NectarCAM is a camera designed for the medium-sized telescopes of the Cherenkov Telescope Array (CTA) covering the central energy range 100 GeV to 30 TeV. It has a modular design based on the NECTAr chip, at the heart of which is a GHz sampling Switched Capacitor Array and 12-bit Analog to Digital converter. The camera will be equipped with 265 7-photomultiplier modules, covering a field of view of 7 to 8 degrees. Each module includes the photomultiplier bases, High Voltage supply, pre-amplifier, trigger, readout and Thernet transceiver. Events recorded last between a few nanoseconds and tens of nanoseconds. A flexible trigger scheme allows to read out very long events. NectarCAM can sustain a data rate of 10 kHz. The camera concept, the design and tests of the various subcomponents and results of thermal and electrical prototypes are presented. The design includes the mechanical structure, the cooling of electronics, read-out, clock distribution, slow control, data-acquisition, trigger, monitoring and services. A 133-pixel prototype with full scale mechanics, cooling, data acquisition and slow control will be built at the end of 2014.
The recent development of multi-channel sensors has motivated interest in devising new methods for the
coherent processing of multivariate data. An extensive work has already been dedicated to multivariate
data processing ranging from blind source separation (BSS) to multi/hyper-spectral data restoration.
Previous work has emphasized on the fundamental role played by sparsity and morphological diversity
to enhance multichannel signal processing.
GMCA is a recent algorithm for multichannel data analysis which was used successfully in a variety of
applications including multichannel sparse decomposition, blind source separation (BSS), color image
restoration and inpainting. Inspired by GMCA, a recently introduced algorithm coined HypGMCA
is described for BSS applications in hyperspectral data processing. It assumes the collected data is a
linear instantaneous mixture of components exhibiting sparse spectral signatures as well as sparse spatial
morphologies, each in specified dictionaries of spectral and spatial waveforms. We report on numerical
experiments with synthetic data and application to real observations which demonstrate the validity of
the proposed method.
The statistics of the temperature anisotropies in the primordial Cosmic Microwave Background radiation field
provide a wealth of information for cosmology and the estimation of cosmological parameters. An even more
acute inference should stem from the study of maps of the polarization state of the CMB radiation. Measuring
the latter extremely weak CMB polarization signal requires very sensitive instruments. The full-sky maps of
both temperature and polarization anisotropies of the CMB to be delivered by the upcoming Planck Surveyor
satellite experiment are hence awaited with excitement. Still, analyzing CMB data requires tackling a number
of practical difficulties, notably that several other astrophysical sources emit radiation in the frequency range
of CMB observations. Separating the different astrophysical foreground components and the CMB proper from
available multichannel data is a problem that has drawn much attention in the community. Nevertheless, some
level of residual contributions, most significantly in the galactic region and at the locations of strong radio
point sources will unavoidably contaminate the estimated spherical CMB map. Masking out these regions is
common practice but the gaps in the data need proper handling. In order to restore the stationarity of a partly
incomplete CMB map and thus lower the impact of the gaps on non-local statistical tests, we developed an
inpainting algorithm on the sphere to fill in the gaps, based on an iterative thresholding scheme in a sparse
representation of the data. This algorithm relies on the variety of recently developed transforms on the sphere
among which several multiscale transforms which we will review. We also contribute to enlarging the set of
available transforms for polarized data on the sphere. We describe new multiscale decompositions namely the
isotropic undecimated wavelet and curvelet transforms for polarized data on the sphere. The proposed transforms
are invertible and so allow for applications in image restoration and denoising.
Over the last few years, the development of multi-channel sensors motivated interest in methods for the
coherent processing of multivariate data. From blind source separation (BSS) to multi/hyper-spectral
data restoration, an extensive work has already been dedicated to multivariate data processing. Previous
work has emphasized on the fundamental role played by sparsity and morphological diversity to
enhance multichannel signal processing.
Morphological diversity has been first introduced in the mono-channel case to deal with contour/texture
extraction. The morphological diversity concept states that the data are the linear combination of several
so-called morphological components which are sparse in different incoherent representations. In
that setting, piecewise smooth features (contours) and oscillating components (textures) are separated
based on their morphological differences assuming that contours (respectively textures) are sparse in the
Curvelet representation (respectively Local Discrete Cosine representation).
In the present paper, we define a multichannel-based framework for sparse multivariate data representation.
We introduce an extension of morphological diversity to the multichannel case which boils down
to assuming that each multichannel morphological component is diversely sparse spectrally and/or spatially.
We propose the Generalized Morphological Component Analysis algorithm (GMCA) which aims
at recovering the so-called multichannel morphological components. Hereafter, we apply the GMCA
framework to two distinct multivariate inverse problems : blind source separation (BSS) and multichannel
data restoration. In the two aforementioned applications, we show that GMCA provides new and
essential insights into the use of morphological diversity and sparsity for multivariate data processing.
Further details and numerical results in multivariate image and signal processing will be given illustrating
the good performance of GMCA in those distinct applications.
The Morphological Component Analysis (MCA) is a a new method which allows us to separate features contained in an image when these features present different morphological aspects. We show that MCA can be very useful for decomposing images into texture and piecewise smooth (cartoon) parts or for inpainting applications. We extend MCA to a multichannel MCA (MMCA) for analyzing multispectral data and present a range of examples which illustrates the results.
Analyzing data mapped to the sphere as may occur in a range of applications in geophysics, medical imaging or astrophysics, requires specific tools. This paper describes new multiscale decompositions for spherical images namely the isotropic undecimated wavelet transform, the ridgelet transform and the curvelet transform each of which is invertible. Several applications are described. We show how these transforms can be used in denoising and especially in a Combined Filtering Method, which uses both the wavelet and the curvelet transforms, thus benefiting from the advantages of both transforms. An application to component separation from multichannel data mapped to the sphere is also described where we take advantage of the spatiospectral localization on the sphere provided by the spherical wavelet functions.