15,937 research outputs found
Ghost imaging with engineered quantum states by Hong-Ou-Mandel interference
Traditional ghost imaging experiments exploit position correlations between
correlated states of light. These correlations occur directly in spontaneous
parametric down-conversion (SPDC), and in such a scenario, the two-photon state
used for ghost imaging is symmetric. Here we perform ghost imaging using an
anti-symmetric state, engineering the two-photon state symmetry by means of
Hong-Ou-Mandel interference. We use both symmetric and anti-symmetric states
and show that the ghost imaging setup configuration results in object-image
rotations depending on the state selected. Further, the object and imaging arms
employ spatial light modulators for the all-digital control of the projections,
being able to dynamically change the measuring technique and the spatial
properties of the states under study. Finally, we provide a detailed theory
that explains the reported observations.Comment: Published version. 19 pages, 5 figure
Simultaneous whole-animal 3D-imaging of neuronal activity using light field microscopy
3D functional imaging of neuronal activity in entire organisms at single cell
level and physiologically relevant time scales faces major obstacles due to
trade-offs between the size of the imaged volumes, and spatial and temporal
resolution. Here, using light-field microscopy in combination with 3D
deconvolution, we demonstrate intrinsically simultaneous volumetric functional
imaging of neuronal population activity at single neuron resolution for an
entire organism, the nematode Caenorhabditis elegans. The simplicity of our
technique and possibility of the integration into epi-fluoresence microscopes
makes it an attractive tool for high-speed volumetric calcium imaging.Comment: 25 pages, 7 figures, incl. supplementary informatio
Local Causal States and Discrete Coherent Structures
Coherent structures form spontaneously in nonlinear spatiotemporal systems
and are found at all spatial scales in natural phenomena from laboratory
hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary
climate dynamics. Phenomenologically, they appear as key components that
organize the macroscopic behaviors in such systems. Despite a century of
effort, they have eluded rigorous analysis and empirical prediction, with
progress being made only recently. As a step in this, we present a formal
theory of coherent structures in fully-discrete dynamical field theories. It
builds on the notion of structure introduced by computational mechanics,
generalizing it to a local spatiotemporal setting. The analysis' main tool
employs the \localstates, which are used to uncover a system's hidden
spatiotemporal symmetries and which identify coherent structures as
spatially-localized deviations from those symmetries. The approach is
behavior-driven in the sense that it does not rely on directly analyzing
spatiotemporal equations of motion, rather it considers only the spatiotemporal
fields a system generates. As such, it offers an unsupervised approach to
discover and describe coherent structures. We illustrate the approach by
analyzing coherent structures generated by elementary cellular automata,
comparing the results with an earlier, dynamic-invariant-set approach that
decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht
Adaptive Wavelet Collocation Method for Simulation of Time Dependent Maxwell's Equations
This paper investigates an adaptive wavelet collocation time domain method
for the numerical solution of Maxwell's equations. In this method a
computational grid is dynamically adapted at each time step by using the
wavelet decomposition of the field at that time instant. In the regions where
the fields are highly localized, the method assigns more grid points; and in
the regions where the fields are sparse, there will be less grid points. On the
adapted grid, update schemes with high spatial order and explicit time stepping
are formulated. The method has high compression rate, which substantially
reduces the computational cost allowing efficient use of computational
resources. This adaptive wavelet collocation method is especially suitable for
simulation of guided-wave optical devices
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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