46,555 research outputs found
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video:
https://youtu.be/0q6ap_OSBA
Experimental assessment of presumed filtered density function models
Measured filtered density functions (FDFs) as well as assumed beta distribution model of mixture fraction and “subgrid” scale (SGS) scalar variance, used typically in large eddy simulations, were studied by analysing experimental data, obtained from two-dimensional planar, laser induced fluorescence measurements in isothermal swirling turbulent flows at a constant Reynolds number of 29 000 for different swirl numbers (0.3, 0.58, and 1.07)
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories
Human action recognition (HAR) is at the core of human-computer interaction and video scene understanding. However, achieving effective HAR in an unconstrained environment is still a challenging task. To that end, trajectory-based video representations are currently widely used. Despite the promising levels of effectiveness achieved by these approaches, problems regarding computational complexity and the presence of redundant trajectories still need to be addressed in a satisfactory way. In this paper, we propose a method for trajectory rejection, reducing the number of redundant trajectories without degrading the effectiveness of HAR. Furthermore, to realize efficient optical flow estimation prior to trajectory extraction, we integrate a method for dynamic frame skipping. Experiments with four publicly available human action datasets show that the proposed approach outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity
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Experimental cross-correlation Nitrogen Q-branch CARS Thermometry in a Spark Ignition Engine
A purely experimental technique was employed to derive temperatures from nitrogen Q-branch Coherent Anti-Stokes Raman Scattering (CARS) spectra, obtained in a high pressure, high temperature environment (spark ignition Otto engine). This was in order to obviate any errors arising from deficiencies in the spectral scaling laws which are commonly used to represent nitrogen Q-branch CARS spectra at high pressure. The spectra obtained in the engine were compared with spectra obtained in a calibrated high pressure, high temperature cell, using direct cross-correlation in place of the minimisation of sums of squares of residuals. The technique is demonstrated through the measurement of air temperature as a function of crankshaft angle inside the cylinder of a motored single-cylinder Ricardo E6 research engine, followed by the measurement of fuel-air mixture temperatures obtained during the compression stroke in a knocking Ricardo E6 engine. A standard CARS program (SANDIA’s CARSFIT) was employed to calibrate the altered non-resonant background contribution to the CARS spectra that was caused by the alteration to the mole fraction of nitrogen in the unburned fuel-air mixture. The compression temperature profiles were extrapolated in order to predict the auto-ignition temperatures
Ash plume properties retrieved from infrared images: a forward and inverse modeling approach
We present a coupled fluid-dynamic and electromagnetic model for volcanic ash
plumes. In a forward approach, the model is able to simulate the plume dynamics
from prescribed input flow conditions and generate the corresponding synthetic
thermal infrared (TIR) image, allowing a comparison with field-based
observations. An inversion procedure is then developed to retrieve ash plume
properties from TIR images.
The adopted fluid-dynamic model is based on a one-dimensional, stationary
description of a self-similar (top-hat) turbulent plume, for which an
asymptotic analytical solution is obtained. The electromagnetic
emission/absorption model is based on the Schwarzschild's equation and on Mie's
theory for disperse particles, assuming that particles are coarser than the
radiation wavelength and neglecting scattering. [...]
Application of the inversion procedure to an ash plume at Santiaguito volcano
(Guatemala) has allowed us to retrieve the main plume input parameters, namely
the initial radius , velocity , temperature , gas mass ratio
, entrainment coefficient and their related uncertainty. Moreover,
coupling with the electromagnetic model, we have been able to obtain a reliable
estimate of the equivalent Sauter diameter of the total particle size
distribution.
The presented method is general and, in principle, can be applied to the
spatial distribution of particle concentration and temperature obtained by any
fluid-dynamic model, either integral or multidimensional, stationary or
time-dependent, single or multiphase. The method discussed here is fast and
robust, thus indicating potential for applications to real-time estimation of
ash mass flux and particle size distribution, which is crucial for model-based
forecasts of the volcanic ash dispersal process.Comment: 41 pages, 13 figures, submitted pape
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