9,300 research outputs found
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
This paper addresses the problem of crack detection which is essential for
health monitoring of built infrastructure. Our approach includes two stages,
data collection using unmanned aerial vehicles (UAVs) and crack detection using
histogram analysis. For the data collection, a 3D model of the structure is
first created by using laser scanners. Based on the model, geometric properties
are extracted to generate way points necessary for navigating the UAV to take
images of the structure. Then, our next step is to stick together those
obtained images from the overlapped field of view. The resulting image is then
clustered by histogram analysis and peak detection. Potential cracks are
finally identified by using locally adaptive thresholds. The whole process is
automatically carried out so that the inspection time is significantly improved
while safety hazards can be minimised. A prototypical system has been developed
for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and
Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression
From large deviations to semidistances of transport and mixing: coherence analysis for finite Lagrangian data
One way to analyze complicated non-autonomous flows is through trying to
understand their transport behavior. In a quantitative, set-oriented approach
to transport and mixing, finite time coherent sets play an important role.
These are time-parametrized families of sets with unlikely transport to and
from their surroundings under small or vanishing random perturbations of the
dynamics. Here we propose, as a measure of transport and mixing for purely
advective (i.e., deterministic) flows, (semi)distances that arise under
vanishing perturbations in the sense of large deviations. Analogously, for
given finite Lagrangian trajectory data we derive a discrete-time and space
semidistance that comes from the "best" approximation of the randomly perturbed
process conditioned on this limited information of the deterministic flow. It
can be computed as shortest path in a graph with time-dependent weights.
Furthermore, we argue that coherent sets are regions of maximal farness in
terms of transport and mixing, hence they occur as extremal regions on a
spanning structure of the state space under this semidistance---in fact, under
any distance measure arising from the physical notion of transport. Based on
this notion we develop a tool to analyze the state space (or the finite
trajectory data at hand) and identify coherent regions. We validate our
approach on idealized prototypical examples and well-studied standard cases.Comment: J Nonlinear Sci, 201
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