3,595 research outputs found
Nonlinear Compressive Particle Filtering
Many systems for which compressive sensing is used today are dynamical. The
common approach is to neglect the dynamics and see the problem as a sequence of
independent problems. This approach has two disadvantages. Firstly, the
temporal dependency in the state could be used to improve the accuracy of the
state estimates. Secondly, having an estimate for the state and its support
could be used to reduce the computational load of the subsequent step. In the
linear Gaussian setting, compressive sensing was recently combined with the
Kalman filter to mitigate above disadvantages. In the nonlinear dynamical case,
compressive sensing can not be used and, if the state dimension is high, the
particle filter would perform poorly. In this paper we combine one of the most
novel developments in compressive sensing, nonlinear compressive sensing, with
the particle filter. We show that the marriage of the two is essential and that
neither the particle filter or nonlinear compressive sensing alone gives a
satisfying solution.Comment: Accepted to CDC 201
Approximative two-flavor framework for neutrino oscillations with nonstandard interactions
In this paper, we develop approximative two-flavor neutrino oscillation
formulas including subleading nonstandard interaction effects. Especially, the
limit when the small mass-squared difference approaches zero is investigated.
The approximate formulas are also tested against numerical simulations in order
to determine their accuracy and they will probably be most useful in the GeV
energy region, which is the energy region where most upcoming neutrino
oscillation experiments will be operating. Naturally, it is important to have
analytical formulas in order to interpret the physics behind the degeneracies
between standard and nonstandard parameters.Comment: 21 pages, 7 figures, REVTeX4. Final version published in Phys. Rev.
Initial experiments concerning quantum information processing in rare-earth-ion doped crystals
In this paper initial experiments towards constructing simple quantum gates
in a solid state material are presented. Instead of using specially tailored
materials, the aim is to select a subset of randomly distributed ions in the
material, which have the interaction necessary to control each other and
therefore can be used to do quantum logic operations. The experimental results
demonstrate that part of an inhomogeneously broadened absorption line can be
selected as a qubit and that a subset of ions in the material can control the
resonance frequency of other ions. This opens the way for the construction of
quantum gates in rare-earth-ion doped crystals.Comment: 24 pages, including 12 figure
Nonlinear Basis Pursuit
In compressive sensing, the basis pursuit algorithm aims to find the sparsest
solution to an underdetermined linear equation system. In this paper, we
generalize basis pursuit to finding the sparsest solution to higher order
nonlinear systems of equations, called nonlinear basis pursuit. In contrast to
the existing nonlinear compressive sensing methods, the new algorithm that
solves the nonlinear basis pursuit problem is convex and not greedy. The novel
algorithm enables the compressive sensing approach to be used for a broader
range of applications where there are nonlinear relationships between the
measurements and the unknowns
Robust Subspace System Identification via Weighted Nuclear Norm Optimization
Subspace identification is a classical and very well studied problem in
system identification. The problem was recently posed as a convex optimization
problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this
framework to handle outliers. The proposed framework takes the form of a convex
optimization problem with an objective that trades off fit, rank and sparsity.
As in robust PCA, it can be problematic to find a suitable regularization
parameter. We show how the space in which a suitable parameter should be sought
can be limited to a bounded open set of the two dimensional parameter space. In
practice, this is very useful since it restricts the parameter space that is
needed to be surveyed.Comment: Submitted to the IFAC World Congress 201
Energy Disaggregation via Adaptive Filtering
The energy disaggregation problem is recovering device level power
consumption signals from the aggregate power consumption signal for a building.
We show in this paper how the disaggregation problem can be reformulated as an
adaptive filtering problem. This gives both a novel disaggregation algorithm
and a better theoretical understanding for disaggregation. In particular, we
show how the disaggregation problem can be solved online using a filter bank
and discuss its optimality.Comment: Submitted to 51st Annual Allerton Conference on Communication,
Control, and Computin
Blind Identification via Lifting
Blind system identification is known to be an ill-posed problem and without
further assumptions, no unique solution is at hand. In this contribution, we
are concerned with the task of identifying an ARX model from only output
measurements. We phrase this as a constrained rank minimization problem and
present a relaxed convex formulation to approximate its solution. To make the
problem well posed we assume that the sought input lies in some known linear
subspace.Comment: Submitted to the IFAC World Congress 2014. arXiv admin note: text
overlap with arXiv:1303.671
Matter Enhanced Neutrino Oscillations with a Realistic Earth Density Profile
We have investigated matter enhanced neutrino oscillations with a
mantle-core-mantle step function and a realistic Earth matter density profile
in both a two and a three neutrino scenario. We found that the realistic Earth
matter density profile can be well approximated with the mantle-core-mantle
step function and that there could be an influence on the oscillation channel
due to resonant enhancement of one of the mixing angles.Comment: 8 pages, 5 figures (PostScript), MPLA LaTe
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