980,651 research outputs found
Model-based Optimization of Compressive Antennas for High-Sensing-Capacity Applications
This paper presents a novel, model-based compressive antenna design method
for high sensing capacity imaging applications. Given a set of design
constraints, the method maximizes the sensing capacity of the compressive
antenna by varying the constitutive properties of scatterers distributed along
the antenna. Preliminary 2D design results demonstrate the new method's ability
to produce antenna configurations with enhanced imaging capabilities
The usability of the optical parametric amplification of light for high-angular-resolution imaging and fast astrometry
High-angular-resolution imaging is crucial for many applications in modern
astronomy and astrophysics. The fundamental diffraction limit constrains the
resolving power of both ground-based and spaceborne telescopes. The recent idea
of a quantum telescope based on the optical parametric amplification (OPA) of
light aims to bypass this limit for the imaging of extended sources by an order
of magnitude or more. We present an updated scheme of an OPA-based device and a
more accurate model of the signal amplification by such a device. The
semiclassical model that we present predicts that the noise in such a system
will form so-called light speckles as a result of light interference in the
optical path. Based on this model, we analysed the efficiency of OPA in
increasing the angular resolution of the imaging of extended targets and the
precise localization of a distant point source. According to our new model, OPA
offers a gain in resolved imaging in comparison to classical optics. For a
given time-span, we found that OPA can be more efficient in localizing a single
distant point source than classical telescopes.Comment: Received: 11 November 2017, revision received: 31 January 2018,
accepted: 31 January 201
Real-time Model-based Image Color Correction for Underwater Robots
Recently, a new underwater imaging formation model presented that the
coefficients related to the direct and backscatter transmission signals are
dependent on the type of water, camera specifications, water depth, and imaging
range. This paper proposes an underwater color correction method that
integrates this new model on an underwater robot, using information from a
pressure depth sensor for water depth and a visual odometry system for
estimating scene distance. Experiments were performed with and without a color
chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the
performance of our proposed method by comparing it with other statistic-,
physic-, and learning-based color correction methods. Applications for our
proposed method include improved 3D reconstruction and more robust underwater
robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Nanoscale electrical conductivity imaging using a nitrogen-vacancy center in diamond
The electrical conductivity of a material can feature subtle, nontrivial, and
spatially-varying signatures with critical insight into the material's
underlying physics. Here we demonstrate a conductivity imaging technique based
on the atom-sized nitrogen-vacancy (NV) defect in diamond that offers local,
quantitative, and noninvasive conductivity imaging with nanoscale spatial
resolution. We monitor the spin relaxation rate of a single NV center in a
scanning probe geometry to quantitatively image the magnetic fluctuations
produced by thermal electron motion in nanopatterned metallic conductors. We
achieve 40-nm scale spatial resolution of the conductivity and realize a
25-fold increase in imaging speed by implementing spin-to-charge conversion
readout of a shallow NV center. NV-based conductivity imaging can probe
condensed-matter systems in a new regime, and as a model example, we project
readily achievable imaging of nanoscale phase separation in complex oxides.Comment: Supplementary information at en
Simultaneous exoplanet detection and instrument aberration retrieval in multispectral coronagraphic imaging
High-contrast imaging for the detection and characterization of exoplanets
relies on the instrument's capability to block out the light of the host star.
Some current post-processing methods for calibrating out the residual speckles
use information redundancy offered by multispectral imaging but do not use any
prior information on the origin of these speckles. We investigate whether
additional information on the system and image formation process can be used to
more finely exploit the multispectral information. We developed an inversion
method in a Bayesian framework that is based on an analytical imaging model to
estimate both the speckles and the object map. The model links the instrumental
aberrations to the speckle pattern in the image focal plane, distinguishing
between aberrations upstream and downstream of the coronagraph. We propose and
validate several numerical techniques to handle the difficult minimization
problems of phase retrieval and achieve a contrast of 10^6 at 0.2 arcsec from
simulated images, in the presence of photon noise. This opens up the the
possibility of tests on real data where the ultimate performance may override
the current techniques if the instrument has good and stable coronagraphic
imaging quality. This paves the way for new astrophysical exploitations or even
new designs for future instruments
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