7,862 research outputs found
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
Robust Network Topology Inference and Processing of Graph Signals
The abundance of large and heterogeneous systems is rendering contemporary
data more pervasive, intricate, and with a non-regular structure. With
classical techniques facing troubles to deal with the irregular (non-Euclidean)
domain where the signals are defined, a popular approach at the heart of graph
signal processing (GSP) is to: (i) represent the underlying support via a graph
and (ii) exploit the topology of this graph to process the signals at hand. In
addition to the irregular structure of the signals, another critical limitation
is that the observed data is prone to the presence of perturbations, which, in
the context of GSP, may affect not only the observed signals but also the
topology of the supporting graph. Ignoring the presence of perturbations, along
with the couplings between the errors in the signal and the errors in their
support, can drastically hinder estimation performance. While many GSP works
have looked at the presence of perturbations in the signals, much fewer have
looked at the presence of perturbations in the graph, and almost none at their
joint effect. While this is not surprising (GSP is a relatively new field), we
expect this to change in the upcoming years. Motivated by the previous
discussion, the goal of this thesis is to advance toward a robust GSP paradigm
where the algorithms are carefully designed to incorporate the influence of
perturbations in the graph signals, the graph support, and both. To do so, we
consider different types of perturbations, evaluate their disruptive impact on
fundamental GSP tasks, and design robust algorithms to address them.Comment: Dissertatio
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