An empirical Bayes estimator for in-scale adaptive filtering

A scale-adaptive filtering scheme is developed for underspread channels based on a model of the linear time-varying channel operator as a process in scale. Recursions serve the purpose of adding detail to the filter estimate until a suitable measure of fidelity and complexity is met. Resolution of the channel impulse response associated with its coherence time is naturally modeled over the observation time via a Gaussian mixture assignment on wavelet coefficients. Maximum likelihood, approximate maximum a posteriori (MAP) and posterior mean estimators, as well as associated variances, are derived. Doppler spread estimation associated with the coherence time of the filter is synonymous with model order selection and a MAP estimate is presented and compared with Laplace's approximation and the popular AIC. The algorithm is implemented with conjugate-gradient iterations at each scale, and as the coherence time is recursively decreased, the lower scale estimate serves as a starting point for successive reduced-coherence time estimates. The algorithm is applied to a set of simulated sparse multipath Doppler spread channels, demonstrating the superior MSE performance of the posterior mean filter estimator and the superiority of the MAP Doppler spread stopping rule.     

Regularized adaptive long autoregressive spectral analysis

This paper is devoted to adaptive long autoregressive spectral analysis when i) very few data are available and ii) information does exist beforehand concerning the spectral smoothness and time continuity of the analyzed signals. The contribution is founded on two papers by Kitagawa and Gersch (1985). The first one deals with spectral smoothness in the regularization framework, while the second one is devoted to time continuity in the Kalman formalism. The present paper proposes an original synthesis of the two contributions. A new regularized criterion is introduced that takes bath pieces of information into account. The criterion is efficiently optimized by a Kalman smoother. One of the major features of the method is that it is entirely unsupervised. The problem of automatically adjusting the hyperparameters that balance data-based versus prior-based information is solved by maximum likelihood (ML). The improvement is quantified in the field of meteorological radar     

Fast quantised estimator of spectral centroid

A new sum based estimator of the spectral centroid is proposed by extending the polarity coincidence method. In the reference case of Gaussian processes, the variance of this new fast estimator is compared to that of the averaged output of the ideal FM receiver.<>     

Enhanced autoregressive moving average spectral estimation appliedto the measurement of Doppler spectral width

The measurement of clear-air turbulence with a Doppler radar is investigated. An autoregressive moving average (ARMA) model is proposed to improve the Doppler spectral width estimates. An iterative algorithm that has its origin in system identification is used for the estimation of the ARMA parameters. By taking advantage of a priori knowledge of the correlation matrix, which arises in the derivation of the governing equations of the ARMA parameters, the ARMA spectral estimate can be improved. This improvement is shown in terms of bias and variance of the spectral width estimate     

A two-dimensional maximum entropy spectral estimator

Using ideas from one-dimensional maximum entropy spectral estimation a two-dimensional spectral estimator is derived by extrapolating the two-dimensional sampled autocorrelation (or covariance) function. The method used maximizes the entropy of a set of random variables. The extrapolation (or prediction) process under this maximum entropy condition is shown to correspond to the most random extension or equivalently to the maximization of the mean-square prediction error when the optimum predictor is used. The two-dimensional extrapolation must he terminated by the investigator. The Fourier transform of the extrapolated autocorrelation function is the two-dimensional spectral estimator. Using this method one can apply windowing prior to calculating the spectral estimate. A specific algorithm for estimating the two-dimensional spectrum is presented, and its computational complexity is estimated. The algorithm has been programmed and computer examples are presented.     

An empirical investigation of deception behavior in instant messaging

Various disciplines have extensively studied deception in human communication. With the increasing use of instant messaging (IM) for both informal communication and task performance in the work place, deception in IM is emerging as an important issue. In this study, we explored the behavioral indicators of deception in a group IM setting. The empirical results showed that three types of nonverbal behaviors and three types of verbal behaviors that were investigated could significantly differentiate deceivers from truth tellers. The findings potentially can broaden our knowledge of deception behavior in human communication and improve deception awareness and deception detection in the cyberspace.     

Recursive autoregressive spectral estimation by minimization of thefree energy

A digital signal processing technique applicable to power spectrum estimation, designated as the minimum free energy method, is described. With no a priori model assumption and no attempt to extract special features such as sinusoids, one can obtain high resolution even with high noise contamination of the measured signal. The technique is demonstrated by modification of the Burg recursive method of spectral analysis. A recursive minimum free energy method in which the reflection coefficient is chosen at each step is proposed for minimizing the free energy (the Burg energy measure minus the product of a temperature and an information entropy). The method produces a spectral estimator more impervious to high noise contamination than the Burg method and diminishes the Burg tendency to produce spurious peaks     

Recovering of autoregressive spectral estimates of signals buried in noise

In this paper, we propose a noise modeling that does not destroy AR structure of buried signals in noise independently of its nature (white or colored, Gaussian or not) and its variance. Expression of perturbed AR coefficients is derived and proposed restoration does not use any a-priori information on the nature of noise and its variance. It is shown that AR coefficients are closer to nominal ones (noise-free) in the presence of noise for lower frequency contents with respect to the sampling frequency of corresponding continuous-time processes from which samples are taken for AR estimation. For unknown frequency contents, denoising of AR coefficients is obtained by decreasing the time interval separating samples used by AR estimation. A model order selection adapted to degraded signal-to-noise ratios is proposed. Performances of the proposed recovering of original AR spectra are demonstrated via signals buried in white and colored noise. Observed results are in accordance with the developed theory.     

Integrating guanxi into technology acceptance: An empirical investigation of WeChat

Social influence is an important research topic in the technology acceptance literature, in particular for social media. Prior empirical studies have for the most part employed social influence theory to investigate user intentions to continue with social media, while culture driven theories have been neglected. Rather than using social influence theory, we introduced guanxi theory to explore how guanxi social mechanisms (or processes) influence Chinese users’ continuance intentions in WeChat. Specifically, we developed a model that examines the role of guanxi as manifested by renqing, mianzi and ganqing in perceived usefulness, perceived enjoyment and continuance intention in WeChat. A survey research method was adopted to test the proposed hypotheses. This study found that ganqing has a positive impact on perceived usefulness and continuance intention. Mianzi exerts a negative effect on continuance intention but exhibits a positive effect on perceived usefulness. Renqing was found to have no significant impact on perceived usefulness and continuance intention. Our study advances the Technology Acceptance Model (TAM) by introducing guanxi-based constructs in a Chinese mobile social-messaging application context. Our study also offers alternative insights on guanxi-based social influence processes in the Chinese technology acceptance literature.     

A consistent nonparametric spectral estimator for randomly sampled signals

In this paper, the nonparametric spectral analysis of a randomly sampled signal is discussed. For this purpose, the general form of Masry's recursive spectral estimators is considered and improved. The corresponding algorithms require the knowledge of the sampling times, the samples, and the sampling law and that the high-frequency decay of the spectrum be at least roughly known. The minimization of the asymptotic equivalent of the mean square estimation error leads to an estimator that is not only consistent but also asymptotically optimal. Theoretical and simulation results regarding this new estimator are given and compared with the standard methods. Besides, the developed theoretical framework strictly includes the case where the sampling time process is Poisson and the signal is Gaussian.     

Uniqueness of a two-step predictor based spectral estimator thatgeneralizes the maximum entropy concept

Given a finite set of autocorrelations, it is well known that maximization of the entropy functional subject to this data leads to a stable autoregressive model. Since maximization of the entropy functional is equivalent to maximization of the minimum mean square error associated with one-step predictors, the problem of obtaining admissible extensions that maximize the k-step minimum-mean-square prediction error subject to the given autocorrelations has been shown to result in stable autoregressive moving-average (ARMA) extensions. The uniqueness of this true generalization of the maximum-entropy extension is proved here by a constructive procedure in the case of two-step predictors     

A comparison of the least squares method and the Burg method for autoregressive spectral analysis

The performance of autoregressive (AR) spectral estimates based on two different methods for computing the AR coefficients are compared. They are the recursive method as stated by Burg, which minimizes the residual power with respect to only one coefficient, and the straightforward but computationally less efficient least squares method (LSM) which minimizes the residual power with respect to all the AR coefficients simultaneously. It is shown that when the input signal consists of two equal-leveled sinusoids in white noise, the LSM estimate is highly superior with respect to resolution, positional bias, and spurious peaks in the spectrum.     

IKONOS image fusion by minimisation of spectral distortion using MAP estimator

Though intensity-hue-saturation (IHS) approaches for image fusion are very fast and easy to implement, they have the intrinsic problem of spectral distortion. To reduce colour distortion in IHS methods, a maximum a posteriori estimator is proposed that minimises colour distance between estimated intensity image and panchromatic image, which preserves spectral information and sharpens the reconstructed images. The simulation demonstrates that the proposed method reconstructs clearer colour images and yields good results, both subjectively and quantitatively.     

An adaptive bit time estimator

A bit time estimator which uses adaptive filtering techniques is presented. The filter weights of an adaptive linear predictor are shown to provide a reliable estimate of the bit time T of a random binary square wave contaminated with additive white Gaussian noise, with little or no a priori information. The quality of this estimator is then evaluated via the least mean square algorithm, and a comparison is made between it and a more conventional estimator based on a zero crossing detector. This comparison shows that an adaptive estimator based on a linear predictor is generally superior     

The Marple algorithm for the autoregressive spectral estimates of the SMMW fourier transform spectroscopy data

The Marple algorthm for the autoregressive spectral estimates has been applied to the SMMW Fourier transform spectrum analysis. The experimental results have shown that this method yields AR spectra with three times higher resolution than the FFT method does. The improvements obtained from the Marple algorithm over the maximum entropy algorithm include higher resolution, less bias in the spectral peak frequency estimation and absence of observed spectral line splitting. The effects of the structure of the spectral lines and the noise on the resolution are discussed.     

An autoregressive moving average tone detector

A new method for estimating tones in an arbitrary spectrum is presented. An autoregressive-moving average estimator is formulated and transformed into a linear regression problem. Many of the shortcomings of an "all pole" model are overcome and simulated test results indicate that the estimates are not particularly sensitive to additive noise. The main advantages of this new method are computational simplicity and robustness in noise environments. The algorithm can be useful in all areas where spectral information must be extracted in a computationally efficient fashion.     

A new approach to spectral estimation: a tunable high-resolutionspectral estimator

Traditional maximum entropy spectral estimation determines a power spectrum from covariance estimates. Here, we present a new approach to spectral estimation, which is based on the use of filter banks as a means of obtaining spectral interpolation data. Such data replaces standard covariance estimates. A computational procedure for obtaining suitable pole-zero (ARMA) models from such data is presented. The choice of the zeros (MA-part) of the model is completely arbitrary. By suitable choices of filter bank poles and spectral zeros, the estimator can be tuned to exhibit high resolution in targeted regions of the spectrum     

Asymptotic properties of 2-D MUSIC estimator and comparison to 2-D MP estimator

The MUSIC estimator of two-dimensional frequencies (2-D MUSIC) is studied assuming a one-measurement data model with deterministic phases and additive complex white Gaussian noise. The large sample estimation covariance of the 2-D MUSIC is derived and compared to that of the 2-D matrix pencil (MP) estimator. The theoretical estimation variances for both the MP and MUSIC estimators are compared with the simulated MP and MUSIC estimation variances and the Cramer-Rao Bound (CRB). In the single 2-D sinusoid case, the most revealing form of the estimation covariance for both estimators are provided. The results shown in this paper are valid for a median range of SNR.