62,293 research outputs found
Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups
A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper
Fast Video Classification via Adaptive Cascading of Deep Models
Recent advances have enabled "oracle" classifiers that can classify across
many classes and input distributions with high accuracy without retraining.
However, these classifiers are relatively heavyweight, so that applying them to
classify video is costly. We show that day-to-day video exhibits highly skewed
class distributions over the short term, and that these distributions can be
classified by much simpler models. We formulate the problem of detecting the
short-term skews online and exploiting models based on it as a new sequential
decision making problem dubbed the Online Bandit Problem, and present a new
algorithm to solve it. When applied to recognizing faces in TV shows and
movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on
GPU/CPU) relative to a state-of-the-art convolutional neural network, at
competitive accuracy.Comment: Accepted at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Application of graphics processing units to search pipelines for gravitational waves from coalescing binaries of compact objects
We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in CPU count required for the detection of inspiral sources afforded by the use of GPUs
Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles
A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions
FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
Matched-filter searches for gravitational waves from coalescing compact
binaries by the LIGO Scientific Collaboration use the FINDCHIRP algorithm: an
implementation of the optimal filter with innovations to account for unknown
signal parameters and to improve performance on detector data that has
nonstationary and non-Gaussian artifacts. We provide details on the FINDCHIRP
algorithm as used in the search for subsolar mass binaries, binary neutron
stars, neutron star-black hole binaries, and binary black holes.Comment: 19 pages, 1 figure, journal version with Creative Commons 4.0
open-access license adde
Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts
We apply a machine learning algorithm, the artificial neural network, to the
search for gravitational-wave signals associated with short gamma-ray bursts.
The multi-dimensional samples consisting of data corresponding to the
statistical and physical quantities from the coherent search pipeline are fed
into the artificial neural network to distinguish simulated gravitational-wave
signals from background noise artifacts. Our result shows that the data
classification efficiency at a fixed false alarm probability is improved by the
artificial neural network in comparison to the conventional detection
statistic. Therefore, this algorithm increases the distance at which a
gravitational-wave signal could be observed in coincidence with a gamma-ray
burst. In order to demonstrate the performance, we also evaluate a few seconds
of gravitational-wave data segment using the trained networks and obtain the
false alarm probability. We suggest that the artificial neural network can be a
complementary method to the conventional detection statistic for identifying
gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure
LUNASKA experiments using the Australia Telescope Compact Array to search for ultra-high energy neutrinos and develop technology for the lunar Cherenkov technique
We describe the design, performance, sensitivity and results of our recent
experiments using the Australia Telescope Compact Array (ATCA) for lunar
Cherenkov observations with a very wide (600 MHz) bandwidth and nanosecond
timing, including a limit on an isotropic neutrino flux. We also make a first
estimate of the effects of small-scale surface roughness on the effective
experimental aperture, finding that contrary to expectations, such roughness
will act to increase the detectability of near-surface events over the neutrino
energy-range at which our experiment is most sensitive (though distortions to
the time-domain pulse profile may make identification more difficult). The aim
of our "Lunar UHE Neutrino Astrophysics using the Square Kilometer Array"
(LUNASKA) project is to develop the lunar Cherenkov technique of using
terrestrial radio telescope arrays for ultra-high energy (UHE) cosmic ray (CR)
and neutrino detection, and in particular to prepare for using the Square
Kilometer Array (SKA) and its path-finders such as the Australian SKA
Pathfinder (ASKAP) and the Low Frequency Array (LOFAR) for lunar Cherenkov
experiments.Comment: 27 pages, 18 figures, 4 tables
Results from an Einstein@Home search for continuous gravitational waves from Cassiopeia A, Vela Jr. and G347.3
We report results of the most sensitive search to date for periodic
gravitational waves from Cassiopeia A, Vela Jr. and G347.3 with frequency
between 20 and 1500 Hz. The search was made possible by the computing power
provided by the volunteers of the Einstein@Home project and improves on
previous results by a factor of 2 across the entire frequency range for all
targets. We find no significant signal candidate and set the most stringent
upper limits to date on the amplitude of gravitational wave signals from the
target population, corresponding to sensitivity depths between 54 and 83 , depending on the
target and the frequency range. At the frequency of best strain sensitivity,
near Hz, we set 90% confidence upper limits on the gravitational wave
intrinsic amplitude of , probing ellipticity values
for Vela Jr. as low as , assuming a distance of 200 pc
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