Evaluation of signal detection performance with pseudocolor display and lumpy backgrounds

1997 ◽  
Author(s):  
Hong Li ◽  
Arthur E. Burgess
Keyword(s):  
Signal Detection ◽  
Detection Performance

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.

scholarly journals Suggestibility and signal detection performance in hallucination-prone students

2017 ◽  
Vol 22 (2) ◽  
pp. 159-174 ◽  
Author(s):  
Fatimah Alganami ◽  
Filippo Varese ◽  
Graham F. Wagstaff ◽  
Richard P. Bentall
Keyword(s):  
Signal Detection ◽  
Detection Performance

Study of BLAST Signal Detection Algorithm in LTE System

2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen
Keyword(s):  
Signal Detection ◽  
High Speed ◽  
Data Communication ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Detection Algorithms ◽  
High Speed Data ◽  
Simulation Results ◽  
Better Than

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.

Diazepam-Induced Changes in Signal Detection Performance

1986 ◽  
Vol 16 (4) ◽  
pp. 193-197 ◽  
Author(s):  
A.A.I. Jansen ◽  
J.J. de Gier ◽  
J.L. Slangen
Keyword(s):  
Signal Detection ◽  
Detection Performance ◽  
Induced Changes

scholarly journals A Novel Method of Seismic Signal Detection Using Waveform Features

2020 ◽  
Vol 10 (8) ◽  
pp. 2919
Author(s):  
Jian Li ◽  
Mengmin He ◽  
Gaofeng Cui ◽  
Xiaoming Wang ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Signal Detection ◽  
Probability Model ◽  
Joint Probability ◽  
Seismic Signal ◽  
Detection Performance ◽  
Feature Model ◽  
Location Information ◽  
Seismic Signals ◽  
Historical Events ◽  
Joint Probability Model

The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.

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