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We localize dynamic electrical conductivity changes and reconstruct their time evolution introducing the spatial filtering technique to electrical impedance tomography (EIT). More precisely, we use the unit‐noise‐gain constrained variation of the distortionless‐response linearly constrained minimum variance spatial filter. We address the effects of interference and the use of zero gain constraints...
In this work we present the design of an FPGA based platform for acquiring and storing signals for SDR applications. The system comprises an embedded RISC processor, an A/D converter, RAM memory chips and a DMA controller core. This last component was designed from scratch to meet the high data rate and bulk requirements. The software needed to control the system was also developed and a Graphical...
We propose to design a small-size antenna array having high direction of arrival (DOA) estimation performance, inspired by the Ormia ochracea's coupled ears. The female Ormia is able to locate male crickets' call accurately, for reproduction purposes, despite the small distance between its ears compared with the incoming wavelength. This phenomenon has been explained by the mechanical coupling between...
Real-time GNSS receivers usually track the navigation satellite signals by means of code and carrier loops. In previous works we derived optimum loop filters and introduced the UFA discriminator to improve the PLL response to acceleration steps expected in launching vehicles. In this paper we present a data detection strategy based on the Per Survivor Processing (PSP) technique to use a four quadrants...
We propose modifications to the Automatic Relevance Determination (ARD) algorithm for solving the EEG/MEG inverse problem when the activation map of the cortex is known to be sparse. We propose to include a term to account for the background noise activity, i.e. electric activity of sources not in the cortex. Also, we prune the results of the ARD algorithm using a Model Selection criterion to get...
We present a shrinkage estimator for the EEG spatial covariance matrix of the background activity. We show that such an estimator has some advantages over the maximum likelihood and sample covariance estimators when the number of available data to carry out the estimation is low. We find sufficient conditions for the consistency of the shrinkage estimators and results concerning their numerical stability...
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