Time filter

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Alexandria, VA, United States

Epstein M.,Digital Signal
The Journal of the Acoustical Society of America | Year: 2012

Preliminary data [M. Epstein and M. Florentine, Ear. Hear. 30, 234-237 (2009)] obtained using speech stimuli from a visually present talker heard via loudspeakers in a sound-attenuating chamber indicate little difference in loudness when listening with one or two ears (i.e., significantly reduced binaural loudness summation, BLS), which is known as "binaural loudness constancy." These data challenge current understanding drawn from laboratory measurements that indicate a tone presented binaurally is louder than the same tone presented monaurally. Twelve normal listeners were presented recorded spondees, monaurally and binaurally across a wide range of levels via earphones and a loudspeaker with and without visual cues. Statistical analyses of binaural-to-monaural ratios of magnitude estimates indicate that the amount of BLS is significantly less for speech presented via a loudspeaker with visual cues than for stimuli with any other combination of test parameters (i.e., speech presented via earphones or a loudspeaker without visual cues, and speech presented via earphones with visual cues). These results indicate that the loudness of a visually present talker in daily environments is little affected by switching between binaural and monaural listening. This supports the phenomenon of binaural loudness constancy and underscores the importance of ecological validity in loudness research. Source

Bernal D.,Digital Signal
Mechanical Systems and Signal Processing | Year: 2011

Normalization of modes obtained from operational modal analysis has received significant attention since the seminal paper on the use of mass perturbations by Parloo et al., in 2002. The contribution in this paper is a formulation where the square of the scaling constants are computed from an over-determined linear system of equations, obtained by evaluating the pole residue form of the receptance at the eigenvalues of the perturbed system. The formulation is exact when there is no truncation and, independently of truncation, for all conditions where existing solutions are exact. Given that identification results are stochastic, the normalization constants are random variables and the paper examines performance in terms of the bias and the standard deviation of the error distribution. The advantage of the receptance approach over other existing techniques is illustrated. © 2010 Elsevier Ltd.All rights reserved. Source

Bernal D.,Digital Signal
Mechanical Systems and Signal Processing | Year: 2013

Damage detection using a Kalman filter is based on a hypothesis test on the whiteness of innovations. Correlations in the innovations arise when either the properties of the system, or the statistics of the noise processes, deviate from the values used to formulate the filter. It is shown that the correlations from the first source decay with lags at a rate that depends on the open loop eigenvalues, while those from the second depend on the eigenvalues of the closed loop. Given that these two rates differ, the resolution of the Kalman filter as a damage detector depends on the interval of lags used to formulate the discriminating test statistic. Recommendations for selecting an interval that promotes sensitivity to damage over changes in the noise processes are given. The proposed lag shifted whiteness test (LSWT) extends use of the Kalman filter as a damage detector to situations where variability in the loading makes a standard whiteness test ineffective. © 2013 Elsevier Ltd. All rights reserved. Source

A laser radar, or lidar system, employs an asymmetric single-ended detector to detect received signals reflected back from targets. The asymmetric single-ended detector benefits from a reduced part count and fewer optical splices while nearly achieving a same gain as a symmetric differential detector.

A system uses range and Doppler velocity measurements from a lidar system and images from a video system to estimate a six degree-of-freedom trajectory of a target. The system estimates this trajectory in two stages: a first stage in which the range and Doppler measurements from the lidar system along with various feature measurements obtained from the images from the video system are used to estimate first stage motion aspects of the target (i.e., the trajectory of the target); and a second stage in which the images from the video system and the first stage motion aspects of the target are used to estimate second stage motion aspects of the target. Once the second stage motion aspects of the target are estimated, a three-dimensional image of the target may be generated.

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