Traditional tests of the white noise hypothesis on the residuals from estimated models can leadto size distortion if some parameters are weakly identified. This paper develops a bootstrappedwhite noise test for serial correlation that is robust to weak identification in the parameters. Weshow...

The identification of gas-liquid flow regimes for co-current upward flow in a vertical pipe is investigated using Polynomial regression (PR) and Linear Discriminant analysis (LDA) on the response from a non-intrusive optical sensor. The average voltage and associated standard deviation can be...

Acoustic source localization in the spherical harmonic domain with reverberation has hitherto not been investigated extensively. When the joint direction-of-arrival (DOA) estimation is treated as a classification task, it needs a lot of different classes and large-scale dataset, which is...

Two parallel Analog-to-Digital Converters (ADC) are the components of 2-Channel TI-ADC (for Time-Interleaved ADC). They are addressed at times nT and is the clock period). ”Timing skews” are biases of these sampling times. They vary slowly, and they have to be estimated and corrected. In...

Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are...

We report non-zero weak lensing convergence correlation signal of Type-Ia supernovae from 5-year Supernovae Legacy Survey data. For our analysis we utilize 296 supernovae magnification data from 5-year SNLS in the weak lensing limit. The data we use consists of measurements from four different...

In this paper, we present a method to estimate the signal subspace at all the frequencies in a given band, which is computed from the usual set of frequency-bin sample covariance matrices in wideband subspace estimation. Fundamentally, the method exploits the similarity between the signal...

We develop a procedure to identify latent group structures in linear panel data models that exploits a grouping in the error variances of cross-sectional units. To accommodate such grouping, we introduce an objective function that avoids a singularity that arises in a pseudo-likelihood approach....

This paper proposes a recursive interval-valued estimation framework for identifying the parameters of linearly parameterized systems which may be slowly time-varying. It is assumed that the model error (which may consist in measurement noise or model mismatch or both) is unknown but lies at...

In this paper, we propose an efficient approach based on linear programming to study constrained stabilization problem of linear positive differential-difference equations with unbounded delay. We first propose new characterizations of the positivity of the equations, then delaydependent...