All Years Seminars
[INMA] 2017-02-28 (14:00) : On Non-Convex Approaches to Blind Calibration for Compressed Sensing
At Euler building (room A.002)
Speaker :
Cambareri, Valerio
Abstract :
Blind Calibration is a bilinear inverse problem that arises in the
recovery of signals and images from some observations, as gathered by
computational sensing models (e.g., those of Compressed Sensing) subject to
parametric uncertainty. As an optimisation problem, blind calibration is
intrinsically non-convex, although it can be convexified through the so-called
"lifting" approach at the expense of high computational complexity.
In this talk we will present some fast algorithms for blind calibration that
attempt to recover an unknown signal from its projections on sub-Gaussian random
vectors, each of which is subject to an unknown multiplicative factor (or gain).
To jointly solve this optimisation problem with respect to the signal and gains
we will resort to a simple projected gradient descent algorithm, which starts
from a carefully chosen initialisation point and runs the descent on a
non-convex objective. Most importantly, an analysis of this scheme under
independent draws of the sensing model allows us to prove that the algorithm
actually convergences to the exact solution provided a sample complexity
requirement is met, i.e., depending on the amount of information collected by
the sensing model. Interestingly, we will show that this requirement grows
linearly (up to log factors) in the number of unknowns of the problem, and how
it depends on the priors assumed on the recovered signal and gains. Moreover, a
straightforward modification of our algorithm will allow us to enforce sparsity
in the signal domain, thus enabling blind calibration for Compressed Sensing.
Our theory is completed by some numerical evidence on the phase transition of
these algorithms, highlighting a regime in which exact recovery occurs almost
always; we shall conclude our talk with a practical application of blind
calibration in the recovery of the gains in an imaging sensor. This is a joint
work with Prof. Laurent Jacques.
[INMA] 2017-02-21 (14:00) : Identification of dynamical networks
At Euler building (room A.002)
Speaker :
Gevers, Michel
Abstract :
We consider the identification of dynamical networks in which the node signals are connected by linear time-invariant systems described by scalar causal rational transfer functions. The node signals are excited through the network by a combination of known external excitation signals and unknown noise sources. Two major problems are considered: the identification of the whole network on the basis of measured node signals and external excitation signals, and the identification of a particular module embedded in the network. Both problems raise the questions of identifiability (what needs to be known about the network structure and which signals need to be measured?) and informativity (which external signals need to be present?). So far all available results assume that all nodes are measured. We shall present recent results on both major problems, as well as the first results for the case where not all nodes are measured. We shall also point out challenging open problems.
[INMA] 2017-02-13 (11:00) : Is the search for non trivial new physics phenomena at the LHC an impossible mission ?
At Euler building (room A.002)
Speaker :
Vincent Lemaître (UCL)
Abstract :
The Large Hadron Collider (LHC) allows us to probe sub-protonic scales where
new elementary particles and / or new fundamental interactions could appear.
The recent observation of the Higgs particle in 2012 was the smoking gun of
a mechanism suggesting the existence of a dramatic phase transition that
altered the structure of the vacuum around 10 ^ -10 seconds after the Big Bang.
However, several important and fundamental questions remain unanswered and the
discovery of the Higgs has opened a new era in the search for physics beyond
the "Standard Model" of fundamental interactions. In this seminar, we will try
to better define what "observation" means and which data analysis techniques
have been/are being put in place to observe possible new physics phenomena at the LHC in the next decade.
[INMA] 2017-02-07 (14:00) : How to extract the oscillating components of a signal? A wavelet-based approach compared to the Empirical Mode Decomposition
At Euler building (room A.002)
Speaker :
Adrien Deliège (ULg)
Abstract :
Researchers are often confronted with time series that display pseudo-periodic
tendencies with time-varying amplitudes and frequencies.
In that framework, a classic Fourier analysis of the data may be of limited
interest, especially if the objective is to derive components from the signal
that capture the non-stationary behaviour of the oscillating factors.
In this talk, we present two powerful tools designed to extract amplitude
modulated-frequency modulated (AM-FM) components from a given signal.
The first one is the renowned Empirical Mode Decomposition (EMD); we explain the
technique, its main benefits, limitations and major practical uses. Then, we
introduce the continuous wavelet transform and the equations that justify
its relevance in the present context. We propose an algorithm based on the
wavelet-induced time-frequency representation of a signal to extract its main components.
The performances of this method are compared with the EMD on various AM-FM signals
exhibiting different particularities. After briefly broaching the problem of edge effects,
we investigate whether the wavelet-based procedure can be used in the domain of time
series forecasting. For that purpose, we study the El Nino Southern Oscillation index
and develop a model aimed at predicting the long-term trends of the signal.
Its predictive skills are tested in several ways and exposed in the final part of the talk.
[INMA] 2016-11-29 (14:00) : Identification for control: Classical results, new ideas, and applications in precision mechatronics
At Euler building (room A.002)
Speaker :
Tom Oomen (Eindhoven University of Technology)
Abstract :
The purpose of identifying a model is often control design. In this
presentation, the connection between system identification and control
is investigated and improved. First, classical results regarding
identification of nominal models for control are revisited. Analysis of
these classical methods reveals the necessity of addressing model
uncertainty. In particular, the model uncertainty interconnection
structure turns out to have a central role in identification for robust
control. This insight leads to new connections between control-relevant
identification of nominal models, coprime factor identification, and the
size of model uncertainty. These results are essential to address
future challenges in high-tech motion systems as arising in, e.g.,
lithographic IC manufacturing and industrial printing.
Seminars
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