Distribution of Articles published per year
(1970 - 2017)
(1970 - 2017)
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Article 1 Read 0 Citations Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal impo... Published: 15 September 2017
EURASIP Journal on Advances in Signal Processing, doi: 10.1186/s13634-017-0499-3
This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain’s connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes.
Article 0 Reads 0 Citations Direct Position Estimation of GNSS Receivers: Analyzing main results, architectures, enhancements, and challenges Published: 01 September 2017
IEEE Signal Processing Magazine, doi: 10.1109/MSP.2017.2718040
Conference 6 Reads 0 Citations MEMS IMU/ZUPT Based Cubature Kalman Filter Applied to Pedestrian Navigation System Published: 02 June 2014
International Electronic Conference on Sensors and Applications, doi: 10.3390/ecsa-1-e002
Article 2 Reads 3 Citations Performance evaluation of MSK and OFDM modulations for future GNSS signals Published: 11 February 2014
GPS Solutions, doi: 10.1007/s10291-014-0368-6
The objective of this work is to investigate the performances of orthogonal frequency division multiplexing (OFDM) and minimum frequency shift keying (MSK) modulations as potential future global navigation satellite systems (GNSS) signal modulation schemes. MSK is used in global system for mobile communications because of its spectral efficiency, while OFDM is used in WLAN and digital video broadcast-terrestrial because of its multipath mitigation capability. These advantages of MSK and OFDM modulations render them as promising modulation candidates for future GNSS signals to offer enhanced performances in challenging environments. Gabor bandwidth and multipath error envelopes of these two modulations were computed and compared with those of the current global positioning system (GPS), Galileo, and Beidou signal modulations. The results show that OFDM modulation demonstrated promises as a viable future GNSS modulation, especially for signals that require pre-filtering bandwidths larger than 2 MHz, while MSK modulation is more desirable for pre-filtering bandwidth below 2 MHz where it exhibits the largest Gabor bandwidth.
Conference 1 Read 0 Citations Assessment of Direct Positioning for IR-UWB in IEEE 802.15.4a channels Published: 01 September 2013
2013 IEEE International Conference on Ultra-Wideband (ICUWB), doi: 10.1109/icuwb.2013.6663822
This paper assesses the problem of localization in IR-UWB under realistic channel models for Direct Position Estimation (DPE) approaches. DPE schemes have been proposed for positioning and localization for well developed systems like GNSS, where it has been analytically proved that the Maximum-likelihood single-step estimator outperforms two-step procedures. The extension to wideband systems and less favorable scenarios like indoor UWB channels is less explored. We derive a DPE algorithm and analyze its performance against two-step TOA based localization for an IR-UWB system. Numerical results are provided for IEEE 802.15.4a channel model showing positioning performance of the two approaches and highlighting the tradeoffs.
Conference 1 Read 0 Citations Sequential estimation of gating variables from voltage traces in single-neuron models by particle filtering Published: 01 May 2013
ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), doi: 10.1109/icassp.2013.6637853
This paper addresses the problem of inferring voltage traces and ionic channel activity from noisy intracellular recordings in a neuron. A particle filtering method with optimal importance density is proposed to that aim, with the benefits of on-line estimation methods and Bayesian filtering theory. The method is applied to an inaccurate Morris-Lecar neuron model without loss of generality. Simulation results show the validity of the approach, where it is observed that theoretical estimation bounds are attained.