Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the ...

In this work, the problem of detecting neuronal spikes or action potentials (AP) in noisy recordings from a Microelectrode Array (MEA) is investigated. In particular, the spike detection algorithms should be less complex ...

We explore the applicability of local polynomial approximation of signals for noise suppression. In the context of data regression, Savitzky and Golay showed that least-squares approximation of data with a polynomial of ...

There is an ever increasing demand for higher spectral usage in wireless communication, radar and imaging systems. Higher spectral efficiency can be achieved using components that are aware of system environment and adapt ...

Every auditory event provides an information-rich signal to the brain. The signal constitutes perceptual attributes of pitch, loudness, timbre, and also, conceptual attributes like location, emotions, meaning, etc. In the ...

Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian ...