A parametric modeling of membrane current fluctuations with itsapplication to the estimation of the kinetic properties of single ionicchannels

A parametric autoregressive moving average (ARMA) signal modeling of membrane current fluctuations observed in biomembranes is described. Kinetic properties of single ionic channels contributing to membrane current fluctuations and the parameters of the corresponding ARMA process are explicitly related. The model was shown to be effectively applied to the estimation of the kinetic parameters of single ionic channels. Estimation of the parameters via ARMA signal identification was examined in detail for the basic closed-open scheme and the three-state sequential blocking scheme. The estimation accuracy of this method was theoretically evaluated. Computer simulation revealed the validity of the proposed modeling and the effectiveness of the parametric method for the estimation of kinetic parameters when the model was applied to the estimation. The proposed modeling may form a theoretical base for the parametric analysis of membrane current fluctuations in a variety of kinetic schemes.     

A Novel Technique for the Identification of ARMA Systems Under Very Low Levels of SNR

In this paper, a novel technique for the identification of minimum-phase autoregressive moving average (ARMA) systems from the output observations in the presence of heavy noise is presented. First, starting from the conventional correlation estimator, a simple and accurate ARMA correlation (ARMAC) model in terms of the poles of the ARMA system is presented in a unified manner for white noise and impulse-train excitations. The AR parameters of the ARMA system are then obtained from the noisy observations by developing and using a residue-based least-squares correlation-fitting optimization technique that employs the proposed ARMAC model. As for the estimation of the MA parameters, it is preceded by the application of a new technique intended to reduce the noise present in the residual signal that is obtained by filtering the noisy ARMA signal via the estimated AR parameters. A scheme is then devised whereby the task of MA parameter estimation is transformed into a problem of correlation-fitting of the inverse autocorrelation function corresponding to the noise-compensated residual signal. In order to demonstrate the effectiveness of the proposed method, extensive simulations are performed by considering synthetic ARMA systems of different orders in the presence of additive white noise and the results are compared with those of some of the existing methods. It is shown that the proposed method is capable of estimating the ARMA parameters accurately and consistently with guaranteed stability for signal-to-noise ratio (SNR) levels as low as $-{5}~{hbox {dB}}$ . Simulation results are also provided for the identification of a human vocal-tract system using natural speech signals showing a superior performance of the proposed technique in terms of the power spectral density of the synthesized speech signal.      

Time-domain tests for Gaussianity and time-reversibility

Statistical signal processing algorithms often rely upon Gaussianity and time-reversibility, two important notions related to the probability structure of stationary random signals and their symmetry. Parametric models obtained via second-order statistics (SOS) are appropriate when the available data is Gaussian and time-reversible. On the other hand, evidence of nonlinearity, non-Gaussianity, or time-irreversibility favors the use of higher-order statistics (HOS). In order to validate Gaussianity and time-reversibility, and quantify the tradeoffs between SOS and HOS, consistent, time-domain chi-squared statistical tests are developed. Exact asymptotic distributions are derived to estimate the power of the tests, including a covariance expression for fourth-order sample cumulants. A modification of existing linearity tests in the presence of additive Gaussian noise is discussed briefly. The novel Gaussianity statistic is computationally attractive, leads to a constant-false-alarm-rate test and is well suited for parametric modeling because it employs the minimal HOS lags which uniquely characterize ARMA processes. Simulations include comparisons with an existing frequency-domain approach and an application to real seismic data. Time-reversibility tests are also derived and their performance is analyzed both theoretically and experimentally     

Network traffic prediction based on a new time series model

Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory‐shortening technique and an adaptive‐order selection method originally developed by this study. Compared to the conventional one‐step ahead prediction using traditional Box–Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright © 2005 John Wiley & Sons, Ltd.     

Time-Frequency ARMA Models and Parameter Estimators for Underspread Nonstationary Random Processes

Parsimonious parametric models for nonstationary random processes are useful in many applications. Here, we consider a nonstationary extension of the classical autoregressive moving-average (ARMA) model that we term the time-frequency autoregressive moving-average (TFARMA) model. This model uses frequency shifts in addition to time shifts (delays) for modeling nonstationary process dynamics. The TFARMA model and its special cases, the TFAR and TFMA models, are shown to be specific types of time-varying ARMA (AR, MA) models. They are attractive because of their parsimony for underspread processes, that is, nonstationary processes with a limited time-frequency correlation structure. We develop computationally efficient order-recursive estimators for the TFARMA, TFAR, and TFMA model parameters which are based on linear time-frequency Yule-Walker equations or on a new time-frequency cepstrum. Simulation results demonstrate that the proposed parameter estimators outperform existing estimators for time-varying ARMA (AR, MA) models with respect to accuracy and/or numerical efficiency. An application to the time-varying spectral analysis of a natural signal is also discussed.     

High-Resolution ARMA Estimation of Mixed Spectra

In this paper, we propose a high-resolution autoregressive moving average (ARMA) modeling technique for signals which are a sum of sinusoids embedded in colored noise. The approach is based on a special ARMA model. We show that an approximation to this model can be found through the central solution of Nevanlinna-Pick interpolation. In this context, it can reach a very fine resolution with a special arrangement of filterbank poles. A very efficient iterative algorithm will then be presented to achieve such desired arrangement. We also derive theoretical expressions for the variance of interpolation values for both continuous and mixed spectra for complex poles. Computer simulations show that the approach is very powerful in joint power spectrum and frequency estimation and provides superior performance with respect to traditional techniques.     

Noncooperative target classification using hierarchical modeling ofhigh-range resolution radar signatures

The classification of high-range resolution (HRR) radar signatures using multiscale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a different scale obeys an ARMA process if it is an ARMA process at the observed scale. Then, an algorithm to estimate model parameters and power spectral density function at different scales using model parameters at the observed scale is presented. A feature set composed of spectral peaks is extracted from the estimated spectral density function using multiscale ARMA models. For HRR radar signature classification, multispectral features extracted from five different scales are used, and a minimum distance classifier with multiple prototypes is used to classify HRR data. The multiscale classifier is applied to two HRR radar data sets. Each data set contains 2500 test samples and 2500 training samples in five classes. For both data sets, about 95% of the radar returns are correctly classified     

时间序列建模在卡钻类别判断中的应用研究

为了提高卡钻预测中卡钻类别判断的准确度,以青海地区地热勘探井实钻数据为基础,结合时间序列分析建模方法,提出了一种适合卡钻类别判断的方法。通过时序模型对未来钻井数据进行预测处理,运用Matlab软件对各个ARMA模型做功率谱估计,比较相邻两个ARMA模型的功率谱密度,计算各个参数的功率谱偏差值,进行数值仿真,当某一参数其功率谱偏差值出现明显异常时,则预判断这一时刻可能发生此参数对应类别的卡钻事故。引入多因素时序建模方法,运用SPSS软件做多因素模型,计算主要参数的预测区间,当预测值超出预测区间时,则可以判断发生对应类别的卡钻事故。最终证实,采用此方法能够实现对钻井过程中未来卡钻事故的类别判断,在实际钻井中有较高的可扩展性及应用价值。     

ARMA modeling of fourth-order cumulants and phase estimation

A new procedure is proposed for ARMA modeling of fourth-order cumulants and trispectrum estimation of non-Gaussian stationary random processes. The new procedure is applied to the identification of nonminimum phase systems for both phase and magnitude response estimation. It is demonstrated by means of comprehensive simulation examples that the ARMA approach exhibits improved performance over conventional trispectrum methods. ARMA model order selection criteria based on fourth-order cumulants are presented and their performance evaluated. The computational complexity of the ARMA and conventional trispectrum methods is also examined. The new procedure does not require knowledge of the non-Gaussian distribution.This work was supported by the Office of Naval Research under Contract No. ONR-N00014-86-K-0219.     

Bias compensation for the bearings-only pseudolinear target track estimator

The bearings-only pseudolinear target track estimator is known to suffer from severe bias problems. This paper presents a bias analysis for the pseudolinear estimator and develops a method of bias compensation, resulting in a closed-form reduced-bias pseudolinear estimator. The reduced-bias estimator is then incorporated into an instrumental variable estimator to produce asymptotically unbiased target motion parameter estimates. Unlike batch iterative estimators, the proposed instrumental variable estimator has a closed-from solution and therefore avoids the convergence problems associated with iterative estimators. The performance of the proposed instrumental variable estimator is illustrated by way of simulation examples and is shown to be almost identical to that of the computationally more demanding iterative maximum likelihood estimator.     

Semiblind identification of nonminimum-phase ARMA models via orderrecursion with higher order cumulants

This paper develops a novel identification methodology for nonminimum-phase autoregressive moving average (ARMA) models of which the models' orders are not given. It is based on the third-order statistics of the given noisy output observations and assumed input random sequences. The semiblind identification approach is thereby named. By the order-recursive technique, the model orders and parameters can be determined simultaneously by minimizing well-defined cost functions. At each updated order, the AR and MA parameters are estimated without computing the residual time series (RTS), with the result of decreasing the computational complexity and memory consumption. Effects of the AR estimation error on the MA parameters estimation are also reduced. Theoretical statements and simulations results, together with practical application to the train vibration signals' modeling, illustrate that the method provides accurate estimates of unknown linear models, despite the output measurements being corrupted by arbitrary Gaussian noises of unknown pdf     

Characterization and estimation of two-dimensional ARMA models

A class of finite-order two-dimensional autoregressive moving average (ARMA) is introduced that can represent any process with rational spectral density. In this model the driving noise is correlated and need not be Gaussian. Currently known classes of ARMA models or AR models are shown to be subsets of the above class. The three definitions of Markov property are discussed, and the class of ARMA models are precisely stated which have the noncausal and semicausal Markov property without imposing any specific boundary conditions. Next two approaches are considered to estimate the parameters of a model to fit a given image. The first method uses only the empirical correlations and involves the solution of linear equations. The second method is the likelihood approach. Since the exact likelihood function is difficult to compute, we resort to approximations suggested by the toroidal models. Numerical experiments compare the quality of the two estimation schemes. Finally the problem of synthesizing a texture obeying an ARMA model is considered.     

ARMA spectral estimation of time series with missing observations

The problem of estimating the power spectral density of stationary time series when the measurements are not contiguous is considered. A new autoregressive moving-average (ARMA) method is proposed for this problem, based on nonlinear optimization of a weighted-squared-error criterion. The method can handle either regularly or randomly missing observations. As a special case, the method can handle the problem of missing sample covariances. The computational complexity is modest compared to exact maximum likelihood estimation of the same parameters. The performance of the algorithm is illustrated by some numerical examples and is shown to be statistically efficient in these cases.     

Dependency Structure Applied to Language Modeling for Information Retrieval

In this paper, we propose a new language model, namely, a dependency structure language model, for information retrieval to compensate for the weaknesses of unigram and bigram language models. The dependency structure language model is based on the first‐order dependency model and the dependency parse tree generated by a linguistic parser. So, long‐distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model, where the dependency structure model gives a better performance than recently proposed language models and the Okapi BM25 method, and the dependency structure is more effective than unigram and bigram in language modeling for information retrieval.     

Lumped-circuit model extraction for vias in multilayer substrates

Via interconnects in multilayer substrates, such as chip scale packaging, ball grid arrays, multichip modules, and printed circuit boards (PCB) can critically impact system performance. Lumped-circuit models for vias are usually established from their geometries to better understand the physics. This paper presents a procedure to extract these element values from a partial element equivalent circuit type method, denoted by CEMPIE. With a known physics-based circuit prototype, this approach calculates the element values from an extensive circuit net extracted by the CEMPIE method. Via inductances in a PCB power bus, including mutual inductances if multiple vias are present, are extracted in a systematic manner using this approach. A closed-form expression for via self inductance is further derived as a function of power plane dimensions, via diameter, power/ground layer separation, and via location. The expression can be used in practical designs for evaluating via inductance without the necessity of full-wave modeling, and, predicting power-bus impedance as well as effective frequency range of decoupling capacitors.     

ARMA-cepstrum Recursion Algorithm for the Estimation of the MA Parameters of 2-D ARMA Models

This paper considers the problem of estimating the moving average (MA) parameters of a two-dimensional autoregressive moving average (2-D ARMA) model. To solve this problem, a new algorithm that is based on a recursion relating the ARMA parameters and cepstral coefficients of a 2-D ARMA process is proposed. On the basis of this recursion, a recursive equation is derived to estimate the MA parameters from the cepstral coefficients and the autoregressive (AR) parameters of a 2-D ARMA process. The cepstral coefficients are computed benefiting from the 2-D FFT technique. Estimation of the AR parameters is performed by the 2-D modified Yule–Walker (MYW) equation approach. The development presented here includes the formulation for real-valued homogeneous quarter-plane (QP) 2-D ARMA random fields, where data are propagated using only the past values. The proposed algorithm is computationally efficient especially for the higher-order 2-D ARMA models, and has the advantage that it does not require any matrix inversion for the calculation of the MA parameters. The performance of the new algorithm is illustrated by some numerical examples, and is compared with another existing 2-D MA parameter estimation procedure, according to three performance criteria. As a result of these comparisons, it is observed that the MA parameters and the 2-D ARMA power spectra estimated by using the proposed algorithm are converged to the original ones     

Characterization of video codecs as autoregressive moving averageprocesses and related queueing system performance

A method of characterizing video codec sources in asynchronous transfer mode (ATM) networks as an autoregressive moving average (ARMA) process is described. Measurements of long-term mean and the autocorrelation function of cell interarrival times allow the parameter estimation of the ARMA model. The video source is then described by ARMA model. Furthermore, it is shown that the multiplexed stream of video cells is also an ARMA process. Such a cell stream is then applied to a model of a queuing system to obtain performance measures of the system. Perturbation analysis is then performed on the functional behavior of the queuing system by appropriate perturbation of the model parameters to determine cell waiting time sensitivity due to slight variations of the input process     

Design of Piezoelectric Transformers for Power Converters by Means of Analytical and Numerical Methods

Piezoelectric transformers (PTs) provide several advantages compared to magnetic components, which are higher power density, lower radiated noise, and higher voltage isolation capability. PT must be properly designed to benefit the power converter with the aforementioned advantages. Analytical models are widely used for PT design in order to validate it before constructing the prototype. In this paper, the additional usefulness of finite element analysis (FEA) for PT design is shown. With FEA, it is possible to optimize the PT design not only by maximizing the energy transference but also by cleaning the working frequency range of spurious modes (geometrical 2D/3D effects). Moreover, FEA tools allow the study of other main aspects of the PT design such as manufacturing tolerances or the influence of the fixing layer on PT performance (which is a critical design point). A method for modeling and designing PTs is proposed, combining analytical 1D models and FEA results. The proposed method is validated with measurements of a PT design for a 10-W ac/dc converter prototype for mobile phone battery charger.     

A Compact Envelope-Memory Polynomial for RF Transmitters Modeling With Application to Baseband and RF-Digital Predistortion

In this letter, a compact envelope-memory polynomial based model, suitable for forward and reverse modeling of weakly nonlinear wireless transmitters and power amplifiers (PAs) exhibiting electrical memory effects, is presented. This model is implemented in a complex gain based architecture and takes advantage of the dependency of PA nonlinearity on the magnitude of the input signal. Contrary to conventional memory polynomials, the proposed model can be implemented in baseband, as well as in radio frequency, digital predistorters. A 100-W average power transmitter is used for experimental validation of the forward and reverse models. Both forward and reverse modeling results obtained with the proposed model are comparable to that of the conventional memory polynomial.     

基于ARMA模型的电力系统负荷预测方法研究

采用加权最小二乘法参数估计方法，得到应用于电力系统日负荷预测和月负荷预测的ARMA模型，实验预测结果表明，用ARMA模型进行电力负荷预测是非常有铲的。尤其是采用加权最小二乘估计的ARMA模型，预测精度更高。