We propose two novel voice activity detection (VAD) algorithms by employing i) the Reyni entropy estimate, and ii) a weighted combination of Reyni and differential entropies, at each frequency bin of power spectral estimates, by considering overlapping frames in the received signal samples. The...

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...

This article deals with nonlinear stochastic uncertain multi-agent systems (MASs) under mixed delays and impulse disturbances and leader-followers and leaderless systems. Based on the Lyapunov Function (LF), stochastic analysis and some inequality scaling skills, asymptotic stability is...

In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for wheeled mobile robots (WMR) subject to velocity input uncertainties. Gaussian process regression (GPR) is employed in view of its powerful estimation ability and wide scope of applications as a...

We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as...

Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust support vector machine classifiers under feature data...

This paper considers estimation and inference for heterogeneous counterfactual effects with high‐dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux (2009)) as a measure of heterogeneous counterfactual...

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model,...