**2017**

DETERMINING POSITIONS OF TRANSDUCERS FOR RECEIVING AND/OR TRANSMITTING WAVE SIGNALS

Matthieu Martin Jean-Andre Simeoni, Paul Hurley, Nezihe Merve Gurel
Abstract
The invention is notably directed to a method for
determining positions pii=1,..., N of transducers
Aii=1,..., N of an apparatus. The transducers are
assumed to be configured for receiving wave signals from
and/or transmitting wave signals to one or more regions
Rmm=1,..., M of interest in an n-dimensional space,
with n = 2 or 3. The method first comprises determining
an n-dimensional spatial filter function , which matches
projections Pmm=1,..., M of the one or more regions
Rmm=1,..., M of interest onto an n ÿ 1-dimensional
sphere centered on the apparatus. Then, a density
function is obtained, based on a Fourier transform of
the determined spatial filter function . Finally, a
position pi is determined, within said n-dimensional
space, for each of N transducers, based on the obtained
density function and a prescribed number N of the
transducers. The invention is further directed to
related devices, apparatuses and systems, as well as
computer program products.

METHOD AND SYSTEM TO REDUCE NOISE IN PHASED-ARRAY SIGNALS FROM RECEIVERS LOCATED AT DIFFERENT LOCATIONS

Nezihe Merve Gurel, Paul Hurley, Matthieu Martin Jean-Andre Simeoni
Abstract
The present invention is notably directed to a
computerized method to reduce noise in phased- array
signals from a set of receivers at different locations.
Time-series are received from the receivers, which
time-series form phased-array signals. The time-series
are ordered based on the different locations of the
receivers and spatially phased series are obtained from
the ordered time- series. Each of the spatially phased
series obtained comprises a series of signal values that
are spatially phased. A noise component is identified in
each of the spatially phased series obtained and removed
from the spatially phased series to obtain denoised
series. The invention is further directed to related
receiver systems and computer program products.

Methods and apparatuses for versatile beamforming

Matthieu Martin Jean-Andre Simeoni, Paul Hurley, Giovanni Cherubini
Abstract
The present invention is directed to methods and
apparatuses for beamforming signals or compute
beamformed signals. The present approach is to determine
a series of beams from or for a set of devices
configured for receiving signals from and/or
transmitting signals to one or more regions of interest
in an n-dimensional space, with n=2 or 3. Each of the
devices has a known position pi within said
n-dimensional space. Signals are to be respectively
transmitted or received non-uniformly in this space,
i.e., according to the particular regions of interest.
During a first phase, operations are performed in order
to successively obtain a spatial filter function
circumflex over (Ï‰)(r), a beamforming function Ï‰(p),
and beamforming weights Ï‰(p1). The spatial filter
function circumflex over (Ï‰)(r) matches projections of
the regions of interest onto an nâˆ’1-dimensional sphere
centered on said set of devices. The function
circumflex over (Ï‰)(r), however, extends over the
n-dimensional space and does not restrict to the
nâˆ’1-dimensional sphere. During a second phase, delays
and gains are suitably introduced in the signals, by
weighting time-series according to beamforming weights
Ï‰(pi) obtained during the first phase.

Blind calibration of sensors of sensor arrays

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-Andre Simeoni
Abstract
Embodiments include methods for calibrating sensors of
one or more sensor arrays. Aspects include accessing one
or more beamforming matrices respectively associated to
the one or more sensor arrays. Source intensity
estimates are obtained for a set of points in a region
of interest, based on measurement values as obtained
after beamforming signals from the one or more sensor
arrays based on the one or more beamforming matrices,
assuming fixed amplitude and phase of gains of sensors
of the one or more sensor arrays. Estimates of amplitude
and phase of the sensor gains are obtained based on:
measurement values as obtained before beamforming; and
the previously obtained source intensity estimates. The
obtained estimates of amplitude and phase can be used
for calibrating said sensors.

Reconstruction using approximate message passing methods

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-Andre Simeoni
Abstract
The present invention is notably directed to a
computer-implemented method for image reconstruction.
The method comprises: accessing elements that
respectively correspond to measurement values, which can
be respectively mapped to measurement nodes; and
performing message passing estimator operations to
obtain estimates of random variables associated with
variable nodes, according to a message passing method in
a bipartite factor graph. In this message passing
method: the measurement values are, each, expressed as a
term that comprises linear combinations of the random
variables; each message exchanged between any of the
measurement nodes and any of the variable nodes is
parameterized by parameters of a distribution of the
random variables; and performing the message passing
estimator operations further comprises randomly mapping
measurement values to the measurement nodes, at one or
more iterations of the message passing method. Finally,
image data are obtained from the obtained estimates of
the random variables, which image data are adapted to
reconstruct an image. The present invention is further
directed to related systems and methods using the above
image reconstruction method.

Iterative image subset processing for image reconstruction

Giovanni Cherubini, Paul Hurley, Sanaz Kazemi, Matthieu Martin Jean-Andre Simeoni
Abstract
The present invention is notably directed to
computer-implemented methods and systems for recovering
an image. Present methods comprise: accessing signal
data representing signals; identifying subsets of points
arranged so as to span a region of interest as current
subsets of points; reconstructing an image based on
current subsets of points, by combining signal data
associated to the current subsets of points; detecting
one or more signal features in a last image
reconstructed; for each of the detected one or more
signal features, modifying one or more subsets of the
current subsets, so as to increase, for each of the
modified one or more subsets, a relative number of
points at a location of said each of the detected one or
more signal features. The relative number of points of a
given subset at a given location may be defined as the
number of points of said given subset at the given
location divided by the total number of points of said
given subset, whereby new current subsets of points are
obtained; and repeating the above steps of
reconstructing, detecting and modifying, as necessary to
obtain a reconstructed image that satisfies a given
condition.