In this paper we consider low-rank estimation of room impulse responses (RIRs). Inspired by a physics-driven room-acoustical model, propose an estimator RIRs that promotes structure for matricization, or reshaping, the estimated RIR. This prior acts as regularizer inverse problem estimating RIR from input-output observations, preventing overfitting and improving accuracy. As directly enforcing ...

Motivated from the central role of mean-square displacement and its second time-derivative -- that is velocity autocorrelation function $\left\langle v(0)v(t)\right\rangle=\frac{1}{2} \frac{\mathrm{d}^{2} \left\langle \Delta r^{2} (t)\right\rangle}{\mathrm{d}t^{2}} $ in description Brownian motion, we revisit physical meaning first particles. By employing a rheological analogue for show $\frac{...

The causal impulse response of the velocity potential for the Stokes wave equation is derived for calculations of transient velocity potential fields generated by circular pistons in viscous media. The causal Green's function is numerically verified using the material impulse response function approach. The causal, lossy impulse response for a baffled circular piston is then calculated within t...

A neural network architecture, which models synapses as Finite Impulse Response (FIR) linear lters, is discussed for use in time series prediction. Analysis and methodology are detailed in the context of the Santa Fe Institute Time Series Prediction Competition. Results of the competition show that the FIR network performed remarkably well on a chaotic laser intensity time series.

In this paper, we present a parallel algorithm for edge detection based on Infinite Impulse Response filter (IIR filter). In particular, the Infinite size Symmetric Exponential Filter (ISEF) that is an optimal IIR filter and computationally efficient smoothing filter is studied. The proposed algorithm exploits efficiently all aspects of potential parallelism (spatial parallelism, temporal paral...

The neural network filter architecture presented in this paper is well suited for a restricted class of nonlinear adaptive filter applications. Our filter can model systems with long time responses and it is able to learn to take into account additional parameters that influence the filter response nonlinearly. Our filter was successfully used in a biomedical application for the removal of the ...