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Byline: Kannan. Sridharan, Chenna. Reddy Sannala, Surulivelrajan. Mallayasamy, Ayyappa. Chaturvedula, Prashant. Kadam, Nivrutti. Hase, Akash. Shukla, Nithya. Gogtay, Urmila. Thatte OBJECTIVES: We attempted to develop a population pharmacokinetic model for [...]

Currently no information about different shots is used in the H.264/AVC video coding standard. This kind of information can help us choose more optimally the size of the Group of Pictures (GOPs) used for encoding of video content. In this paper, we initially propose an MPEG-7 descriptor based shot detection technique with low computational cost for H.264/AVC. Then we propose an adaptive initial quantization parameter (QP) estimation method for each shot based on modeling, training according to the content of video sequences and the shot detection method of our previous step. This two step architecture can help us reduce the bit rate and PSNR fluctuation when video sequences have multiple shots. Our proposed scheme outperforms the rate control of the H.264/AVC significantly in terms of reducing the average bit rate fluctuation (variance) by 8.6%-99% and the average PSNR fluctuation (variance) by 5%-99% between shots. Experimental results also demonstrate that the proposed algorithm can achieve similar or even better Rate Distortion (R-D) performance than standard Rate Control algorithms. It is also applicable in computationally and memory restricted devices since it needs maximum 2 frames buffer space for MPEG-7 descriptor calculation, while the average amount of extra processing is only about 5.8% of the total CPU cycles. Index Terms--H264/AVC, initial quantization parameter estimation, MPEG-7, rate control, scene detection.

In this paper, we propose a novel approach of simultaneous localized feature selection and model detection for unsupervised learning. In our approach, local feature saliency, together with other parameters of Gaussian mixtures, are estimated by Bayesian variational learning. Experiments performed on both synthetic and real-world data sets demonstrate that our approach is superior over both global feature selection and subspace clustering methods. Index Terms--Unsupervised, localized, feature selection, Bayesian.

Tracer kinetics models are commonly employed to estimate physiological parameters related to blood transport and capillary-tissue exchange. A priori identifiability addresses the question of whether the parameters in a model can be uniquely determined from a given experiment. It has been previously shown that the one- and two-compartment distributed parameter (DP) models are nonidentifiable from tracer outflow data obtained by arterial-venous sampling. In this correspondence, we show that both DP models are a priori identifiable from residual tracer data obtained by dynamic contrast-enhanced (DCE) imaging. We list the various parameters of the DP models that can be uniquely estimated from DCE imaging data, and discuss this seemingly different outcome for DCE imaging experiments, as compared with the arterial-venous sampling experiments. Index Terms--Dynamic contrast-enhanced imaging, identifiability, parameter estimation, tracer kinetics modeling.

A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semi-Markov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating transition data by introducing multiple factors in the model. JEL classification: C15; C33; C41; C43; G11; G21 Keywords: Unobserved components; Credit cycles; Duration model; Generator matrix; Monte Carlo likelihood

System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue. Index Terms--Initial conditions, oculomotor plant, output error, parameter estimation, state-space.

Excitation system is key element in the dynamic performance of electric power systems, accurate excitation models are of great importance in simulating and investigating the power system transient phenomena. Parameter identification of the Brushless excitation system was presented. First a block diagram for the EXS parameter was proposed based on the documents and maps in the power station. To identify the parameters of this model, a test procedure to obtain step response, was presented. Using the Genetic Algorithm with the Matlab-software it was possible to identify all the necessary parameters of the model. Using the same measured input signals the response from the standard model showed nearly the same behavior as the excitation system. Key words: Excitation system, parameter estimation, genetic algorithm method, model validation, INTRODUCTION Power system small signal, transient and dynamic stability studies are only as accurate as the underlying models used in the computer analysis. The validity of the results of these [...]

A sample-by-sample dual scheme using [H.sub.infinity] estimators is proposed for solving the joint signal and parameter estimation for linear state-space models. Findings reveal the capability of the scheme in minimizing the worst possible effects of the disturbances on the estimation error.

The neutral theory of S. P. Hubbell postulates a two-scale hierarchical framework consisting of a metacommunity following the speciation--drift equilibrium characterized by the 'biodiversity number' [theta], and local communities following the migration--drift equilibrium characterized by the 'migration rate' m (or the 'fundamental dispersal number' I). While Etienne's sampling formula allows simultaneous estimation of [theta] and m from a single sample of a local community, its applicability to a network of (rather small) samples is questionable. We define here an alternative two-stage approach estimating [theta] from an adequate subset of the individuals sampled in the field (using Ewens' sampling formula) and m from community samples (using Etienne's sampling formula). We compare its results with the simultaneous estimation of [theta] and m (one-stage estimation), for simulated neutral samples and for 50 1-ha plots of evergreen forest in South India. The one-stage approach exhibits problems of bias and of poor differentiability between high-[theta], low-m and low-[theta], high-m solution domains. Conversely, the two-stage approach yielded reasonable estimates and is to be preferred when several small, scattered plots are available instead of a single large one. Key words: community ecology; dispersal limitation; Etienne's sampling formula; Ewens' sampling formula: India; neutral theory; sampling; wet evergreen tropical forest.

A report on the development of a model based on an exponential cumulative damage approach, exhibited a similar structural form of the other models so that previous theory for cumulative damage models can be utilized to find parameter estimates is presented.

In a phase controlled rectifier, the fastest response can be achieved by predictive current control without any overshoot. However, any error in the load parameters can cause steady state errors between a reference current and the feedback current because this current control scheme depends on the mathematical relationships between the output current, the firing angle, and the power source voltage. Thus, in the predictive current control, an accurate parameter is necessary for attaining a zero steady-state error. In this paper, the load resistance and inductance are estimated using the least square method of an online parameter calculation based on digitally sampled instantaneous voltage and currents. For the careful sampling of data and the reduction of the calculation time, a new timer-based gating algorithm is proposed and explained in detail. Using the proposed algorithm, the fastest current control performance in the transient state is obtained by the online correction of load parameters in various simulations and experimental results. Index Terms--Parameter estimation, phase controlled rectifier, predictive current control.

The parameter estimation problem is studied for LOFAR calibration by incorporating constraining signal models. There are limitations though justifiable constraint can be applied to yield viable solutions for calibration.

This paper describes a new Fourier-based sliding window method for estimating the damping of exponentially decaying modes that occur in power networks as a result of electric disturbances. The key innovation in the new method is the use of multiple orthogonal sliding windows rather than just a single pair of sliding windows. The use of these multiple orthogonal windows allows least-squares averaging strategies to be used, enabling lower variance estimates to be obtained as a result. A statistical analysis is provided, and simulations are presented to illustrate the effectiveness of the new method. The technique is also applied to real power system data. Index Terms--Interconnected power systems, least-squares methods, parameter estimation, power system monitoring, power system transients, singular value decomposition.

An inverse approach based on an optimization technique is proposed to characterize a fiber Bragg grating (FBG) and the strain gauge factor (GF) when the FBG is bonded on a structure. By bonding an FBG on a substrate and simply straining this FBG into a chirped fiber Bragg grating with a predesignated strain, the proposed method, based on an optimization technique, can be used to reconstruct seven parameters of the FBG from the corresponding reflective spectrum. The parameters identified are the length of an FBG, the grating period, the average refractive index, the index modulation, the apodization coefficient, the starting point bonded on the plate, and the strain GF. The information from the predesignated strain, as well as the measured reflective spectrum, is used as the objective function during the optimal search. As a result, the design sensitivity for the optimal search is much improved compared with the design sensitivity when only the reflective spectrum is used. In particular, the strain GF, which depends on the adhesive, the bonding layer characteristics, etc., can be determined in order to provide a reference for an FBG used as a strain sensor. Results from numerical simulations and experiments show that seven parameters of an FBG can be obtained accurately and efficiently.

A report on the formulation of a theory for the roots of angiosperm species with a set of loading conditions that is used to analyze interplay between mechanical and metabolic behavior in plants and to study elastodynamics of plant tissue is presented.

We present theoretical explanations and show through simulation that the individual admixture proportion estimates obtained by using ancestry informative markers should be seen as an error-contaminated measurement of the underlying individual ancestry proportion. These estimates can be used in structured association tests as a control variable to limit type I error inflation or reduce loss of power due to population stratification observed in studies of admixed populations. However, the inclusion of such error-containing variables as covariates in regression models can bias parameter estimates and reduce ability to control for the confounding effect of admixture in genetic association tests. Measurement error correction methods offer a way to overcome this problem but require an a priori estimate of the measurement error variance. We show how an tipper bound of this variance can be obtained, present four measurement error correction methods that are applicable to this problem, and conduct a simulation study to compare their utility in the case where the admixed population results from the intermating between two ancestral populations. Out results show that the quadratic measurement error correction (QMEC) method performs better than the other methods and maintains the type I error to its nominal level.

Subspace-based schemes for the estimation of poles of a sum of damped and delayed sinusoids are presented. Classical subspace-based methods are not suited to handle signals with varying time supports and solutions based on the approximation of a partially structured Hankel-type tensor on which the data are mapped are proposed.

Field orientation techniques without flux measurements depend on the parameters of the motor, particularly on the rotor resistance or rotor time constant (for rotor field orientation). Since these parameters change continuously as a function of temperature, it is important that the value of rotor resistance is continuously estimated online. A fourth-order sliding-mode flux observer is developed in this paper. Two sliding surfaces representing combinations of estimated flux and current errors are used to enforce the flux and current estimates to their real values. Switching functions are used to drive the sliding surfaces to zero. The equivalent values of the switching functions (low-frequency components) are proven to be the rotor resistance and the inverse of the rotor time constant. This property is used to simultaneously estimate the rotor resistance and the inverse of the time constant without prior knowledge of either the rotor resistance or the magnetizing inductance. Simulations and experimental results prove the validity of the proposed approach. Index Terms--Field-oriented control, induction motor, online parameter estimation, sliding mode.

Proximal tubular pressure shows periodic self-sustained oscillations in normotensive rats but highly irregular fluctuations in spontaneously hypertensive rats (SHR). Although we have suggested that the irregular fluctuations in SHR represent low-dimensional deterministic chaos in tubuloglomerular feedback (TGF), they could also arise from other mechanisms, such as intrinsic instabilities in preglomerular vessels or inputs from neighboring, coupled nephrons. To test this possibility, we applied a parameter estimation procedure to a model of TGF, where a stochastic process was added to represent mechanisms not included explicitly in the model. In its deterministic version, the model can have chaotic dynamics arising from TGF. The model introduces random fluctuations into a parameter that determines the gain of TGF. The model shows a rich variety of dynamics ranging from low-dimensional deterministic oscillations and chaos to high-dimensional random fluctuations. To lit the data from normotensive rats, the model must introduce only a small variation in the feedback gain, and its estimates of that gain agree well with experimental values. These results support the use of the deterministic model of nephron dynamics in normotensive rats. In contrast, the irregular tubular pressure fluctuations in SHR were best described by a model dominated by random parameter fluctuations. The results point to the failure of simple mathematical models of nephron dynamics adequately to describe processes that are important for the irregular tubular pressure fluctuations and the need to consider other factors, such as differences in vascular function or nephron-nephron interactions, in further work on this problem. stochastic processes; spectral density; nephron; tubuloglomerular feedback mechanism; deterministic chaos

This paper presents a novel approach for estimating the parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP) problem in which both a disparity map and MRF parameters are estimated from the stereo pair itself. We present an iterative algorithm for the MAP estimation that alternates between estimating the parameters while fixing the disparity map and estimating the disparity map while fixing the parameters. The estimated parameters include robust truncation thresholds for both data and neighborhood terms, as well as a regularization weight. The regularization weight can be either a constant for the whole image or spatially-varying, depending on local intensity gradients. In the latter case, the weights for intensity gradients are also estimated. Our approach works as a wrapper for existing stereo algorithms based on graph cuts or belief propagation, automatically tuning their parameters to improve performance without requiring the stereo code to be modified. Experiments demonstrate that our approach moves a baseline belief propagation stereo algorithm up six slots in the Middlebury rankings. Index Terms--Stereo matching, parameter setting, Markov Random Fields.

Abstract--Nowadays, more engineering systems are using some kind of Artificial Intelligence (AI) for the development of their processes. Some well-known AI techniques include artificial neural nets, fuzzy inference systems, and [...]

Motivated by the estimation of spatio-temporal events with cheap, simple sensors, we consider the problem of estimation of a parameter [theta] of a signal s(x; [theta]) corrupted by noise assuming that only 1-bit precision dithered quantized samples are available. An estimate that does not require the knowledge of the dither signal and the noise distribution is proposed, and it is analyzed in detail under variety of nonidealities. The consistency and asymptotic normality of the estimate is established for deterministic and random sampling, imprecise knowledge of sampling locations, Gaussian and non-Gaussian noise (with possibly infinite variance), a wide class of dither distributions, and under erroneous transmission of the binary observations via binary-symmetric channels (BSCs). It is also shown that if approximation to the log-likelihood equation in the full precision case yields a good estimate, then there is a corresponding good estimate based on 1-bit dithered samples. The proposed estimate requires no more computation than the maximum-likelihood estimate for the full precision case and suffers only a logarithmic rate loss compared to the full precision case when uniform dithering is used. It is shown that uniform dithering leads to the best rate among a broad class of dither distributions. A condition under which no dithering leads to a better estimate is also given. Index Terms--1-bit dithered quantization, sensor networks, signal parameter estimation.

A successive parameter estimation algorithm based on chirplet transform and an automatic echo windowing method to evaluate the echo parameters like amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate is described. The proposed algorithm provides a single decomposition method along with factors useful for data compression, pattern recognition, signal deconvolution, target detection, object sizing, and material characterization and the provides precise echo estimation leading to signal-to-noise (SNR) enhancements.

A cohesive interface element is presented for the finite element analysis of crack growth in thin specimens. In this work, the traditional cohesive interface model is extended in handle cracks in the context of three-dimensional shell elements. In addition to the traction-displacement last; a bending moment-rotation relation is included to transmit the moment and describe the initiation and propagation of cracks growing through the thickness of the shell elements. Since crack initiation and evolution are a natural outcome of the cohesive zone model without the need of any ad hoc fracture criterion, this model results in automatic prediction of fracture. In particular, this paper will focus on cases involving mode I/III fracture and bending, typical of complex cases existing in industrial applications in which thin-walled structures are subjected to extreme loading conditions (e.g. crashworthiness analysis). Finally, we will discuss how the three-dimensional effects near the crack front may affect the determination of the cohesive parameters to be used with this model. [DOI: 10.1115/1.2173286]

Nonparametric approaches for estimating the number of unique sequences in a biological tissue, cDNA library or amplified cDNA are considered and by introducing moments of species' abundance in a population, the relative abundance of species is analyzed. It is found that the lower bound estimator yields a big bias when applied to gene expression data, which has relatively large coefficients of variation and small sample sizes.

The correlation analysis based methods are not suitable for identifying parameters of nonstationary autoregressive (AR), moving average (MA), and ARMA systems. By using estimated residuals in place of unmeasurable noise, terms in information sector or matrix, a least squares based and gradient based algorithms are developed, and the consistency of the proposed algorithms without assuming noise stationarity, ergodicity, or existence of higher order moments are established.

The statistical problem of estimating the bandwidth parameter of a Gauss-Markov (GM) process from a realization of fixed and finite duration T at selectable sampling interval delta is addressed. It is shown that because of finite and fixed T, a second-order expansion provides better bias and variance results in parameter estimation.

A study assessing the empirical validity of Zipf's Law for cities using new data on seventy-three countries and two estimation methods, OLS and the Hill estimator is presented.

A profit maximization method based on semiparametric generalized additive models is presented to estimate customers' responses to mails or catalogs to maximize the firm's profit.

An unbiased, maximum-likelihood (ML), channel parameter-estimation algorithm for direct-sequence spread-spectrum systems with strong interference is discussed in this paper. The algorithm includes correcting terms to the extended Kalman filter (EKF) based on the gradient of the negative log-likelihood function of the output of a conventional matched filter. By an asymptotic analysis, the algorithm is shown to determine the actual parameters. A complete implementation of the algorithm is given, and its transient behavior is examined by computer simulations. Results show the ML algorithm, albeit optimal in the sense of unbiased parameter estimation, is less robust than the modified EKF described in the first reference. Index Terms--Identification, interference suppression, Kalman filtering, parameter estimation, pseudonoise coded communications.

A parameter estimation scheme with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory for the general MIMO Takagi-Sugeno (T-S) fuzzy models. The parameters of the Takagi-Sugeno fuzzy models can be estimated by observing the behavior of the system and with the online parameter estimator, any type of fuzzy controllers works adaptively to the parameter perturbation. In order to show the applicability of the proposed estimator, an existing fuzzy state feedback controller is adopted and indirect adaptive fuzzy control design with the proposed estimator is shown. From the numerical simulations and experiments, it is shown that the derived adaptive law works for the estimation model to follows the parameterized plant model and the overall control system has robustness to the parameter perturbation. Index Terms--Adaptive control, fuzzy control, fuzzy model, online estimation.

This paper describes an efficient and robust approach for the computer diagnosis and tuning of RF and microwave filters relying upon model-based parameter estimation (MBPE) and multilevel optimization. The frequency sampled S-parameters are obtained from the measurement, and then an MBPE procedure based on adaptive sampling is employed to approximate the frequency-domain behavior of S-parameters in terms of rational functions. This approach uses a reduced-order system model. The effect of measurement noise is also considered. The approach is applied to coupled resonator filters that are modeled by a general equivalent circuit. The loss of each resonator is included in the model by a series resistor. A simple and efficient error function is used to reduce the computational effort of the optimization while improving the speed and robustness of diagnosis process for lossy filters. This approach can be applied to many classes of filters. The proposed approach is demonstrated through numerical examples and application to the manufactured filter. Index Terms--Adaptive sampling, diagnosis, microwave filters, model reduction, parameter estimation, tuning.

This paper focuses on the systematic development of a parametric approach for classifying averaged event-related potentials (ERPs) recorded from multiple channels. It is shown that the parameters of the averaged ERP ensemble can be estimated directly from the parameters of the single-trial ensemble, thus, making it possible to design a class of parametric classifiers without having to collect a prohibitively large number of single-trial ERPs. An approach based on random sampling without replacement is developed to generate a large number of averaged ERP ensembles in order to evaluate the performance of a classifier. A two-class ERP classification problem is considered and the parameter estimation methods are applied to independently design a Gaussian likelihood ratio classifier for each channel. A fusion rule is formulated to classify an ERP using the classification results from all the channels. Experiments using real and simulated ERPs are designed to show that, through the approach developed, parametric classifiers can be designed and evaluated even when the number of averaged ERPs does not exceed the dimension of the ERP vector. Additionally, it is shown that the performance of a majority rule fusion classifier is consistently superior to the rule that selects a single best channel. Index Terms--Event-related potentials, parameter estimation, parametric classification, signal averaging.

Parameter estimation is used to solve infinite-horizon dynamic models at low cost.

In this note, we consider the problem of motion and shape estimation using a camera and a laser range finder. The object considered is a plane which is undergoing a Riccati motion. The camera observes features on the moving plane perspectively. The range finder camera is capable of obtaining the range of the plane along a given "laser plane," which can either be kept fixed or can be altered in time. Finally, we assume that the identification is carried out as soon as the visual and range data is available or alter a suitable temporal integration. In each of these various cases, we derive-to what extent motion and shape parameters are identifiable and characterize the results as orbit of a suitable group. The note does not emphasize any specific choice of algorithms. Index Terms--Affine motion, laser range finder, machine vision, parameter identification, perspective dynamical system, Riccati motion, rigid motion.

This paper analyzes the numerical identifiability of the electrical parameters of induction machines. Formulations of the single and double-cage induction machine, with and without core-losses in both models, are developed. The impossibility of identifying all the parameters of these models when only external measurements (voltage, current, speed and torque) are used is shown. One proposed solution is the formulation of machine equations by using the minimum number of parameters (which are identifiable parameters). As an application example, the parameters of a double-cage induction machine are identified using steady-state measurements corresponding to different angular speeds. Index Terms--Induction machine, numerical identifiability, parameter estimation, parameter indetermination.

A study of the parametrization using compounds in the data set that do not contain bond between SI and O at the HF/MIDI!6D,HF/6-31G*,HF/6-31(sub +)G*,HF/cc-pVDZ,BPW91/MIDI!,BPW91/MIDI!6D,BPW91/DVZP,B3LYP/MIDI!,AMI,and PM3 levels of theory is presented. The results show that the mean unsigned errors over the eight differential free energies of solvation and three absolute free energies for these levels of theory are in the range of 0.48-0.53kcal/mol.

This paper is an empirical analysis of survey data on unemployed men in the Spanish labour force using a new method of estimating semiparametrically a proportional hazard model.

This paper describes a method for computer-controlled tuning of waveguide filters. The tuning algorithm is based on approximate filter network models, which take into account the effects of input/output coupling. Based on measurements of an initial filter design, the approximate network model is then corrected by optimizing the element values such that they minimize the mean square error between the measured and simulated response. The sensitivities of the tuning screws are determined directly from the sensitivities of the element values. Filter tuning is accomplished by gradient optimization of the corrected computer model rather than its physical realization. Only as a final step are the tuning screws of the physical model turned to the position determined by the optimization process. Index Terms--CAD, gradient methods, manufacturing automation, modeling, optimization methods, parameter estimation, tunable filters, waveguide filters.