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VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Radar Clutter Modeling and Analysis
DTSTART;VALUE=DATE-TIME:20121016T123000Z
DTEND;VALUE=DATE-TIME:20121016T133000Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-11@cern.ch
DESCRIPTION:Speakers: GRECO\, Sabrina Maria ()\nURL: http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=11&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=11&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Searches for GW transients with the ground-based detectors.
DTSTART;VALUE=DATE-TIME:20121016T071500Z
DTEND;VALUE=DATE-TIME:20121016T091500Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-10@cern.ch
DESCRIPTION:Speakers: KLIMENKO\, Sergey ()\nDescription: In this lecture I
give an overview of the gravitational-wave experiment \nand methods used
in the analysis of data from the ground-base detectors. \nThe purpose of
the lecture is to describe how a worldwide network of GW detectors
\nworks\, with focus on signal processing techniques and reconstruction
methods\, \nrather than to describe details of specific search algorithms
used so far in the\nGW experiment. The lecture is organized in three
parts:\n1) Short introduction into the gravitational-wave experiment.\n2)
Signal processing methods and their applications to the analysis of single
detector data\, including:\n - time-frequency transformations\n -
data conditioning with linear prediction error (LPE) filters\n -
regression of GW data\, Wiener-Kolmogorov filters\n - time-frequency
analysis\, clustering in TF domain\n - multi-resolution analysis\n3)
Analysis of data from multiple GW detectors (coherent network analysis)\,
including\n - characterization of detector networks\n - Inverse
problem for bursts\, likelihood methods\n - matched filters\, weakly
modeled and un-modeled searches \n - detection statistics\n -
reconstruction of polarization states\n - reconstruction of GW
waveforms\n - sky localization\n - factors affecting GW network
performance.\nURL: http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=10&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=10&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Welcome
DTSTART;VALUE=DATE-TIME:20121015T071500Z
DTEND;VALUE=DATE-TIME:20121015T072500Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-13@cern.ch
DESCRIPTION:Speakers: Prof. FERRINI\, Federico (EGO)\nURL: http://events
.ego-gw.it/indico/contributionDisplay.py?contribId=13&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=13&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bayesian Inference in Digital Communication.Part II: Applications
DTSTART;VALUE=DATE-TIME:20121015T142000Z
DTEND;VALUE=DATE-TIME:20121015T162000Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-12@cern.ch
DESCRIPTION:Speakers: BARLETTA\, Luca ()\nDescription: A classical problem
in synchronization\, an activity that plays a central role in signal
processing for digital communication\, is that of extracting the phase of
a sinusoid affected by phase noise and embedded in additive noise. In the
talk\, the problem is presented as an instance of Bayesian inference. Four
variants of the basic problem are treated.\n1) Sinusoid with constant
amplitude\, where the phase is extracted by filtering the sinusoid
through the Wiener filter.\n2) Sinusoid affected by known amplitude
modulation. Here the system model is no more stationary\, and the phase
should be extracted by filtering the sinusoid through the Kalman filter.
\n3) Sinusoid affected by unknown amplitude modulation. Here non-
parametric methods\, such as quantization of the phase space\, should be
adopted. \n4) A message is transmitted through a channel that includes
up/down conversion\, that is multiplication of the message by a sinusoid.
In this context\, the phase noise affecting the sinusoid is a source of
disturbance that compromises channel capacity. Computation of channel
capacity can be approached by Bayesian methods that track the phase noise
that affect the sinusoid.\nURL: http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=12&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=12&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Understanding (and Taming) Glitches: Threads in a Tapestry
DTSTART;VALUE=DATE-TIME:20121015T072500Z
DTEND;VALUE=DATE-TIME:20121015T090500Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-1@cern.ch
DESCRIPTION:Speakers: Prof. PINTO\, Innocenzo ()\nDescription: A cartoon
introduction to Gravitational waves.\nNon ideal features of noise in
interferometric GW detectoers\nGlitches\nData quality and Vetoing\nGW
interferometers as MIMOs\nLessons from PEM-injections\; bilinear
pseudochannels\nWiener-Volterra description\nChannel-Channel Transfer
functions\nGW interferometers as MISOs\n"Input" disturbances vs
environmental ones.\nGlitch structure.\nModal decomposition.\nURL:
http://events.ego-gw.it/indico/contributionDisplay.py?contribId=1&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=1&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Welcome
DTSTART;VALUE=DATE-TIME:20121015T070500Z
DTEND;VALUE=DATE-TIME:20121015T071500Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-0@cern.ch
DESCRIPTION:Speakers: Dr. CUOCO\, Elena (EGO)\nURL: http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=0&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=0&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Bayesian Inference in Digital Communication.Part I: Theory
DTSTART;VALUE=DATE-TIME:20121015T120000Z
DTEND;VALUE=DATE-TIME:20121015T140000Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-3@cern.ch
DESCRIPTION:Speakers: MAGARINI\, Maurizio ()\nDescription: Given a hidden
state process with memory and a measurement process that is memoryless
given the state\, one has to make inference on the hidden state sequence
given the measurement sequence. This activity can be performed by the so-
called Bayesian tracking\, where the probability of the state at time k is
recursively computed from the probability of the state at time k-1 and
from the measurement at time k. The talk will recall the basics of
Bayesian tracking: two-step recursion based on predict-update\, forward-
backward recursion for making inference on the state at time k given the
entire measurement sequence. The Kalman filter is then presented as an
instance of Bayesian tracking for a non-stationary linear state
transition model and Gaussian processes. Wiener filter is then derived for
the stationary case. The general case of non-linear and non-Gaussian
model can be difficult to deal with. To get good results\, the approach to
be adopted should fit the specific state transition and measurement model
at hand. As examples\, the talk will present the non-Gaussian parametric
approach\, the state-space discretization approach\, and the sequential
importance sampling approach based on particle filters.\nURL:
http://events.ego-gw.it/indico/contributionDisplay.py?contribId=3&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=3&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Signals and noises in neuroimaging
DTSTART;VALUE=DATE-TIME:20121015T093000Z
DTEND;VALUE=DATE-TIME:20121015T104500Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-2@cern.ch
DESCRIPTION:Speakers: Dr. CHINCARINI\, Andrea (INFN)\nDescription: In the
framework of neurodegenerative diseases\, and in particular the
Alzheimer's disease (AD)\, we are witnessing an increasing presence of
neuroimaging data\, magnetic resonant images (MRI) and positron emission
tomography (PET) above all.\nDespite the fact that clinical scanners have
been around for some decades\, MRI and PET images have only recently
become a dependable support in diagnosing early and prodromal AD.\nThe
purpose of image analysis is to find supporting evidence to help in early
clinical assessment. In the case of neurodegenration leading to AD\, we
would ideally look for a measure (marker) able to discriminate between
normalcy versus pathology at a pre-clinical stage\, easy to implement in
clinical practice and possibly based on quick\, low- cost\, and widely
available procedures. In addition\, a “good” marker should have
predictive value as to whether a subject with current unknown or unclear
clinical assessment will or will not develop the pathological condition in
a given timeframe.\nFrom a data analysis point of view\, the problem can
be restated as a measure of a “signal” (the pathology marker) on a
“background” of normalcy. Whether a marker can have predictive value
at all implies that it must be sensitive to some key aspects of the
pathology process well before their effects have a clear clinical
counterpart.\nWe shall give an overview of some relevant branches in brain
image analysis and the requirements to define a suitable signal over the
confounding variability (noises) peculiar to life-science studies.\nURL:
http://events.ego-gw.it/indico/contributionDisplay.py?contribId=2&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=2&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Coherent Target Detection in Heavy-Tailed Compound-Gaussian
Clutter
DTSTART;VALUE=DATE-TIME:20121016T135000Z
DTEND;VALUE=DATE-TIME:20121016T155000Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-7@cern.ch
DESCRIPTION:Speakers: Prof. GINI\, Fulvio ()\nDescription: Coherent radar
target detection is the subject of the second part of the talk. In high
resolution radar systems the disturbance cannot be modelled as Gaussian
distributed and the classical detectors suffer from high losses. Then\,
according to the adopted disturbance model\, optimum and sub-optimum
detectors are derived and their performance analyzed against a non-
Gaussian background. First\, the problem of detecting completely known and
fluctuating random signals\, possibly with unknown parameters\, against
correlated non-Gaussian clutter modelled as a compound-Gaussian process is
then described exploiting different degrees of knowledge on target and
clutter statistical characteristics. A generalized likelihood ratio test
(GLRT) detector and a fully adaptive constant false alarm rate (CFAR)
detector are derived and different novel interpretations of the detection
algorithms provided in order to highlight the relationships and the
differences among them and the links with the Gaussian clutter case. Each
interpretation suggested different ways to implement the optimum detector
and allowed to derive sub-optimum detectors easier to implement than the
optimum and with very low losses. \n Modern radar systems generally
operate in non-homogeneous and non-stationary clutter environment. In this
condition the amplitude statistics and the power spectral density of the
disturbance are unknown. Therefore\, the adaptive versions of the
algorithms previously described\, which estimate the clutter covariance
matrix\, are then introduced. An estimation algorithm is described which
guarantees the CFAR property and its performance are investigated and
compared to that of the maximum likelihood (ML) estimator. The important
cases of target signal partially unknown or modelled as a subspace random
process are also analyzed. The proposed detectors are tested against both
simulated data and measured high resolution sea clutter data to
investigate the dependence of their performance on the various clutter and
signal parameters.\nURL: http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=7&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=7&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Radar Clutter Modeling and Analysis
DTSTART;VALUE=DATE-TIME:20121016T094500Z
DTEND;VALUE=DATE-TIME:20121016T104500Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-6@cern.ch
DESCRIPTION:Speakers: Dr. GRECO\, Maria Sabrina ()\nDescription: The first
part of the talk focuses on the statistical analysis and modeling of radar
clutter echoes. This is a central issue for the design and performance
evaluation of radar systems. Main goal of this talk is to describe the
state-of-the-art approaches to the modeling and understanding of land and
sea clutter echoes and their implications on performance prediction and
signal processors design. \nThe talk will first introduce radar sea and
ground clutter phenomena\, measurements and measurement limitations\, at
high and low resolution\, high and low grazing angles with some references
to classical model for Radar Cross Section prediction. Then the main part
of the talk will be dedicated to modern statistical and spectral models
for high resolution sea and ground clutter and\, particularly\, to the
methods of experimental validation using recorded data sets. A plethora of
experimental results will be shown and commented.\nURL: http://events
.ego-gw.it/indico/contributionDisplay.py?contribId=6&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=6&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Many-core computing in experimental gravitational wave physics.
DTSTART;VALUE=DATE-TIME:20121017T091500Z
DTEND;VALUE=DATE-TIME:20121017T110000Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-9@cern.ch
DESCRIPTION:Speakers: DEBRECZENI\, Gergeley ()\nDescription: With the
advance of cheaply available many-core architectures (such as GPU-s) on
the computing\nmarket scientific computing entered into a new era. There
are plenty of mathematical algorithms\nand data analysis methods which can
be easily\, massively parallelized and implemented on
these\narchitectures resulting a dramatic increase of speed (several order
of magnitude) and - at the same\ntime - decrease of computational
costs!\n The presenataion will give a brief overview of currently
available hardwares and programing\ntechniques with their application
focused on experimental gravitational wave physics.In the last third\nof
the lecture an introductory hands-on tutorial will be given to the
audiance. Bring your laptop\nwith you\, if possible.\nURL: http://events
.ego-gw.it/indico/contributionDisplay.py?contribId=9&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=9&confId=1
END:VEVENT
BEGIN:VEVENT
SUMMARY:Understanding (and Taming) Glitches: Threads in a Tapestry
DTSTART;VALUE=DATE-TIME:20121017T070000Z
DTEND;VALUE=DATE-TIME:20121017T090000Z
DTSTAMP;VALUE=DATE-TIME:20191118T115012Z
UID:indico-contribution-1-8@cern.ch
DESCRIPTION:Speakers: PINTO\, Innocenzo ()\nDescription: Retrieving glitch
constituents.\nThe Hilbert-Huang transform\; Prony (robustified)
decomposition\nGlitch taxonomy and entomology:\ncorrelation graphs\,
clustering and classification\nTF representations: Q and Wigner-Ville
transform\nGetting rid of intermodulation artifacts\nRetrieving TF
skeletons.\nSparse representations in time-domain\nCompressed coding and
adaptive dictionary learning.\nNon ideal features of the residual noise
floor: Gaussianity tests\; SIRP model.\nGlitch noise statistics\, via
Middleton model\nLocally Optimum Detector (LOD) and its robust
implementation\nNumerical experiments on real data.\nURL: http://events
.ego-gw.it/indico/contributionDisplay.py?contribId=8&confId=1
LOCATION:EGO\, Cascina Auditorium
URL:http://events.ego-
gw.it/indico/contributionDisplay.py?contribId=8&confId=1
END:VEVENT
END:VCALENDAR