Speech Intelligibility Prediction Based on the Envelope Power Spectrum Model with the Dynamic Compressive Gammachirp Auditory Filterbank

By analyzing the evolutionary spectrum method for multivariate nonstationary stochastic processes, a simulation method for fully nonstationary spatially variable ground motion is proposed based on the Kameda time-varying power spectrum model. This method can properly simulate nonstationary spatially variable ground motion based on a target response spectrum. Two numerical examples, in which the Kameda time-varying power spectra are calculated for different conditions, are presented to demonstrate the capabilities of the proposed method. In the first example, the nonstationary spatially variable ground motion that satisfies the time-frequency characteristics and response characteristics of the original ground motion is simulated by identifying the parameters of the given time-varying power spectrum. In the second example, the ground motion that satisfies the design response spectra is simulated by defining the parameters of the time-varying power spectrum directly. The results demonstrate that the method can effectively simulate nonstationary spatially variable ground motion, which implies that the proposed method can be used in engineering applications.

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AUTOMATED MICROARRAY IMAGE GRIDDING USING IMAGE PROJECTION VECTORS COUPLED WITH POWER SPECTRUM MODEL

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141000810x ◽

2010 ◽

Vol 24 (04) ◽

pp. 581-600 ◽

Cited By ~ 1

Author(s):

NING DENG ◽

HUILONG DUAN

Keyword(s):

Power Spectrum ◽

Image Data ◽

Microarray Experiment ◽

Microarray Image ◽

Image Projection ◽

Microarray Datasets ◽

Microarray Analyses ◽

Microarray Image Processing ◽

Spectrum Model ◽

Experimental Comparisons

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.

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