9,934 research outputs found
Time-frequency detection of Gravitational Waves
We present a time-frequency method to detect gravitational wave signals in
interferometric data. This robust method can detect signals from poorly modeled
and unmodeled sources. We evaluate the method on simulated data containing
noise and signal components. The noise component approximates initial LIGO
interferometer noise. The signal components have the time and frequency
characteristics postulated by Flanagan and Hughes for binary black hole
coalescence. The signals correspond to binaries with total masses between to and with (optimal filter) signal-to-noise ratios of 7
to 12. The method is implementable in real time, and achieves a coincident
false alarm rate for two detectors 1 per 475 years. At this false
alarm rate, the single detector false dismissal rate for our signal model is as
low as 5.3% at an SNR of 10. We expect to obtain similar or better detection
rates with this method for any signal of similar power that satisfies certain
adiabaticity criteria. Because optimal filtering requires knowledge of the
signal waveform to high precision, we argue that this method is likely to
detect signals that are undetectable by optimal filtering, which is at present
the best developed detection method for transient sources of gravitational
waves.Comment: 24 pages, 5 figures, uses REVTE
Unsupervised edge map scoring: a statistical complexity approach
We propose a new Statistical Complexity Measure (SCM) to qualify edge maps
without Ground Truth (GT) knowledge. The measure is the product of two indices,
an \emph{Equilibrium} index obtained by projecting the edge map
into a family of edge patterns, and an \emph{Entropy} index ,
defined as a function of the Kolmogorov Smirnov (KS) statistic.
This new measure can be used for performance characterization which includes:
(i)~the specific evaluation of an algorithm (intra-technique process) in order
to identify its best parameters, and (ii)~the comparison of different
algorithms (inter-technique process) in order to classify them according to
their quality.
Results made over images of the South Florida and Berkeley databases show
that our approach significantly improves over Pratt's Figure of Merit (PFoM)
which is the objective reference-based edge map evaluation standard, as it
takes into account more features in its evaluation
A mathematical morphology based approach for vehicle detection in road tunnels
A novel approach to automatically detect vehicles in road tunnels is presented in this paper. Non-uniform and poor illumination conditions prevail in road tunnels making difficult to achieve robust vehicle detection. In order to cope with the illumination issues, we propose a local higher-order statistic filter to make the vehicle detection invariant to illumination changes, whereas a morphological-based background subtraction is used to generate a convex hull segmentation of the vehicles. An evaluation test comparing our approach with a benchmark object detector shows that our approach outperforms in terms of false detection rate and overlap area detection
Correlation between ultrasonic velocity and magnetic adaptive testing in flake graphite cast iron
A recently developed nondestructive method, called Magnetic Adaptive Testing was applied for investigation of flake graphite cast iron samples having various metallic matrices and graphite structures. MAT is typical by its low required magnetization of samples, because it is based on measurement of families of minor magnetic hysteresis loops. The flat samples were magnetized by an attached yoke and sensitive descriptors of their magnetic/structural state were obtained from evaluation of the measured data. Ultrasonic velocity measurements were performed and results of the non-destructive magnetic tests were compared with these data. A very good correlation was found between the magnetic descriptors and ultrasonic velocity.Web of Science667s17717
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