17,069 research outputs found

    Discriminant linear processing of time-frequency plane

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    Extending previous works done on considerably smaller data sets, the paper studies linear discriminant analysis of about 30 hours of phoneme-labeled speech data in the time-frequency domain. Analysis is carried both independently in time and frequency and jointly. Data driven spectral basis show similar frequency sensitivity as human hearing. LDA-derived temporal FIR filters are consistent with temporal lateral inhibition. Considerable improvement is obtained using first temporal discriminant

    Discriminant linear processing of time-frequency plane

    Get PDF
    Extending previous works done on considerably smaller data sets, the paper studies linear discriminant analysis of about 30 hours of phoneme-labeled speech data in the time-frequency domain. Analysis is carried both independently in time and frequency and jointly. Data driven spectral basis show similar frequency sensitivity as human hearing. LDA-derived temporal FIR filters are consistent with temporal lateral inhibition. Considerable improvement is obtained using first temporal discriminant

    Radar data processing and analysis

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    Digitized four-channel radar images corresponding to particular areas from the Phoenix and Huntington test sites were generated in conjunction with prior experiments performed to collect X- and L-band synthetic aperture radar imagery of these two areas. The methods for generating this imagery are documented. A secondary objective was the investigation of digital processing techniques for extraction of information from the multiband radar image data. Following the digitization, the remaining resources permitted a preliminary machine analysis to be performed on portions of the radar image data. The results, although necessarily limited, are reported

    Tracking of motor vehicles from aerial video imagery using the OT-MACH correlation filter

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    Accurately tracking moving targets in a complex scene involving moving cameras, occlusions and targets embedded in noise is a very active research area in computer vision. In this paper, an optimal trade-off maximum correlation height (OT-MACH) filter has been designed and implemented as a robust tracker. The algorithm allows selection of different objects as a target, based on the operator’s requirements. The user interface is designed so as to allow the selection of a different target for tracking at any time. The filter is updated, at a frequency selected by the user, which makes the filter more resistant to progressive changes in the object’s orientation and scale. The tracker has been tested on both colour visible band as well as infra-red band video sequences acquired from the air by the Sussex County police helicopter. Initial testing has demonstrated the ability of the filter to maintain a stable track on vehicles despite changes of scale, orientation and lighting and the ability to re-acquire the track after short losses due to the vehicle passing behind occlusions

    Centrifugal instability of Stokes layers in crossflow: the case of a forced cylinder wake

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    The wake flow around a circular cylinder at Re≈100Re\approx100 performing rotatory oscillations has been thoroughly discussed in the literature, mostly focusing on the modifications to the natural B\'enard-von K\'arm\'an vortex street that result from the forced shedding modes locked to the rotatory oscillation frequency. The usual experimental and theoretical frameworks at these Reynolds numbers are quasi-two-dimensional, since the secondary instabilities bringing a three-dimensional structure to the cylinder wake flow occur only at higher Reynolds numbers. In the present paper we show that a three-dimensional structure can appear below the usual three-dimensionalization threshold, when forcing with frequencies lower than the natural vortex shedding frequency, at high amplitudes, as a result of a previously unreported mechanism: a pulsed centrifugal instability of the oscillating Stokes layer at the wall of the cylinder. The present numerical investigation lets us in this way propose a physical explanation for the turbulence-like features reported in the recent experimental study of D'Adamo et al. (2011).Comment: 18 pages, 13 figures. To appear in Proc. Roy. Soc. A. For supplementary video material, see http://vimeo.com/12315202

    Spatial land-use inventory, modeling, and projection/Denver metropolitan area, with inputs from existing maps, airphotos, and LANDSAT imagery

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    A landscape model was constructed with 34 land-use, physiographic, socioeconomic, and transportation maps. A simple Markov land-use trend model was constructed from observed rates of change and nonchange from photointerpreted 1963 and 1970 airphotos. Seven multivariate land-use projection models predicting 1970 spatial land-use changes achieved accuracies from 42 to 57 percent. A final modeling strategy was designed, which combines both Markov trend and multivariate spatial projection processes. Landsat-1 image preprocessing included geometric rectification/resampling, spectral-band, and band/insolation ratioing operations. A new, systematic grid-sampled point training-set approach proved to be useful when tested on the four orginal MSS bands, ten image bands and ratios, and all 48 image and map variables (less land use). Ten variable accuracy was raised over 15 percentage points from 38.4 to 53.9 percent, with the use of the 31 ancillary variables. A land-use classification map was produced with an optimal ten-channel subset of four image bands and six ancillary map variables. Point-by-point verification of 331,776 points against a 1972/1973 U.S. Geological Survey (UGSG) land-use map prepared with airphotos and the same classification scheme showed average first-, second-, and third-order accuracies of 76.3, 58.4, and 33.0 percent, respectively

    Classification of chirp signals using hierarchical bayesian learning and MCMC methods

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    This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm
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