scholarly journals Chaos-based weak sinusoidal signal detection approach under colored noise background

2003 ◽  
Vol 52 (3) ◽  
pp. 526
Author(s):  
Li Yue ◽  
Yang Bao-Jun ◽  
Shi Yao-Wu
Keyword(s):  
Signal Detection ◽  
Colored Noise ◽  
Sinusoidal Signal ◽  
Noise Background ◽  
Detection Approach

Associations between collectivism and perception of social threat: a signal detection approach

2019 ◽  
Author(s):  
Tuong-Van Vu ◽  
Catrin Finkenauer ◽  
Lydia Krabbendam
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Social Adjustment ◽  
Emotional Expressions ◽  
Social Threat ◽  
Threat Detection ◽  
Perceptual Sensitivity ◽  
Detection Approach ◽  
Self Construal ◽  
The Individual

Collectivistic orientation, which entails interdependent self-construal and concern for interpersonal harmony and social adjustment, has been suggested to be associated with detecting emotional expressions that signal social threat than individualistic orientation, which entails independent self-construal. The present research tested if this detection is a result of enhanced perceptual sensitivity or of response bias. We used country as proxy of individualism and collectivism (Country IC), measured IC of individuals with a questionnaire (Individual IC) and manipulated IC with culture priming (Situational IC). Dutch participants in the Netherlands (n = 143) and Chinese participants in China (n = 151) performed a social threat detection task where they had to categorize ambiguous facial expressions as “angry” or “not angry”. As the stimuli varied in degrees of scowling and frequency of presentation, we were able to measure the participants' perceptual sensitivity and response bias following the principles of the Signal Detection Theory. On the Country IC level, the results indicated that individualism-representative Dutch participants had higher perceptual sensitivity than collectivism-representative Chinese participants; whereas, Chinese participants were more biased towards categorizing a scowling face as “angry” than the Dutch (i.e. stronger liberal bias). In both groups, collectivism on the Individual IC was associated with a bias towards recognizing a scowling face as “not angry” (i.e. stronger conservative bias). Culture priming (Situational IC) affected neither perceptual sensitivity nor response bias. Our data suggested that cultural differences were in the form of behavioral tendency and IC entails multiple constructs linked to different outcomes in social threat detection.

New targets number estimation method under colored noise background

2012 ◽  
Vol 23 (6) ◽  
pp. 831-837 ◽  
Author(s):  
Jing Wang ◽  
Jianguo Huang ◽  
Jing Han ◽  
Zhenhua Xu
Keyword(s):  
Colored Noise ◽  
Estimation Method ◽  
Noise Background ◽  
New Targets

STOCHASTIC RESONANCE IN SATURATION NONLINEARITIES BASED ON SIGNAL DETECTION

2008 ◽  
Vol 08 (02) ◽  
pp. L229-L235 ◽  
Author(s):  
LEI ZHANG ◽  
JUN HE ◽  
AIGUO SONG
Keyword(s):  
Signal Detection ◽  
White Noise ◽  
Stochastic Resonance ◽  
Gaussian White Noise ◽  
Detection Performance ◽  
Sinusoidal Signal ◽  
Signal Noise ◽  
Noise Ratio

Recently, it was reported that some saturation nonlinearities could effectively act as noise-aided signal-noise-ratio amplifiers. In the letter we consider the signal detection performance of saturation nonlinearities driven by a sinusoidal signal buried in Gaussian white noise. It is showed that the signal detection statistics still undergo a nonmonotonic evolution as noise is raised. We also particularly show that an improvement of the SNR in terms of the first harmonic does not imply the possibility to improve the signal detection performance through stochastic resonance. The study might also complement other reports about stochastic resonance in saturation nonlinearities.

Signal detection using multi-channel seismic data

1996 ◽  
Vol 86 (1A) ◽  
pp. 221-231 ◽  
Author(s):  
Gregory S. Wagner ◽  
Thomas J. Owens
Keyword(s):  
Signal Detection ◽  
Seismic Data ◽  
Spatial Coherence ◽  
Vertical Component ◽  
Principal Component ◽  
Minimum Variance ◽  
Data Detection ◽  
Array Data ◽  
Detection Approach ◽  
Sample Covariance

Abstract We outline a simple signal detection approach for multi-channel seismic data. Our approach is based on the premise that the wave-field spatial coherence increases when a signal is present. A measure of spatial coherence is provided by the largest eigenvalue of the multi-channel data's sample covariance matrix. The primary advantages of this approach are its speed and simplicity. For three-component data, this approach provides a more robust statistic than particle motion polarization. For array data, this approach provides beamforming-like signal detection results without the need to form beams. This approach allows several options for the use of three-component array data. Detection statistics for three-component, vertical-component array, and three different three-component array approaches are compared to conventional and minimum-variance vertical-component beamforming. Problems inherent in principal-component analysis (PCA) in general and PCA of high-frequency seismic data in particular are also discussed. Multi-channel beamforming and the differences between principal component and factor analysis are discussed in the appendix.

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