Unveiling meaningful geophysical information from seismic data requires to deal with both
random and structured “noises”. As their amplitude may be greater than signals of interest …

Sparsity-aware adaptive algorithms present some advantages over standard ones, specially
due to the fact that they have faster convergence rate. This paper proposes a stochastic …

Ordinary differential equations (ODEs) are widely used to model the dynamic behavior of a
complex system. Parameter estimation and variable selection for a “Big System” with linear …

We propose a novel online algorithm for efficient nonlinear estimation. Target nonlinear
functions are approximated with “unfixed” Gaussians of which the parameters are regarded …

Observing that sparse systems are almost smooth, we propose to utilize the newly-
introduced adaptively-weighted total variation (AWTV) for sparse system identification. In our …

In this paper, we propose a robust sparsity-aware adaptive filtering algorithm under
impulsive noise environment, by using the Huber loss function in the frame of adaptive …

We propose a novel stochastic-optimization framework based on the regularized dual
averaging (RDA) method. The proposed approach differs from the previous studies of RDA …

We propose an efficient adaptive update method for the kernel parameters: the kernel
coefficients, scales and centers. The mirror descent and the steepest descent method for …

We previously proposed a joint learning scheme of Gaussian parameters (scales and
centers) and coefficients for online nonlinear estimation. The instantaneous squared error …

Observing that a typical primary path in Active Noise Control (ANC) system is sparse, ie,
having a few significant coefficients, we propose an adaptive learning which promotes the …