27,308 research outputs found
Fronts, Domain Growth and Dynamical Scaling in a d=1 non-Potential System
We present a study of dynamical scaling and front motion in a one dimensional
system that describes Rayleigh-Benard convection in a rotating cell. We use a
model of three competing modes proposed by Busse and Heikes to which spatial
dependent terms have been added. As long as the angular velocity is different
from zero, there is no known Lyapunov potential for the dynamics of the system.
As a consequence the system follows a non-relaxational dynamics and the
asymptotic state can not be associated with a final equilibrium state. When the
rotation angular velocity is greater than some critical value, the system
undergoes the Kuppers-Lortz instability leading to a time dependent chaotic
dynamics and there is no coarsening beyond this instability. We have focused on
the transient dynamics below this instability, where the dynamics is still
non-relaxational. In this regime the dynamics is governed by a non-relaxational
motion of fronts separating dynamically equivalent homogeneous states. We
classify the families of fronts that occur in the dynamics, and calculate their
shape and velocity. We have found that a scaling description of the coarsening
process is still valid as in the potential case. The growth law is nearly
logarithmic with time for short times and becomes linear after a crossover,
whose width is determined by the strength of the non-potential terms.Comment: 15 pages, 10 figure
Multi-modal Image Processing based on Coupled Dictionary Learning
In real-world scenarios, many data processing problems often involve
heterogeneous images associated with different imaging modalities. Since these
multimodal images originate from the same phenomenon, it is realistic to assume
that they share common attributes or characteristics. In this paper, we propose
a multi-modal image processing framework based on coupled dictionary learning
to capture similarities and disparities between different image modalities. In
particular, our framework can capture favorable structure similarities across
different image modalities such as edges, corners, and other elementary
primitives in a learned sparse transform domain, instead of the original pixel
domain, that can be used to improve a number of image processing tasks such as
denoising, inpainting, or super-resolution. Practical experiments demonstrate
that incorporating multimodal information using our framework brings notable
benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing
Advances In Wireless Communication
Means and method of measuring viscoelastic strain Patent
Photographic method for measuring viscoelastic strain in solid propellants and other material
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