scholarly journals Efficient Detection Approach for DTMF Signal Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 422
Author(s):  
Cheng-Yu Yeh ◽  
Shaw-Hwa Hwang
Keyword(s):  
Signal Detection ◽  
Single Point ◽  
Load Reduction ◽  
Optimal Frequency ◽  
Embedded Devices ◽  
Computational Load ◽  
Efficient Detection ◽  
Detection Approach ◽  
Point Detection ◽  
Tone Detection

A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices.

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.

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.

Traffic Classification Features and its Application in DDoS Detection

2013 ◽  
Vol 380-384 ◽  
pp. 2673-2676
Author(s):  
Ze Yu Xiong
Keyword(s):  
Detection Method ◽  
Single Point ◽  
Attack Detection ◽  
Traffic Classification ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Detection ◽  
Point Detection ◽  
Ddos Attack Detection ◽  
Collaborative Detection

DDoS attacks have relatively low proportion of normal flow in the boundary network at the attack traffic,In this paper,we establish DDoS attack detection method based on defense stage and defensive position, and design and implement collaborative detection of DDoS attacks. Simulation results show that our approach has good timeliness, accuracy and scalability than the single-point detection and route-based distributed detection scheme.

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