Power spectrum model of visual masking: simulations and empirical data

2013 ◽  
Vol 30 (6) ◽  
pp. 1119 ◽  
Author(s):  
Ignacio Serrano-Pedraza ◽  
Vicente Sierra-Vázquez ◽  
Andrew M. Derrington
2013 ◽  
Vol 13 (9) ◽  
pp. 1176-1176
Author(s):  
I. Serrano-Pedraza ◽  
C. Brash ◽  
J. C. A. Read

Author(s):  
Katsuhiko Yamamoto ◽  
Toshio Irino ◽  
Toshie Matsui ◽  
Shoko Araki ◽  
Keisuke Kinoshita ◽  
...  

2017 ◽  
Vol 141 (5) ◽  
pp. 3970-3970
Author(s):  
Helia Relaño-Iborra ◽  
Alexandre Chabot-Leclerc ◽  
Christoph Scheidiger ◽  
Johannes Zaar ◽  
Torsten Dau

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Huiguo Chen ◽  
Yingmin Li ◽  
Junru Ren

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.


Author(s):  
NING DENG ◽  
HUILONG DUAN

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.


1993 ◽  
Author(s):  
Emily H. Vandiver ◽  
James A. Dawson ◽  
Wanda K. Fluhler ◽  
Eric J. Borg ◽  
Kimberly A. Eubank

Author(s):  
Georg Northoff

Neuroscience has made considerable progress over the last decades in revealing neuronal mechanisms on different levels of brain activity including genetic, molecular, cellular, regional and network levels. However, despite all this progress, no particular model of the brain has commanded consensus. A model of the brain should attribute clear features to the brain, such as its degree of participation in its own processing of stimuli. While primarily a theoretical issue, models of the brain may create major reverberations within neuroscientific investigation and philosophical work on the mind-brain problem. Both philosophers and neuroscientists often presuppose a passive model of the brain wherein the brain passively receives and processes external stimuli. However, recent empirical data do not support a passive view of the brain. Accordingly, I will advocate for an active model of the brain. The empirical support for an active model of brain comes from findings concerning its resting state or spontaneous activity. Empirical data shows that the brain’s stimulus-induced activity results from the integration of spontaneous activity and external stimuli. However, the brain’s activity can vary with respect to the extent of integration of resting state activity and external stimuli. This leads me to suggest what I describe as a spectrum model of the brain. The spectrum model claims that stimulus-induced activity is based on a spectrum or continuum of different possible relationships or balances between spontaneous activity and external stimuli.


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