Invariant activity detection of a constant magnitude signal with unknown parameters in white Gaussian noise

2009 ◽  
Vol 3 (8) ◽  
pp. 1420 ◽  
Author(s):  
M. Derakhtian ◽  
A.A. Tadaion ◽  
S. Gazor ◽  
M.M. Nayebi
Keyword(s):  
Gaussian Noise ◽  
White Gaussian Noise ◽  
Activity Detection ◽  
Unknown Parameters ◽  
Constant Magnitude

scholarly journals Smooth Signal Activity Detection in White Gaussian Noise: Application to P300 Detection

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Reza Boostani ◽  
Negar Kheirandish
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Hypothesis Test ◽  
White Gaussian Noise ◽  
Least Square ◽  
Unknown Parameters ◽  
Detection Scheme ◽  
Ml Estimation ◽  
Unknown Variance ◽  
Synthetic Datasets

<div>Abstract—In this research, we have proposed a new scheme to detect and extract the activity of an unknown smooth template in presence of white Gaussian noise with unknown variance. In this regard, the problem is considered a binary hypothesis test, and it is solved employing the generalized likelihood ratio (GLR) method. GLR test uses the maximum likelihood (ML) estimation of unknown parameters under each hypothesis. The ML estimation of the desired signal yields an optimization problem with smoothness constraint which is in the form of a conventional least square error estimation problem and can be solved optimally. The proposed detection scheme is studied for P300 elicitation from the background electroencephalography signal. In addition, to assume the P300 smoothness, two prior knowledge are considered in terms of positivity and approximate occurrence time of P300. The performance of the method is assessed on both real and synthetic datasets in different noise levels and compared to a conventional signal detection scheme without considering smoothness priors, as well as state-of-theart linear and quadratic discriminant analysis. The results are illustrated in terms of detection probability, false alarm rate, and accuracy. The proposed method outperforms the counterparts in low signal-to-noise ratio situations.</div>

scholarly journals Smooth Signal Activity Detection in White Gaussian Noise: Application to P300 Detection

2021 ◽  
Author(s):  
Ali Mobaien ◽  
Reza Boostani ◽  
Negar Kheirandish
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Hypothesis Test ◽  
White Gaussian Noise ◽  
Least Square ◽  
Unknown Parameters ◽  
Detection Scheme ◽  
Ml Estimation ◽  
Unknown Variance ◽  
Synthetic Datasets

<div>Abstract—In this research, we have proposed a new scheme to detect and extract the activity of an unknown smooth template in presence of white Gaussian noise with unknown variance. In this regard, the problem is considered a binary hypothesis test, and it is solved employing the generalized likelihood ratio (GLR) method. GLR test uses the maximum likelihood (ML) estimation of unknown parameters under each hypothesis. The ML estimation of the desired signal yields an optimization problem with smoothness constraint which is in the form of a conventional least square error estimation problem and can be solved optimally. The proposed detection scheme is studied for P300 elicitation from the background electroencephalography signal. In addition, to assume the P300 smoothness, two prior knowledge are considered in terms of positivity and approximate occurrence time of P300. The performance of the method is assessed on both real and synthetic datasets in different noise levels and compared to a conventional signal detection scheme without considering smoothness priors, as well as state-of-theart linear and quadratic discriminant analysis. The results are illustrated in terms of detection probability, false alarm rate, and accuracy. The proposed method outperforms the counterparts in low signal-to-noise ratio situations.</div>

Signal Activity Detection of Offset-QPSK in Colored Gaussian Noise

2011 ◽  
Vol E94-A (11) ◽  
pp. 2226-2229
Author(s):  
Sayed Jalal ZAHABI ◽  
Mohammadali KHOSRAVIFARD ◽  
Ali A. TADAION ◽  
T. Aaron GULLIVER
Keyword(s):  
Gaussian Noise ◽  
Activity Detection ◽  
Signal Activity ◽  
Colored Gaussian Noise

scholarly journals Effects of white Gaussian noise on dynamic balance in healthy young adults

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ziyou Zhou ◽  
Can Wu ◽  
Zhen Hu ◽  
Yujuan Chai ◽  
Kai Chen ◽  
...  
Keyword(s):  
Young Adults ◽  
Gaussian Noise ◽  
Center Of Pressure ◽  
Dynamic Balance ◽  
White Gaussian Noise ◽  
Fluctuation Analysis ◽  
Balance Performance ◽  
Short Term ◽  
Crossover Point ◽  
Short Time

AbstractIt has been known that short-time auditory stimulation can contribute to the improvement of the balancing ability of the human body. The present study aims to explore the effects of white Gaussian noise (WGN) of different intensities and frequencies on dynamic balance performance in healthy young adults. A total of 20 healthy young participants were asked to stand at a dynamic balance force platform, which swung along the x-axis with an amplitude of ± 4° and frequency of 1 Hz. Their center of pressure (COP) trajectories were recorded when they were stimulated by WGN of different intensities (block 1) and different frequencies (block 2). A traditional method and detrended fluctuation analysis (DFA) were used for data preprocessing. The authors found that only with 75–85 dB WGN, the COP parameters improved. WGN frequency did not affect the dynamic balance performance of all the participants. The DFA results indicated stimulation with 75 dB WGN enhanced the short-term index and reduced the crossover point. Stimulation with 500 Hz and 2500 Hz WGN significantly enhanced the short-term index. These results suggest that 75 dB WGN and 500 Hz and 2500 Hz WGN improved the participants’ dynamic balance performance. The results of this study indicate that a certain intensity of WGN is indispensable to achieve a remarkable improvement in dynamic balance. The DFA results suggest that WGN only affected the short-term persistence, indicating the potential of WGN being considered as an adjuvant therapy in low-speed rehabilitation training.

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