We study the spatial-temporal sampling of a linear diffusion field, and show that it is possible to compensate for insufficient spatial sampling densities by oversampling in tim...
—We consider constrained minimization of a sum of convex functions over a convex and compact set, when each component function is known only to a specific agent in a timevarying...
This paper presents a new method for analysis of center of pressure (COP) signals using Empirical Mode Decomposition (EMD). The EMD decomposes a COP signal into a finite set of b...
Ram Bilas Pachori, David J. Hewson, Hichem Snoussi...
When sampling signals below the Nyquist rate, efficient and accurate reconstruction is nevertheless possible, whenever the sampling system is well behaved and the signal is well ...
We design distributed and quantized average consensus algorithms on arbitrary connected networks. By construction, quantized algorithms cannot produce a real, analog average. Inst...
Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separ...
In this paper, we propose an F0 Frame Error (FFE) metric which combines Gross Pitch Error (GPE) and Voicing Decision Error (VDE) to objectively evaluate the performance of fundame...
We present a CMOS imager with built-in capability to perform Compressed Sensing coding by Random Convolution. It is achieved by a shift register set in a pseudo-random configurat...
Laurent Jacques, Pierre Vandergheynst, Alexandre B...
We propose a “compressive” estimator of the Wigner-Ville spectrum (WVS) for time-frequency sparse, underspread, nonstationary random processes. A novel WVS estimator involving...
Resource limitations in wireless sensor networks have put stringent constraints on distributed signal processing. In this paper, we propose a cluster-based decentralized variation...