29,728 research outputs found
DSP-based ionospheric radiolink using DS-CDMA and on-line channel estimation
In this paper, a new blind multiuser detection algorithm is presented. It can both cancel multiuser interference and estimate the multipath channel response in a blind way. The method has been specially conceived for low coherence bandwidth channels such as the ionospheric channel and exhibits very low computational requirements. Real-time measurements from a fully digital HF radio-link are presented that confirm the reliability of the method for the ionospheric channel.Peer ReviewedPostprint (published version
Smart Device for the Determination of Heart Rate Variability in Real Time
This work presents a first approach to the design, development, and implementation of a smart device for the real-time
measurement and detection of alterations in heart rate variability (HRV). The smart device follows a modular design scheme,
which consists of an electrocardiogram (ECG) signal acquisition module, a processing module and a wireless communications
module. From five-minute ECG signals, the processing module algorithms perform a spectral estimation of the HRV. The
experimental results demonstrate the viability of the smart device and the proposed processing algorithms.Fundación Pública Andaluza Progreso y Salud. Gobierno de AndalucÃa PI-0010-2013 y PI-0041-2014Ministerio de EconomÃa y Competitividad (Instituto de Salud Carlos III) PI15 / 00306 y DTS15 / 00195CIBER-BBN INT-2-CAR
New receivers for DS-SS in time variant multipath channels based on the PN alignment concept
We present new combined blind equalization and detection schemes for a DS-SS system. The new proposed algorithms improve the bit error rate compared to traditional RAKE receivers in time-variant channels with multipath. This improvement is obtained in both simulated and a real ionospheric HF link. Its very low computational complexity makes them suitable to be implemented in real receivers.Peer ReviewedPostprint (published version
Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization
Spherical deconvolution (SD) methods are widely used to estimate the
intra-voxel white-matter fiber orientations from diffusion MRI data. However,
while some of these methods assume a zero-mean Gaussian distribution for the
underlying noise, its real distribution is known to be non-Gaussian and to
depend on the methodology used to combine multichannel signals. Indeed, the two
prevailing methods for multichannel signal combination lead to Rician and
noncentral Chi noise distributions. Here we develop a Robust and Unbiased
Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with
realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to
Rician and noncentral Chi likelihood models. To quantify the benefits of using
proper noise models, RUMBA-SD was compared with dRL-SD, a well-established
method based on the RL algorithm for Gaussian noise. Another aim of the study
was to quantify the impact of including a total variation (TV) spatial
regularization term in the estimation framework. To do this, we developed TV
spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The
evaluation was performed by comparing various quality metrics on 132
three-dimensional synthetic phantoms involving different inter-fiber angles and
volume fractions, which were contaminated with noise mimicking patterns
generated by data processing in multichannel scanners. The results demonstrate
that the inclusion of proper likelihood models leads to an increased ability to
resolve fiber crossings with smaller inter-fiber angles and to better detect
non-dominant fibers. The inclusion of TV regularization dramatically improved
the resolution power of both techniques. The above findings were also verified
in brain data
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