In order to optimize the signal estimation within the SC we estimated an alternative model, in addition to the standard hemodynamic response function (HRF) model, using an HRF with an earlier peak that has been demonstrated to be better-suited for investigating SC activity [42].
We present our data as signal estimation models of the tracheal bronchial frequency spectra.
The Wiener filter is the well-known solution for linear minimum mean square error (LMMSE) signal estimation.
A quadrature digital receiver and associated signal estimation procedure are reported for L-band electron paramagnetic resonance (EPR) spectroscopy.
A three stage procedure based on the inner signal estimation, outlined in a previous contribution, has been proposed.
In fact, we analytically prove that the sample-profile signal estimation problem can be solved independently of the control design scheme for amplitude regulation.
In this paper, we consider the design problem of optimal sensor quantization rules (quantizers) and an optimal linear estimation fusion rule in bandwidth-constrained decentralized random signal estimation fusion systems.
Recently this coding method has been used for the application of remote signal estimation in wireless sensor networks (WSN), where multiple sensor nodes compress their correlated observations without inter-node communications.
However, existing diffusion MRI data sampling and signal estimation methods have largely been developed and tuned without the benefit of such theory, instead relying on approximations, intuition, and extensive empirical evaluation.
For these methods, one faulty signal may cause all the other signals that use the faulty signal as an input in signal estimation to be un-correctly reconstructed and subsequently falsely identified as faulty too.
These building blocks can be used to generate different novel SVM-based methods for problems of signal estimation, and we deal with several of the most important ones in DSP.
