient harm, prolonged hospital stay, and resistance development. Several systematic reviews show that a PCT algorithm for antibiotic discontinuation is safe, but upfront investment required for PCT remains an important barrier against implementation. The current study investigates to what extent this PCT algorithm is a cost-effective use of scarce healthcare resources in ICU patients with sepsis compared to current practice. A decision tree was developed to estimate the health economic consequences of the PCT algorithm for antibiotic discontinuation from a Dutch hospital perspective. Input data were obtained from a systematic literature review. When necessary, additional information was gathered from open interviews with clinical chemists and intensivists. The primary effectiveness measure is defined as the number of antibiotic days, and cost-effectiveness is expressed as incremental costs per antibiotic day avoided. The PCT algorithm for antibiotic discontinuation is expected to reduce hospital spending by circa € 3503 per patient, indicating savings of 9.2%. Savings are mainly due to reductions in length of hospital stay, number of blood cultures performed, and, importantly, days on antibiotic therapy. Probabilistic and one-way sensitivity analyses showed the model outcome to be robust against changes in model inputs. Proven safe, a PCT algorithm for antibiotic discontinuation is a cost-effective means of reducing antibiotic exposure in adult ICU patients with sepsis, compared to current practice. Additional resources required for PCT are more than offset by downstream cost savings. This finding is highly important given the aim of preventing widespread antibiotic resistance.

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

The purpose of the computer program is to generate system matrices that model data acquisition process in dynamic single photon emission computed tomography (SPECT). The application is for the reconstruction of dynamic data from projection measurements that provide the time evolution of activity uptake and wash out in an organ of interest. The measurement of the time activity in the blood and organ tissue provide time-activity curves (TACs) that are used to estimate kinetic parameters. The program provides a correct model of the in vivo spatial and temporal distribution of radioactive in organs. The model accounts for the attenuation ofmore » the internal emitting radioactivity, it accounts for the vary point response of the collimators, and correctly models the time variation of the activity in the organs. One important application where the software is being used in a measuring the arterial input function (AIF) in a dynamic SPECT study where the data are acquired from a slow camera rotation. Measurement of the arterial input function (AIF) is essential to deriving quantitative estimates of regional myocardial blood flow using kinetic models. A study was performed to evaluate whether a slowly rotating SPECT system could provide accurate AIF's for myocardial perfusion imaging (MPI). Methods: Dynamic cardiac SPECT was first performed in human subjects at rest using a Phillips Precedence SPECT/CT scanner. Dynamic measurements of Tc-99m-tetrofosmin in the myocardium were obtained using an infusion time of 2 minutes. Blood input, myocardium tissue and liver TACs were estimated using spatiotemporal splines. These were fit to a one-compartment perfusion model to obtain wash-in rate parameters K1. Results: The spatiotemporal 4D ML-EM reconstructions gave more accurate reconstructions that did standard frame-by-frame 3D ML-EM reconstructions. From additional computer simulations and phantom studies, it was determined that a 1 minute infusion with a SPECT system rotation speed providing 180 degrees of projection data every 54s can produce measurements of blood pool and myocardial TACs. This has important application in the circulation of coronary flow reserve using rest/stress dynamic cardiac SPECT. They system matrices are used in maximum likelihood and maximum a posterior formulations in estimation theory where through iterative algorithms (conjugate gradient, expectation maximization, or maximum a posteriori probability algorithms) the solution is determined that maximizes a likelihood or a posteriori probability function.« less

Planck 2015 results: VI. LFI mapmaking

DOE PAGES

Ade, P. A. R.; Aghanim, N.; Ashdown, M.; ...

2016-09-20

This article describes the mapmaking procedure applied to Planck Low Frequency Instrument (LFI) data. The mapmaking step takes as input the calibrated timelines and pointing information. The main products are sky maps of I, Q, and U Stokes components. For the first time, we present polarization maps at LFI frequencies. The mapmaking algorithm is based on a destriping technique, which is enhanced with a noise prior. The Galactic region is masked to reduce errors arising from bandpass mismatch and high signal gradients. We apply horn-uniform radiometer weights to reduce the effects of beam-shape mismatch. The algorithm is the same asmore » used for the 2013 release, apart from small changes in parameter settings. We validate the procedure through simulations. Special emphasis is put on the control of systematics, which is particularly important for accurate polarization analysis. We also produce low-resolution versions of the maps and corresponding noise covariance matrices. These serve as input in later analysis steps and parameter estimation. The noise covariance matrices are validated through noise Monte Carlo simulations. The residual noise in the map products is characterized through analysis of half-ring maps, noise covariance matrices, and simulations.« less

Development of Eye Dosimeter Using Additive Manufacturing Techniques to Monitor Occupational Eye Lens Exposures to Interventional Radiologists

NASA Astrophysics Data System (ADS)

Choi, JungHwan

In this project, an eye dosimeter was designed for monitoring occupational lens of the eye exposures targeted to interventional radiologists who are often indirectly exposed to scattered radiation from the patient while performing image-guided procedures. The dosimeter was designed with a computer-aided design software to facilitate additive manufacturing techniques to make the dosimeter. The dosimeter consisted of three separate components that are attached to the hinges and the bridge of the occupational worker's protective eyewear. The produced dosimeter was radiologically calibrated to measure the lens dose on an anthropomorphic phantom of the human head. To supplement the physical design, an algorithm was written that prompts the user to input the element responses of the dosimeter, then estimates the average angle, energy, and resulting lens dose of the exposure by comparing the input with the data acquired during the dosimeter calibration procedure. The performance of the calibrated dosimeter (and the algorithm) was evaluated according to guidelines of the American National Standards Institute, and the dosimeter demonstrated a performance that was in compliance with the standard's performance criteria which suggests that the design of the eye dosimeter is feasible.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes.

PubMed

Mougiakakou, Stavroula G; Prountzou, Aikaterini; Iliopoulou, Dimitra; Nikita, Konstantina S; Vazeou, Andriani; Bartsocas, Christos S

2006-01-01

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Fast Inference with Min-Sum Matrix Product.

PubMed

Felzenszwalb, Pedro F; McAuley, Julian J

2011-12-01

The MAP inference problem in many graphical models can be solved efficiently using a fast algorithm for computing min-sum products of n × n matrices. The class of models in question includes cyclic and skip-chain models that arise in many applications. Although the worst-case complexity of the min-sum product operation is not known to be much better than O(n(3)), an O(n(2.5)) expected time algorithm was recently given, subject to some constraints on the input matrices. In this paper, we give an algorithm that runs in O(n(2) log n) expected time, assuming that the entries in the input matrices are independent samples from a uniform distribution. We also show that two variants of our algorithm are quite fast for inputs that arise in several applications. This leads to significant performance gains over previous methods in applications within computer vision and natural language processing.

Autonomous frequency domain identification: Theory and experiment

NASA Technical Reports Server (NTRS)

Yam, Yeung; Bayard, D. S.; Hadaegh, F. Y.; Mettler, E.; Milman, M. H.; Scheid, R. E.

1989-01-01

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

Characterization and error analysis of an operational retrieval algorithm for estimating column ozone and aerosol properties from ground-based ultra-violet irradiance measurements

NASA Astrophysics Data System (ADS)

Taylor, Thomas E.; L'Ecuyer, Tristan; Slusser, James; Stephens, Graeme; Krotkov, Nick; Davis, John; Goering, Christian

2005-08-01

Extensive sensitivity and error characteristics of a recently developed optimal estimation retrieval algorithm which simultaneously determines aerosol optical depth (AOD), aerosol single scatter albedo (SSA) and total ozone column (TOC) from ultra-violet irradiances are described. The algorithm inverts measured diffuse and direct irradiances at 7 channels in the UV spectral range obtained from the United States Department of Agriculture's (USDA) UV-B Monitoring and Research Program's (UVMRP) network of 33 ground-based UV-MFRSR instruments to produce aerosol optical properties and TOC at all seven wavelengths. Sensitivity studies of the Tropospheric Ultra-violet/Visible (TUV) radiative transfer model performed for various operating modes (Delta-Eddington versus n-stream Discrete Ordinate) over domains of AOD, SSA, TOC, asymmetry parameter and surface albedo show that the solutions are well constrained. Realistic input error budgets and diagnostic and error outputs from the retrieval are analyzed to demonstrate the atmospheric conditions under which the retrieval provides useful and significant results. After optimizing the algorithm for the USDA site in Panther Junction, Texas the retrieval algorithm was run on a cloud screened set of irradiance measurements for the month of May 2003. Comparisons to independently derived AOD's are favorable with root mean square (RMS) differences of about 3% to 7% at 300nm and less than 1% at 368nm, on May 12 and 22, 2003. This retrieval method will be used to build an aerosol climatology and provide ground-truthing of satellite measurements by running it operationally on the USDA UV network database.

The EUMETSAT sea ice concentration climate data record

NASA Astrophysics Data System (ADS)

Tonboe, Rasmus T.; Eastwood, Steinar; Lavergne, Thomas; Sørensen, Atle M.; Rathmann, Nicholas; Dybkjær, Gorm; Toudal Pedersen, Leif; Høyer, Jacob L.; Kern, Stefan

2016-09-01

An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea ice concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new sea ice concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea ice concentration retrieval accuracy. A comparison to US National Ice Center sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate sea ice concentrations between open water and 100 % ice. The sea ice concentration climate data record is available for download at www.osi-saf.org, including documentation.

Real-time estimation of prostate tumor rotation and translation with a kV imaging system based on an iterative closest point algorithm.

PubMed

Tehrani, Joubin Nasehi; O'Brien, Ricky T; Poulsen, Per Rugaard; Keall, Paul

2013-12-07

Previous studies have shown that during cancer radiotherapy a small translation or rotation of the tumor can lead to errors in dose delivery. Current best practice in radiotherapy accounts for tumor translations, but is unable to address rotation due to a lack of a reliable real-time estimate. We have developed a method based on the iterative closest point (ICP) algorithm that can compute rotation from kilovoltage x-ray images acquired during radiation treatment delivery. A total of 11 748 kilovoltage (kV) images acquired from ten patients (one fraction for each patient) were used to evaluate our tumor rotation algorithm. For each kV image, the three dimensional coordinates of three fiducial markers inside the prostate were calculated. The three dimensional coordinates were used as input to the ICP algorithm to calculate the real-time tumor rotation and translation around three axes. The results show that the root mean square error was improved for real-time calculation of tumor displacement from a mean of 0.97 mm with the stand alone translation to a mean of 0.16 mm by adding real-time rotation and translation displacement with the ICP algorithm. The standard deviation (SD) of rotation for the ten patients was 2.3°, 0.89° and 0.72° for rotation around the right-left (RL), anterior-posterior (AP) and superior-inferior (SI) directions respectively. The correlation between all six degrees of freedom showed that the highest correlation belonged to the AP and SI translation with a correlation of 0.67. The second highest correlation in our study was between the rotation around RL and rotation around AP, with a correlation of -0.33. Our real-time algorithm for calculation of rotation also confirms previous studies that have shown the maximum SD belongs to AP translation and rotation around RL. ICP is a reliable and fast algorithm for estimating real-time tumor rotation which could create a pathway to investigational clinical treatment studies requiring real-time measurement and adaptation to tumor rotation.

Real-time estimation of prostate tumor rotation and translation with a kV imaging system based on an iterative closest point algorithm

NASA Astrophysics Data System (ADS)

Nasehi Tehrani, Joubin; O'Brien, Ricky T.; Rugaard Poulsen, Per; Keall, Paul

2013-12-01

Previous studies have shown that during cancer radiotherapy a small translation or rotation of the tumor can lead to errors in dose delivery. Current best practice in radiotherapy accounts for tumor translations, but is unable to address rotation due to a lack of a reliable real-time estimate. We have developed a method based on the iterative closest point (ICP) algorithm that can compute rotation from kilovoltage x-ray images acquired during radiation treatment delivery. A total of 11 748 kilovoltage (kV) images acquired from ten patients (one fraction for each patient) were used to evaluate our tumor rotation algorithm. For each kV image, the three dimensional coordinates of three fiducial markers inside the prostate were calculated. The three dimensional coordinates were used as input to the ICP algorithm to calculate the real-time tumor rotation and translation around three axes. The results show that the root mean square error was improved for real-time calculation of tumor displacement from a mean of 0.97 mm with the stand alone translation to a mean of 0.16 mm by adding real-time rotation and translation displacement with the ICP algorithm. The standard deviation (SD) of rotation for the ten patients was 2.3°, 0.89° and 0.72° for rotation around the right-left (RL), anterior-posterior (AP) and superior-inferior (SI) directions respectively. The correlation between all six degrees of freedom showed that the highest correlation belonged to the AP and SI translation with a correlation of 0.67. The second highest correlation in our study was between the rotation around RL and rotation around AP, with a correlation of -0.33. Our real-time algorithm for calculation of rotation also confirms previous studies that have shown the maximum SD belongs to AP translation and rotation around RL. ICP is a reliable and fast algorithm for estimating real-time tumor rotation which could create a pathway to investigational clinical treatment studies requiring real-time measurement and adaptation to tumor rotation.

Satellite-based estimation of evapotranspiration in typical forests of China

NASA Astrophysics Data System (ADS)

Wang, Y.; Li, R.

2017-12-01

Evapotranspiration (ET) is the key process affecting the interaction between land surface and atmosphere. Satellite remote sensing is the only feasible technique to monitor the terrestrial ET on large scale. Microwave Emissivity Difference Vegetation Index (EDVI) indicates vegetation water content and can be retrieved under both clear and cloudy sky. Based on EDVI, a quantitative algorithm for ET estimation in China was developed. In this study, we improved the EDVI-based ET algorithm by using the datasets from multiple platforms, including Moderate-Resolution Imaging Spectroradiometer (MODIS), Clouds and Earth's Radiation energy system (CERES) and European Centre for Medium-Range Weather Forecasts (ECMWF). As primary inputs of the algorithm, they are all independent from ground-based measurements. The improved algorithm was tested in three ChinaFlux forest sites, Dinghushan(DHS) subtropical evergreen broad-leaved forest site, Qianyanzhou(QYZ) subtropical man-planted forest site and Changbaishan(CBS) temperate deciduous broad-leaved coniferous mixed forest site. Validations against the in-situ measured ETobs from 2003 to 2005 showed that the EDVI-based algorithm has the capability to simulate midday ET within reasonable accuracies. In terms of the magnitude and seasonal cycle, the estimated ETcal are in very good agreement with the ETobs. The correlation coefficients(R) between ETcal and ETobs during midday vary from 0.51 to 0.80 over the study years, with the annual mean bias (relative bias) ranging from -53.02 Wm-2 (-26.46%) to 34.02 Wm-2 (+23.69%). At monthly scale, the R of monthly mean ETcal and ETobs can reach to 0.83, 0.93 and 0.82 at DHS, QYZ and CBS, with bias of +3.0%, -22.3% and -9.7%, respectively. Contamination from precipitation can partly affect the performances of this algorithm. Validation results generally become better after removing those samples in rainy days. The results indicate that this EDVI-based algorithm, driven completely by using satellite and reanalysis datasets, has a great potential for monitoring terrestrial ET in large spatial scale and under both clear and cloudy sky.

Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Morelli, Eugene A.

2012-01-01

Results of the Aeronautics Research Mission Directorate Seedling Project Phase I research project entitled "Nonlinear Aerodynamics Modeling using Fuzzy Logic" are presented. Efficient and rapid flight test capabilities were developed for estimating highly nonlinear models of airplane aerodynamics over a large flight envelope. Results showed that the flight maneuvers developed, used in conjunction with the fuzzy-logic system identification algorithms, produced very good model fits of the data, with no model structure inputs required, for flight conditions ranging from cruise to departure and spin conditions.

DEM Calibration Approach: design of experiment

NASA Astrophysics Data System (ADS)

Boikov, A. V.; Savelev, R. V.; Payor, V. A.

2018-05-01

The problem of DEM models calibration is considered in the article. It is proposed to divide models input parameters into those that require iterative calibration and those that are recommended to measure directly. A new method for model calibration based on the design of the experiment for iteratively calibrated parameters is proposed. The experiment is conducted using a specially designed stand. The results are processed with technical vision algorithms. Approximating functions are obtained and the error of the implemented software and hardware complex is estimated. The prospects of the obtained results are discussed.

Note: Design of FPGA based system identification module with application to atomic force microscopy

NASA Astrophysics Data System (ADS)

Ghosal, Sayan; Pradhan, Sourav; Salapaka, Murti

2018-05-01

The science of system identification is widely utilized in modeling input-output relationships of diverse systems. In this article, we report field programmable gate array (FPGA) based implementation of a real-time system identification algorithm which employs forgetting factors and bias compensation techniques. The FPGA module is employed to estimate the mechanical properties of surfaces of materials at the nano-scale with an atomic force microscope (AFM). The FPGA module is user friendly which can be interfaced with commercially available AFMs. Extensive simulation and experimental results validate the design.

Multiclass Bayes error estimation by a feature space sampling technique

NASA Technical Reports Server (NTRS)

Mobasseri, B. G.; Mcgillem, C. D.

1979-01-01

A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Inverse Flush Air Data System (FADS) for Real Time Simulations

NASA Astrophysics Data System (ADS)

Madhavanpillai, Jayakumar; Dhoaya, Jayanta; Balakrishnan, Vidya Saraswathi; Narayanan, Remesh; Chacko, Finitha Kallely; Narayanan, Shyam Mohan

2017-12-01

Flush Air Data Sensing System (FADS) forms a mission critical sub system in future reentry vehicles. FADS makes use of surface pressure measurements from the nose cap of the vehicle for deriving the air data parameters of the vehicle such as angle of attack, angle of sideslip, Mach number, etc. These parameters find use in the flight control and guidance systems, and also assist in the overall mission management. The FADS under consideration in this paper makes use of nine pressure ports located in the nose cap of a technology demonstrator vehicle. In flight, the air data parameters are obtained from the FADS estimation algorithm using the pressure data at the nine pressure ports. But, these pressure data will not be available, for testing the FADS package during ground simulation. So, an inverse software to FADS which estimates the pressure data at the pressure ports for a given flight condition is developed. These pressure data at the nine ports will go as input to the FADS package during ground simulation. The software is run to generate the pressure data for the descent phase trajectory of the technology demonstrator. This data is used again to generate the air data parameters from FADS algorithm. The computed results from FADS algorithm match well with the trajectory data.

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

NASA Astrophysics Data System (ADS)

Ebtehaj, Isa; Bonakdari, Hossein

2017-12-01

Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number ( Fr) parameters includes the volumetric sediment concentration ( C V ), ratio of median particle diameter to hydraulic radius ( d/R), ratio of median particle diameter to pipe diameter ( d/D) and overall friction factor of sediment ( λ s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy ( R 2 = 0.965) than the ANFIS model and regression-based equations.

Extraction of incident irradiance from LWIR hyperspectral imagery

NASA Astrophysics Data System (ADS)

Lahaie, Pierre

2014-10-01

The atmospheric correction of thermal hyperspectral imagery can be separated in two distinct processes: Atmospheric Compensation (AC) and Temperature and Emissivity separation (TES). TES requires for input at each pixel, the ground leaving radiance and the atmospheric downwelling irradiance, which are the outputs of the AC process. The extraction from imagery of the downwelling irradiance requires assumptions about some of the pixels' nature, the sensor and the atmosphere. Another difficulty is that, often the sensor's spectral response is not well characterized. To deal with this unknown, we defined a spectral mean operator that is used to filter the ground leaving radiance and a computation of the downwelling irradiance from MODTRAN. A user will select a number of pixels in the image for which the emissivity is assumed to be known. The emissivity of these pixels is assumed to be smooth and that the only spectrally fast varying variable in the downwelling irradiance. Using these assumptions we built an algorithm to estimate the downwelling irradiance. The algorithm is used on all the selected pixels. The estimated irradiance is the average on the spectral channels of the resulting computation. The algorithm performs well in simulation and results are shown for errors in the assumed emissivity and for errors in the atmospheric profiles. The sensor noise influences mainly the required number of pixels.

SNDR Limits of Oscillator-Based Sensor Readout Circuits.

PubMed

Cardes, Fernando; Quintero, Andres; Gutierrez, Eric; Buffa, Cesare; Wiesbauer, Andreas; Hernandez, Luis

2018-02-03

This paper analyzes the influence of phase noise and distortion on the performance of oscillator-based sensor data acquisition systems. Circuit noise inherent to the oscillator circuit manifests as phase noise and limits the SNR. Moreover, oscillator nonlinearity generates distortion for large input signals. Phase noise analysis of oscillators is well known in the literature, but the relationship between phase noise and the SNR of an oscillator-based sensor is not straightforward. This paper proposes a model to estimate the influence of phase noise in the performance of an oscillator-based system by reflecting the phase noise to the oscillator input. The proposed model is based on periodic steady-state analysis tools to predict the SNR of the oscillator. The accuracy of this model has been validated by both simulation and experiment in a 130 nm CMOS prototype. We also propose a method to estimate the SNDR and the dynamic range of an oscillator-based readout circuit that improves by more than one order of magnitude the simulation time compared to standard time domain simulations. This speed up enables the optimization and verification of this kind of systems with iterative algorithms.

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

NASA Astrophysics Data System (ADS)

Krishnanathan, Kirubhakaran; Anderson, Sean R.; Billings, Stephen A.; Kadirkamanathan, Visakan

2016-11-01

In this paper, we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (1) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (2) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (3) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

Quantum rewinding via phase estimation

NASA Astrophysics Data System (ADS)

Tabia, Gelo Noel

2015-03-01

In cryptography, the notion of a zero-knowledge proof was introduced by Goldwasser, Micali, and Rackoff. An interactive proof system is said to be zero-knowledge if any verifier interacting with an honest prover learns nothing beyond the validity of the statement being proven. With recent advances in quantum information technologies, it has become interesting to ask if classical zero-knowledge proof systems remain secure against adversaries with quantum computers. The standard approach to show the zero-knowledge property involves constructing a simulator for a malicious verifier that can be rewinded to a previous step when the simulation fails. In the quantum setting, the simulator can be described by a quantum circuit that takes an arbitrary quantum state as auxiliary input but rewinding becomes a nontrivial issue. Watrous proposed a quantum rewinding technique in the case where the simulation's success probability is independent of the auxiliary input. Here I present a more general quantum rewinding scheme that employs the quantum phase estimation algorithm. This work was funded by institutional research grant IUT2-1 from the Estonian Research Council and by the European Union through the European Regional Development Fund.

A probabilistic approach to the drag-based model

NASA Astrophysics Data System (ADS)

Napoletano, Gianluca; Forte, Roberta; Moro, Dario Del; Pietropaolo, Ermanno; Giovannelli, Luca; Berrilli, Francesco

2018-02-01

The forecast of the time of arrival (ToA) of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the ToA using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.

A soft computing based approach using modified selection strategy for feature reduction of medical systems.

PubMed

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

PubMed Central

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data. PMID:23573172

Investigation of new techniques for aircraft navigation using the omega navigation

NASA Technical Reports Server (NTRS)

Baxa, E. G., Jr.

1978-01-01

An OMEGA navigation receiver with a microprocessor as the computational component was investigated. A version of the INTEL 4004 microprocessor macroassembler suitable for use on the CDC-6600 system and development of a FORTRAN IV simulator program for the microprocessor was developed. Supporting studies included development and evaluation of navigation algorithms to generate relative position information from OMEGA VLF phase measurements. Simulation studies were used to evaluate assumptions made in developing a navigation equation in OMEGA Line of Position (LOP) coordinates. Included in the navigation algorithms was a procedure for calculating a position in latitude/longitude given an OMEGA LOP fix. Implementation of a digital phase locked loop (DPLL) was evaluated on the basic of phase response characteristics over a range of input phase variations. Included also is an analytical evaluation on the basis of error probability of an algorithm for automatic time synchronization of the receiver to the OMEGA broadcast format. The use of actual OMEGA phase data and published propagation prediction corrections to determine phase velocity estimates was discussed.

An Envelope Based Feedback Control System for Earthquake Early Warning: Reality Check Algorithm

NASA Astrophysics Data System (ADS)

Heaton, T. H.; Karakus, G.; Beck, J. L.

2016-12-01

Earthquake early warning systems are, in general, designed to be open loop control systems in such a way that the output, i.e., the warning messages, only depend on the input, i.e., recorded ground motions, up to the moment when the message is issued in real-time. We propose an algorithm, which is called Reality Check Algorithm (RCA), which would assess the accuracy of issued warning messages, and then feed the outcome of the assessment back into the system. Then, the system would modify its messages if necessary. That is, we are proposing to convert earthquake early warning systems into feedback control systems by integrating them with RCA. RCA works by continuously monitoring and comparing the observed ground motions' envelopes to the predicted envelopes of Virtual Seismologist (Cua 2005). Accuracy of magnitude and location (both spatial and temporal) estimations of the system are assessed separately by probabilistic classification models, which are trained by a Sparse Bayesian Learning technique called Automatic Relevance Determination prior.

An H(∞) control approach to robust learning of feedforward neural networks.

PubMed

Jing, Xingjian

2011-09-01

A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise. Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H(∞) "noise" attenuation level. Several existing BP-type algorithms are shown to be special cases of the new H(∞)-learning algorithm. Theoretical analysis and several examples are provided to show the advantages of the new method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Fast Emission Estimates in China Constrained by Satellite Observations (Invited)

NASA Astrophysics Data System (ADS)

Mijling, B.; van der A, R.

2013-12-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for an emerging economy such as China, where rapid economic growth changes emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. Constraining emissions from concentration measurements is, however, computationally challenging. Within the GlobEmission project of the European Space Agency (ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China, using the CHIMERE model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission estimates result in a better agreement between observations and simulations of air pollutant concentrations, facilitating improved air quality forecasts. The EU project MarcoPolo will combine these emission estimates from space with statistical information on e.g. land use, population density and traffic to construct a new up-to-date emission inventory for China.

Software algorithm and hardware design for real-time implementation of new spectral estimator

PubMed Central

2014-01-01

Background Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Method Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. Results The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. Conclusions The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time. PMID:24886214

Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data.

NASA Astrophysics Data System (ADS)

Steiner, Matthias; Houze, Robert A., Jr.; Yuter, Sandra E.

1995-09-01

Three algorithms extract information on precipitation type, structure, and amount from operational radar and rain gauge data. Tests on one month of data from one site show that the algorithms perform accurately and provide products that characterize the essential features of the precipitation climatology. Input to the algorithms are the operationally executed volume scans of a radar and the data from a surrounding rain gauge network. The algorithms separate the radar echoes into convective and stratiform regions, statistically summarize the vertical structure of the radar echoes, and determine precipitation rates and amounts on high spatial resolution.The convective and stratiform regions are separated on the basis of the intensity and sharpness of the peaks of echo intensity. The peaks indicate the centers of the convective region. Precipitation not identified as convective is stratiform. This method avoids the problem of underestimating the stratiform precipitation. The separation criteria are applied in exactly the same way throughout the observational domain and the product generated by the algorithm can be compared directly to model output. An independent test of the algorithm on data for which high-resolution dual-Doppler observations are available shows that the convective stratiform separation algorithm is consistent with the physical definitions of convective and stratiform precipitation.The vertical structure algorithm presents the frequency distribution of radar reflectivity as a function of height and thus summarizes in a single plot the vertical structure of all the radar echoes observed during a month (or any other time period). Separate plots reveal the essential differences in structure between the convective and stratiform echoes.Tests yield similar results (within less than 10%) for monthly rain statistics regardless of the technique used for estimating the precipitation, as long as the radar reflectivity values are adjusted to agree with monthly rain gauge data. It makes little difference whether the adjustment is by monthly mean rates or percentiles. Further tests show that 1-h sampling is sufficient to obtain an accurate estimate of monthly rain statistics.

Effects of control inputs on the estimation of stability and control parameters of a light airplane

NASA Technical Reports Server (NTRS)

Cannaday, R. L.; Suit, W. T.

1977-01-01

The maximum likelihood parameter estimation technique was used to determine the values of stability and control derivatives from flight test data for a low-wing, single-engine, light airplane. Several input forms were used during the tests to investigate the consistency of parameter estimates as it relates to inputs. These consistencies were compared by using the ensemble variance and estimated Cramer-Rao lower bound. In addition, the relationship between inputs and parameter correlations was investigated. Results from the stabilator inputs are inconclusive but the sequence of rudder input followed by aileron input or aileron followed by rudder gave more consistent estimates than did rudder or ailerons individually. Also, square-wave inputs appeared to provide slightly improved consistency in the parameter estimates when compared to sine-wave inputs.

Hybrid Reduced Order Modeling Algorithms for Reactor Physics Calculations

NASA Astrophysics Data System (ADS)

Bang, Youngsuk

Reduced order modeling (ROM) has been recognized as an indispensable approach when the engineering analysis requires many executions of high fidelity simulation codes. Examples of such engineering analyses in nuclear reactor core calculations, representing the focus of this dissertation, include the functionalization of the homogenized few-group cross-sections in terms of the various core conditions, e.g. burn-up, fuel enrichment, temperature, etc. This is done via assembly calculations which are executed many times to generate the required functionalization for use in the downstream core calculations. Other examples are sensitivity analysis used to determine important core attribute variations due to input parameter variations, and uncertainty quantification employed to estimate core attribute uncertainties originating from input parameter uncertainties. ROM constructs a surrogate model with quantifiable accuracy which can replace the original code for subsequent engineering analysis calculations. This is achieved by reducing the effective dimensionality of the input parameter, the state variable, or the output response spaces, by projection onto the so-called active subspaces. Confining the variations to the active subspace allows one to construct an ROM model of reduced complexity which can be solved more efficiently. This dissertation introduces a new algorithm to render reduction with the reduction errors bounded based on a user-defined error tolerance which represents the main challenge of existing ROM techniques. Bounding the error is the key to ensuring that the constructed ROM models are robust for all possible applications. Providing such error bounds represents one of the algorithmic contributions of this dissertation to the ROM state-of-the-art. Recognizing that ROM techniques have been developed to render reduction at different levels, e.g. the input parameter space, the state space, and the response space, this dissertation offers a set of novel hybrid ROM algorithms which can be readily integrated into existing methods and offer higher computational efficiency and defendable accuracy of the reduced models. For example, the snapshots ROM algorithm is hybridized with the range finding algorithm to render reduction in the state space, e.g. the flux in reactor calculations. In another implementation, the perturbation theory used to calculate first order derivatives of responses with respect to parameters is hybridized with a forward sensitivity analysis approach to render reduction in the parameter space. Reduction at the state and parameter spaces can be combined to render further reduction at the interface between different physics codes in a multi-physics model with the accuracy quantified in a similar manner to the single physics case. Although the proposed algorithms are generic in nature, we focus here on radiation transport models used in support of the design and analysis of nuclear reactor cores. In particular, we focus on replacing the traditional assembly calculations by ROM models to facilitate the generation of homogenized cross-sections for downstream core calculations. The implication is that assembly calculations could be done instantaneously therefore precluding the need for the expensive evaluation of the few-group cross-sections for all possible core conditions. Given the generic natures of the algorithms, we make an effort to introduce the material in a general form to allow non-nuclear engineers to benefit from this work.

A new algorithm for reliable and general NMR resonance assignment.

PubMed

Schmidt, Elena; Güntert, Peter

2012-08-01

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Evaluation of Algorithms for a Miles-in-Trail Decision Support Tool

NASA Technical Reports Server (NTRS)

Bloem, Michael; Hattaway, David; Bambos, Nicholas

2012-01-01

Four machine learning algorithms were prototyped and evaluated for use in a proposed decision support tool that would assist air traffic managers as they set Miles-in-Trail restrictions. The tool would display probabilities that each possible Miles-in-Trail value should be used in a given situation. The algorithms were evaluated with an expected Miles-in-Trail cost that assumes traffic managers set restrictions based on the tool-suggested probabilities. Basic Support Vector Machine, random forest, and decision tree algorithms were evaluated, as was a softmax regression algorithm that was modified to explicitly reduce the expected Miles-in-Trail cost. The algorithms were evaluated with data from the summer of 2011 for air traffic flows bound to the Newark Liberty International Airport (EWR) over the ARD, PENNS, and SHAFF fixes. The algorithms were provided with 18 input features that describe the weather at EWR, the runway configuration at EWR, the scheduled traffic demand at EWR and the fixes, and other traffic management initiatives in place at EWR. Features describing other traffic management initiatives at EWR and the weather at EWR achieved relatively high information gain scores, indicating that they are the most useful for estimating Miles-in-Trail. In spite of a high variance or over-fitting problem, the decision tree algorithm achieved the lowest expected Miles-in-Trail costs when the algorithms were evaluated using 10-fold cross validation with the summer 2011 data for these air traffic flows.

A new real-time tsunami detection algorithm

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Pignagnoli, L.

2016-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of sea-bed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability, at low computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. Pressure data sets acquired by Bottom Pressure Recorders in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event which occurred at Haida Gwaii on October 28th, 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard the GEOSTAR stand-alone multidisciplinary abyssal observatory, deployed in the Gulf of Cadiz during the EC project NEAREST and on NEMO-SN1 cabled observatory deployed in the Western Ionian Sea, operational node of the European research infrastructure EMSO.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Facial skin color measurement based on camera colorimetric characterization

NASA Astrophysics Data System (ADS)

Yang, Boquan; Zhou, Changhe; Wang, Shaoqing; Fan, Xin; Li, Chao

2016-10-01

The objective measurement of facial skin color and its variance is of great significance as much information can be obtained from it. In this paper, we developed a new skin color measurement procedure which includes following parts: first, a new skin tone color checker made of pantone Skin Tone Color Checker was designed for camera colorimetric characterization; second, the chromaticity of light source was estimated via a new scene illumination estimation method considering several previous algorithms; third, chromatic adaption was used to convert the input facial image into output facial image which appears taken under canonical light; finally the validity and accuracy of our method was verified by comparing the results obtained by our procedure with these by spectrophotometer.

BrainIACS: a system for web-based medical image processing

NASA Astrophysics Data System (ADS)

Kishore, Bhaskar; Bazin, Pierre-Louis; Pham, Dzung L.

2009-02-01

We describe BrainIACS, a web-based medical image processing system that permits and facilitates algorithm developers to quickly create extensible user interfaces for their algorithms. Designed to address the challenges faced by algorithm developers in providing user-friendly graphical interfaces, BrainIACS is completely implemented using freely available, open-source software. The system, which is based on a client-server architecture, utilizes an AJAX front-end written using the Google Web Toolkit (GWT) and Java Servlets running on Apache Tomcat as its back-end. To enable developers to quickly and simply create user interfaces for configuring their algorithms, the interfaces are described using XML and are parsed by our system to create the corresponding user interface elements. Most of the commonly found elements such as check boxes, drop down lists, input boxes, radio buttons, tab panels and group boxes are supported. Some elements such as the input box support input validation. Changes to the user interface such as addition and deletion of elements are performed by editing the XML file or by using the system's user interface creator. In addition to user interface generation, the system also provides its own interfaces for data transfer, previewing of input and output files, and algorithm queuing. As the system is programmed using Java (and finally Java-script after compilation of the front-end code), it is platform independent with the only requirements being that a Servlet implementation be available and that the processing algorithms can execute on the server platform.

Transform methods for precision continuum and control models of flexible space structures

NASA Technical Reports Server (NTRS)

Lupi, Victor D.; Turner, James D.; Chun, Hon M.

1991-01-01

An open loop optimal control algorithm is developed for general flexible structures, based on Laplace transform methods. A distributed parameter model of the structure is first presented, followed by a derivation of the optimal control algorithm. The control inputs are expressed in terms of their Fourier series expansions, so that a numerical solution can be easily obtained. The algorithm deals directly with the transcendental transfer functions from control inputs to outputs of interest, and structural deformation penalties, as well as penalties on control effort, are included in the formulation. The algorithm is applied to several structures of increasing complexity to show its generality.

Tensor Rank Preserving Discriminant Analysis for Facial Recognition.

PubMed

Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo

2017-10-12

Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.

Autumn Algorithm-Computation of Hybridization Networks for Realistic Phylogenetic Trees.

PubMed

Huson, Daniel H; Linz, Simone

2018-01-01

A minimum hybridization network is a rooted phylogenetic network that displays two given rooted phylogenetic trees using a minimum number of reticulations. Previous mathematical work on their calculation has usually assumed the input trees to be bifurcating, correctly rooted, or that they both contain the same taxa. These assumptions do not hold in biological studies and "realistic" trees have multifurcations, are difficult to root, and rarely contain the same taxa. We present a new algorithm for computing minimum hybridization networks for a given pair of "realistic" rooted phylogenetic trees. We also describe how the algorithm might be used to improve the rooting of the input trees. We introduce the concept of "autumn trees", a nice framework for the formulation of algorithms based on the mathematics of "maximum acyclic agreement forests". While the main computational problem is hard, the run-time depends mainly on how different the given input trees are. In biological studies, where the trees are reasonably similar, our parallel implementation performs well in practice. The algorithm is available in our open source program Dendroscope 3, providing a platform for biologists to explore rooted phylogenetic networks. We demonstrate the utility of the algorithm using several previously studied data sets.

Neuro-genetic non-invasive temperature estimation: intensity and spatial prediction.

PubMed

Teixeira, César A; Ruano, M Graça; Ruano, António E; Pereira, Wagner C A

2008-06-01

The existence of proper non-invasive temperature estimators is an essential aspect when thermal therapy applications are envisaged. These estimators must be good predictors to enable temperature estimation at different operational situations, providing better control of the therapeutic instrumentation. In this work, radial basis functions artificial neural networks were constructed to access temperature evolution on an ultrasound insonated medium. The employed models were radial basis functions neural networks with external dynamics induced by their inputs. Both the most suited set of model inputs and number of neurons in the network were found using the multi-objective genetic algorithm. The neural models were validated in two situations: the operating ones, as used in the construction of the network; and in 11 unseen situations. The new data addressed two new spatial locations and a new intensity level, assessing the intensity and space prediction capacity of the proposed model. Good performance was obtained during the validation process both in terms of the spatial points considered and whenever the new intensity level was within the range of applied intensities. A maximum absolute error of 0.5 degrees C+/-10% (0.5 degrees C is the gold-standard threshold in hyperthermia/diathermia) was attained with low computationally complex models. The results confirm that the proposed neuro-genetic approach enables foreseeing temperature propagation, in connection to intensity and space parameters, thus enabling the assessment of different operating situations with proper temperature resolution.

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

NASA Technical Reports Server (NTRS)

Wang, Jian-Jian; Adler, Robert F.; Huffman, George; Bolvin, David

2013-01-01

An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primaryTRMMinstruments: theTRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation sigma among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3mm day(exp -1) for the deep tropical ocean beltbetween 10 deg N and 10 deg S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by sigma is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

Approximated affine projection algorithm for feedback cancellation in hearing aids.

PubMed

Lee, Sangmin; Kim, In-Young; Park, Young-Cheol

2007-09-01

We propose an approximated affine projection (AP) algorithm for feedback cancellation in hearing aids. It is based on the conventional approach using the Gauss-Seidel (GS) iteration, but provides more stable convergence behaviour even with small step sizes. In the proposed algorithm, a residue of the weighted error vector, instead of the current error sample, is used to provide stable convergence. A new learning rate control scheme is also applied to the proposed algorithm to prevent signal cancellation and system instability. The new scheme determines step size in proportion to the prediction factor of the input, so that adaptation is inhibited whenever tone-like signals are present in the input. Simulation results verified the efficiency of the proposed algorithm.

Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Morucci, S.

2017-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

Neuromusculoskeletal model self-calibration for on-line sequential bayesian moment estimation

NASA Astrophysics Data System (ADS)

Bueno, Diana R.; Montano, L.

2017-04-01

Objective. Neuromusculoskeletal models involve many subject-specific physiological parameters that need to be adjusted to adequately represent muscle properties. Traditionally, neuromusculoskeletal models have been calibrated with a forward-inverse dynamic optimization which is time-consuming and unfeasible for rehabilitation therapy. Non self-calibration algorithms have been applied to these models. To the best of our knowledge, the algorithm proposed in this work is the first on-line calibration algorithm for muscle models that allows a generic model to be adjusted to different subjects in a few steps. Approach. In this paper we propose a reformulation of the traditional muscle models that is able to sequentially estimate the kinetics (net joint moments), and also its full self-calibration (subject-specific internal parameters of the muscle from a set of arbitrary uncalibrated data), based on the unscented Kalman filter. The nonlinearity of the model as well as its calibration problem have obliged us to adopt the sum of Gaussians filter suitable for nonlinear systems. Main results. This sequential Bayesian self-calibration algorithm achieves a complete muscle model calibration using as input only a dataset of uncalibrated sEMG and kinematics data. The approach is validated experimentally using data from the upper limbs of 21 subjects. Significance. The results show the feasibility of neuromusculoskeletal model self-calibration. This study will contribute to a better understanding of the generalization of muscle models for subject-specific rehabilitation therapies. Moreover, this work is very promising for rehabilitation devices such as electromyography-driven exoskeletons or prostheses.

Modeling Global Biogenic Emission of Isoprene: Exploration of Model Drivers

NASA Technical Reports Server (NTRS)

Alexander, Susan E.; Potter, Christopher S.; Coughlan, Joseph C.; Klooster, Steven A.; Lerdau, Manuel T.; Chatfield, Robert B.; Peterson, David L. (Technical Monitor)

1996-01-01

Vegetation provides the major source of isoprene emission to the atmosphere. We present a modeling approach to estimate global biogenic isoprene emission. The isoprene flux model is linked to a process-based computer simulation model of biogenic trace-gas fluxes that operates on scales that link regional and global data sets and ecosystem nutrient transformations Isoprene emission estimates are determined from estimates of ecosystem specific biomass, emission factors, and algorithms based on light and temperature. Our approach differs from an existing modeling framework by including the process-based global model for terrestrial ecosystem production, satellite derived ecosystem classification, and isoprene emission measurements from a tropical deciduous forest. We explore the sensitivity of model estimates to input parameters. The resulting emission products from the global 1 degree x 1 degree coverage provided by the satellite datasets and the process model allow flux estimations across large spatial scales and enable direct linkage to atmospheric models of trace-gas transport and transformation.

Developments in Human Centered Cueing Algorithms for Control of Flight Simulator Motion Systems

NASA Technical Reports Server (NTRS)

Houck, Jacob A.; Telban, Robert J.; Cardullo, Frank M.

1997-01-01

The authors conducted further research with cueing algorithms for control of flight simulator motion systems. A variation of the so-called optimal algorithm was formulated using simulated aircraft angular velocity input as a basis. Models of the human vestibular sensation system, i.e. the semicircular canals and otoliths, are incorporated within the algorithm. Comparisons of angular velocity cueing responses showed a significant improvement over a formulation using angular acceleration input. Results also compared favorably with the coordinated adaptive washout algorithm, yielding similar results for angular velocity cues while eliminating false cues and reducing the tilt rate for longitudinal cues. These results were confirmed in piloted tests on the current motion system at NASA-Langley, the Visual Motion Simulator (VMS). Proposed future developments by the authors in cueing algorithms are revealed. The new motion system, the Cockpit Motion Facility (CMF), where the final evaluation of the cueing algorithms will be conducted, is also described.

A method of 3D object recognition and localization in a cloud of points

NASA Astrophysics Data System (ADS)

Bielicki, Jerzy; Sitnik, Robert

2013-12-01

The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.

Power amplifier linearization technique with IQ imbalance and crosstalk compensation for broadband MIMO-OFDM transmitters

NASA Astrophysics Data System (ADS)

Gregorio, Fernando; Cousseau, Juan; Werner, Stefan; Riihonen, Taneli; Wichman, Risto

2011-12-01

The design of predistortion techniques for broadband multiple input multiple output-OFDM (MIMO-OFDM) systems raises several implementation challenges. First, the large bandwidth of the OFDM signal requires the introduction of memory effects in the PD model. In addition, it is usual to consider an imbalanced in-phase and quadrature (IQ) modulator to translate the predistorted baseband signal to RF. Furthermore, the coupling effects, which occur when the MIMO paths are implemented in the same reduced size chipset, cannot be avoided in MIMO transceivers structures. This study proposes a MIMO-PD system that linearizes the power amplifier response and compensates nonlinear crosstalk and IQ imbalance effects for each branch of the multiantenna system. Efficient recursive algorithms are presented to estimate the complete MIMO-PD coefficients. The algorithms avoid the high computational complexity in previous solutions based on least squares estimation. The performance of the proposed MIMO-PD structure is validated by simulations using a two-transmitter antenna MIMO system. Error vector magnitude and adjacent channel power ratio are evaluated showing significant improvement compared with conventional MIMO-PD systems.

Real-Time Cloud, Radiation, and Aircraft Icing Parameters from GOES over the USA

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Nguyen, Louis; Smith, William, Jr.; Young, David; Khaiyer, Mandana; Palikonda, Rabindra; Spangenberg, Douglas; Doelling, Dave; Phan, Dung; Nowicki, Greg

2004-01-01

A preliminary new, physically based method for realtime estimation of the probability of icing conditions has been demonstrated using merged GOES-10 and 12 data over the continental United States and southern Canada. The algorithm produces pixel-level cloud and radiation properties as well as an estimate of icing probability with an associated intensity rating Because icing depends on so many different variables, such as aircraft size or air speed, it is not possible to achieve 100% success with this or any other type of approach. This initial algorithm, however, shows great promise for diagnosing aircraft icing and putting it at the correct altitude within 0.5 km most of the time. Much additional research must be completed before it can serve as a reliable input for the operational CIP. The delineation of the icing layer vertical boundaries will need to be improved using either the RUC or balloon soundings or ceilometer data to adjust the cloud base height, a change that would require adjustment of the cloud-top altitude also.

Paracousti-UQ: A Stochastic 3-D Acoustic Wave Propagation Algorithm.

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

Preston, Leiph

Acoustic full waveform algorithms, such as Paracousti, provide deterministic solutions in complex, 3-D variable environments. In reality, environmental and source characteristics are often only known in a statistical sense. Thus, to fully characterize the expected sound levels within an environment, this uncertainty in environmental and source factors should be incorporated into the acoustic simulations. Performing Monte Carlo (MC) simulations is one method of assessing this uncertainty, but it can quickly become computationally intractable for realistic problems. An alternative method, using the technique of stochastic partial differential equations (SPDE), allows computation of the statistical properties of output signals at a fractionmore » of the computational cost of MC. Paracousti-UQ solves the SPDE system of 3-D acoustic wave propagation equations and provides estimates of the uncertainty of the output simulated wave field (e.g., amplitudes, waveforms) based on estimated probability distributions of the input medium and source parameters. This report describes the derivation of the stochastic partial differential equations, their implementation, and comparison of Paracousti-UQ results with MC simulations using simple models.« less

A closed-form solution to tensor voting: theory and applications.

PubMed

Wu, Tai-Pang; Yeung, Sai-Kit; Jia, Jiaya; Tang, Chi-Keung; Medioni, Gérard

2012-08-01

We prove a closed-form solution to tensor voting (CFTV): Given a point set in any dimensions, our closed-form solution provides an exact, continuous, and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation. Using CFTV, we prove the convergence of tensor voting on a Markov random field (MRF), thus termed as MRFTV, where the structure-aware tensor at each input site reaches a stationary state upon convergence in structure propagation. We then embed structure-aware tensor into expectation maximization (EM) for optimizing a single linear structure to achieve efficient and robust parameter estimation. Specifically, our EMTV algorithm optimizes both the tensor and fitting parameters and does not require random sampling consensus typically used in existing robust statistical techniques. We performed quantitative evaluation on its accuracy and robustness, showing that EMTV performs better than the original TV and other state-of-the-art techniques in fundamental matrix estimation for multiview stereo matching. The extensions of CFTV and EMTV for extracting multiple and nonlinear structures are underway.

Power flow prediction in vibrating systems via model reduction

NASA Astrophysics Data System (ADS)

Li, Xianhui

This dissertation focuses on power flow prediction in vibrating systems. Reduced order models (ROMs) are built based on rational Krylov model reduction which preserve power flow information in the original systems over a specified frequency band. Stiffness and mass matrices of the ROMs are obtained by projecting the original system matrices onto the subspaces spanned by forced responses. A matrix-free algorithm is designed to construct ROMs directly from the power quantities at selected interpolation frequencies. Strategies for parallel implementation of the algorithm via message passing interface are proposed. The quality of ROMs is iteratively refined according to the error estimate based on residual norms. Band capacity is proposed to provide a priori estimate of the sizes of good quality ROMs. Frequency averaging is recast as ensemble averaging and Cauchy distribution is used to simplify the computation. Besides model reduction for deterministic systems, details of constructing ROMs for parametric and nonparametric random systems are also presented. Case studies have been conducted on testbeds from Harwell-Boeing collections. Input and coupling power flow are computed for the original systems and the ROMs. Good agreement is observed in all cases.

Sensor selection cost optimisation for tracking structurally cyclic systems: a P-order solution

NASA Astrophysics Data System (ADS)

Doostmohammadian, M.; Zarrabi, H.; Rabiee, H. R.

2017-08-01

Measurements and sensing implementations impose certain cost in sensor networks. The sensor selection cost optimisation is the problem of minimising the sensing cost of monitoring a physical (or cyber-physical) system. Consider a given set of sensors tracking states of a dynamical system for estimation purposes. For each sensor assume different costs to measure different (realisable) states. The idea is to assign sensors to measure states such that the global cost is minimised. The number and selection of sensor measurements need to ensure the observability to track the dynamic state of the system with bounded estimation error. The main question we address is how to select the state measurements to minimise the cost while satisfying the observability conditions. Relaxing the observability condition for structurally cyclic systems, the main contribution is to propose a graph theoretic approach to solve the problem in polynomial time. Note that polynomial time algorithms are suitable for large-scale systems as their running time is upper-bounded by a polynomial expression in the size of input for the algorithm. We frame the problem as a linear sum assignment with solution complexity of ?.

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

PubMed

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fast emission estimates in China and South Africa constrained by satellite observations

NASA Astrophysics Data System (ADS)

Mijling, Bas; van der A, Ronald

2013-04-01

Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for emerging economies such as China and South Africa, where rapid economic growth change emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. However, constraining emissions from observations of concentrations is computationally challenging. Within the GlobEmission project (part of the Data User Element programme of ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China and South Africa, using the CHIMERE chemical transport model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission inventories result in a better agreement between observations and simulations of air pollutant concentrations, facilitating improved air quality forecasts.

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds.

PubMed

Kim, Keo-Sik; Seo, Jeong-Hwan; Song, Chul-Gyu

2011-08-10

Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised. Twelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J1, 3, J3, 3, S1, 2, S2, 1, S2, 2, S3, 2), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN. As a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively. The jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

PubMed

Güntürkün, Rüştü

2010-08-01

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

Robust reinforcement learning.

PubMed

Morimoto, Jun; Doya, Kenji

2005-02-01

This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both offline learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H(infinity) control, we consider a differential game in which a "disturbing" agent tries to make the worst possible disturbance while a "control" agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H(infinity) control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.

The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms

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

Michel, D.; Jimenez, C.; Miralles, D. G.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared tomore » tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements ( R 2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower ( R 2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. In conclusion, an extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.« less

The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms

DOE PAGES

Michel, D.; Jimenez, C.; Miralles, D. G.; ...

2016-02-23

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared tomore » tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements ( R 2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower ( R 2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. In conclusion, an extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.« less

The ESA Cloud CCI project: Generation of Multi Sensor consistent Cloud Properties with an Optimal Estimation Based Retrieval Algorithm

NASA Astrophysics Data System (ADS)

Jerg, M.; Stengel, M.; Hollmann, R.; Poulsen, C.

2012-04-01

The ultimate objective of the ESA Climate Change Initiative (CCI) Cloud project is to provide long-term coherent cloud property data sets exploiting and improving on the synergetic capabilities of past, existing, and upcoming European and American satellite missions. The synergetic approach allows not only for improved accuracy and extended temporal and spatial sampling of retrieved cloud properties better than those provided by single instruments alone but potentially also for improved (inter-)calibration and enhanced homogeneity and stability of the derived time series. Such advances are required by the scientific community to facilitate further progress in satellite-based climate monitoring, which leads to a better understanding of climate. Some of the primary objectives of ESA Cloud CCI Cloud are (1) the development of inter-calibrated radiance data sets, so called Fundamental Climate Data Records - for ESA and non ESA instruments through an international collaboration, (2) the development of an optimal estimation based retrieval framework for cloud related essential climate variables like cloud cover, cloud top height and temperature, liquid and ice water path, and (3) the development of two multi-annual global data sets for the mentioned cloud properties including uncertainty estimates. These two data sets are characterized by different combinations of satellite systems: the AVHRR heritage product comprising (A)ATSR, AVHRR and MODIS and the novel (A)ATSR - MERIS product which is based on a synergetic retrieval using both instruments. Both datasets cover the years 2007-2009 in the first project phase. ESA Cloud CCI will also carry out a comprehensive validation of the cloud property products and provide a common data base as in the framework of the Global Energy and Water Cycle Experiment (GEWEX). The presentation will give an overview of the ESA Cloud CCI project and its goals and approaches and then continue with results from the Round Robin algorithm comparison exercise carried out at the beginning of the project which included three algorithms. The purpose of the exercise was to assess and compare existing cloud retrieval algorithms in order to chose one of them as backbone of the retrieval system and also identify areas of potential improvement and general strengths and weaknesses of the algorithm. Furthermore the presentation will elaborate on the optimal estimation algorithm subsequently chosen to derive the heritage product and which is presently further developed and will be employed for the AVHRR heritage product. The algorithm's capabilities to coherently and simultaneously process all radiative input and yield retrieval parameters together with associated uncertainty estimates will be presented together with first results for the heritage product. In the course of the project the algorithm is being developed into a freely and publicly available community retrieval system for interested scientists.

An online input force time history reconstruction algorithm using dynamic principal component analysis

NASA Astrophysics Data System (ADS)

Prawin, J.; Rama Mohan Rao, A.

2018-01-01

The knowledge of dynamic loads acting on a structure is always required for many practical engineering problems, such as structural strength analysis, health monitoring and fault diagnosis, and vibration isolation. In this paper, we present an online input force time history reconstruction algorithm using Dynamic Principal Component Analysis (DPCA) from the acceleration time history response measurements using moving windows. We also present an optimal sensor placement algorithm to place limited sensors at dynamically sensitive spatial locations. The major advantage of the proposed input force identification algorithm is that it does not require finite element idealization of structure unlike the earlier formulations and therefore free from physical modelling errors. We have considered three numerical examples to validate the accuracy of the proposed DPCA based method. Effects of measurement noise, multiple force identification, different kinds of loading, incomplete measurements, and high noise levels are investigated in detail. Parametric studies have been carried out to arrive at optimal window size and also the percentage of window overlap. Studies presented in this paper clearly establish the merits of the proposed algorithm for online load identification.

Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.

PubMed

Rezaee, Kh; Haddadnia, J

2013-09-01

Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters' number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

Inverse-consistent rigid registration of CT and MR for MR-based planning and adaptive prostate radiation therapy

NASA Astrophysics Data System (ADS)

Rivest-Hénault, David; Dowson, Nicholas; Greer, Peter; Dowling, Jason

2014-03-01

MRI-alone treatment planning and adaptive MRI-based prostate radiation therapy are two promising techniques that could significantly increase the accuracy of the curative dose delivery processes while reducing the total radiation dose. State-of-the-art methods rely on the registration of a patient MRI with a MR-CT atlas for the estimation of pseudo-CT [5]. This atlas itself is generally created by registering many CT and MRI pairs. Most registration methods are not symmetric, but the order of the images influences the result [8]. The computed transformation is therefore biased, introducing unwanted variability. This work examines how much a symmetric algorithm improves the registration. Methods: A robust symmetric registration algorithm is proposed that simultaneously optimises a half space transform and its inverse. During the registration process, the two input volumetric images are transformed to a common position in space, therefore minimising any computational bias. An asymmetrical implementation of the same algorithm was used for comparison purposes. Results: Whole pelvis MRI and CT scans from 15 prostate patients were registered, as in the creation of MR-CT atlases. In each case, two registrations were performed, with different input image orders, and the transformation error quantified. Mean residuals of 0.63±0.26 mm (translation) and (8.7±7.3) × 10--3 rad (rotation) were found for the asymmetrical implementation with corresponding values of 0.038±0.039 mm and (1.6 ± 1.3) × 10--3 rad for the proposed symmetric algorithm, a substantial improvement. Conclusions: The increased registration precision will enhance the generation of pseudo-CT from MRI for atlas based MR planning methods.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

An improved state-parameter analysis of ecosystem models using data assimilation

USGS Publications Warehouse

Chen, M.; Liu, S.; Tieszen, L.L.; Hollinger, D.Y.

2008-01-01

Much of the effort spent in developing data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be lowered if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) by combining ensemble Kalman filter with kernel smoothing technique. SEnKF has following characteristics: (1) to estimate simultaneously the model states and parameters through concatenating unknown parameters and state variables into a joint state vector; (2) to mitigate dramatic, sudden changes of parameter values in parameter sampling and parameter evolution process, and control narrowing of parameter variance which results in filter divergence through adjusting smoothing factor in kernel smoothing algorithm; (3) to assimilate recursively data into the model and thus detect possible time variation of parameters; and (4) to address properly various sources of uncertainties stemming from input, output and parameter uncertainties. The SEnKF is tested by assimilating observed fluxes of carbon dioxide and environmental driving factor data from an AmeriFlux forest station located near Howland, Maine, USA, into a partition eddy flux model. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and others, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the simultaneous parameter estimation procedure significantly improves model predictions. Results also show that the SEnKF can dramatically reduce the variance in state variables stemming from the uncertainty of parameters and driving variables. The SEnKF is a robust and effective algorithm in evaluating and developing ecosystem models and in improving the understanding and quantification of carbon cycle parameters and processes. ?? 2008 Elsevier B.V.

Real-Time Monitoring and Prediction of the Pilot Vehicle System (PVS) Closed-Loop Stability

NASA Astrophysics Data System (ADS)

Mandal, Tanmay Kumar

Understanding human control behavior is an important step for improving the safety of future aircraft. Considerable resources are invested during the design phase of an aircraft to ensure that the aircraft has desirable handling qualities. However, human pilots exhibit a wide range of control behaviors that are a function of external stimulus, aircraft dynamics, and human psychological properties (such as workload, stress factor, confidence, and sense of urgency factor). This variability is difficult to address comprehensively during the design phase and may lead to undesirable pilot-aircraft interaction, such as pilot-induced oscillations (PIO). This creates the need to keep track of human pilot performance in real-time to monitor the pilot vehicle system (PVS) stability. This work focused on studying human pilot behavior for the longitudinal axis of a remotely controlled research aircraft and using human-in-the-loop (HuIL) simulations to obtain information about the human controlled system (HCS) stability. The work in this dissertation is divided into two main parts: PIO analysis and human control model parameters estimation. To replicate different flight conditions, this study included time delay and elevator rate limiting phenomena, typical of actuator dynamics during the experiments. To study human control behavior, this study employed the McRuer model for single-input single-output manual compensatory tasks. McRuer model is a lead-lag controller with time delay which has been shown to adequately model manual compensatory tasks. This dissertation presents a novel technique to estimate McRuer model parameters in real-time and associated validation using HuIL simulations to correctly predict HCS stability. The McRuer model parameters were estimated in real-time using a Kalman filter approach. The estimated parameters were then used to analyze the stability of the closed-loop HCS and verify them against the experimental data. Therefore, the main contribution of this dissertation is the design of an unscented Kalman filter-based algorithm to estimate McRuer model parameters in real time, and a framework to validate this algorithm for single-input single-output manual compensatory tasks to predict instabilities.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

Fast self contained exponential random deviate algorithm

NASA Astrophysics Data System (ADS)

Fernández, Julio F.

1997-03-01

An algorithm that generates random numbers with an exponential distribution and is about ten times faster than other well known algorithms has been reported before (J. F. Fernández and J. Rivero, Comput. Phys. 10), 83 (1996). That algorithm requires input of uniform random deviates. We now report a new version of it that needs no input and is nearly as fast. The only limitation we predict thus far for the quality of the output is the amount of computer memory available. Performance results under various tests will be reported. The algorithm works in close analogy to the set up that is often used in statistical physics in order to obtain the Gibb's distribution. N numbers, that are are stored in N registers, change with time according to the rules of the algorithm, keeping their sum constant. Further details will be given.

Evaluation of improved land use and canopy representation in ...

EPA Pesticide Factsheets

Biogenic volatile organic compounds (BVOC) participate in reactions that can lead to secondarily formed ozone and particulate matter (PM) impacting air quality and climate. BVOC emissions are important inputs to chemical transport models applied on local to global scales but considerable uncertainty remains in the representation of canopy parameterizations and emission algorithms from different vegetation species. The Biogenic Emission Inventory System (BEIS) has been used to support both scientific and regulatory model assessments for ozone and PM. Here we describe a new version of BEIS which includes updated input vegetation data and canopy model formulation for estimating leaf temperature and vegetation data on estimated BVOC. The Biogenic Emission Landuse Database (BELD) was revised to incorporate land use data from the Moderate Resolution Imaging Spectroradiometer (MODIS) land product and 2006 National Land Cover Database (NLCD) land coverage. Vegetation species data are based on the US Forest Service (USFS) Forest Inventory and Analysis (FIA) version 5.1 for 2002–2013 and US Department of Agriculture (USDA) 2007 census of agriculture data. This update results in generally higher BVOC emissions throughout California compared with the previous version of BEIS. Baseline and updated BVOC emission estimates are used in Community Multiscale Air Quality (CMAQ) Model simulations with 4 km grid resolution and evaluated with measurements of isoprene and monoterp