The optimal power law for the detection of a Gaussian burst in a background of Gaussian noise

1991 ◽  
Vol 37 (1) ◽  
pp. 209-214 ◽  
Author(s):  
J.A. Fawcett ◽  
B.H. Maranda
Keyword(s):  
Power Law ◽  
Gaussian Noise ◽  
Optimal Power

scholarly journals Greedy–Merge Degrading has Optimal Power-Law

2019 ◽  
Vol 65 (2) ◽  
pp. 917-934 ◽  
Author(s):  
Assaf Kartowsky ◽  
Ido Tal
Keyword(s):  
Power Law ◽  
Optimal Power

Transient Signal Detection in Non-Gaussian Noise Using Stochastic Resonance and Wavelet Domain Power-Law Detector

2014 ◽  
Vol 945-949 ◽  
pp. 2043-2047
Author(s):  
Hua Yu Dong ◽  
Zhi Yang
Keyword(s):  
Stochastic Resonance ◽  
Power Law ◽  
Gaussian Noise ◽  
Mixture Distribution ◽  
Resonance Method ◽  
Gaussian Mixture ◽  
Transient Signal ◽  
Wavelet Domain ◽  
The Matrix ◽  
Non Gaussian

The detection of weak transient signal buried in non-Gaussian noise is investigated. Non-Gaussian noise is modeled by Gaussian mixture distribution. 3-level quantizer is used as a nondynamic stochastic resonance method to enhance SNR of weak signal. NL-length samples of signal are arranged into a matrix. Every column of the matrix is calculated into M-level decomposition. Based on the squared value of the detail and approximation coefficients, a novel Power-Law detector in wavelet domain is established. Numerical experiments and comparison show that, on the same SNR and false alarm rate, proposed method could provide higher detection probability.

scholarly journals INFORMATION CONTENT IN UNIFORMLY DISCRETIZED GAUSSIAN NOISE: OPTIMAL COMPRESSION RATES

1999 ◽  
Vol 10 (04) ◽  
pp. 687-716 ◽  
Author(s):  
AUGUST ROMEO ◽  
ENRIQUE GAZTAÑAGA ◽  
JOSE BARRIGA ◽  
EMILIO ELIZALDE
Keyword(s):  
Power Spectrum ◽  
White Noise ◽  
Information Content ◽  
Frequency Spectrum ◽  
Power Law ◽  
Compression Ratio ◽  
Gaussian Noise ◽  
Theoretical Problem ◽  
Gaussian Signal ◽  
Power Law Noise

We approach the theoretical problem of compressing a signal dominated by Gaussian noise. We present expressions for the compression ratio which can be reached, under the light of Shannon's noiseless coding theorem, for a linearly quantized stochastic Gaussian signal (noise). The compression ratio decreases logarithmically with the amplitude of the frequency spectrum P(f) of the noise. Entropy values and compression rates are shown to depend on the shape of this power spectrum, given different normalizations. The cases of white noise (w.n.), fnp power-law noise (including 1/f noise), ( w.n. +1/f) noise, and piecewise ( w.n. +1/f | w.n. +1/f2) noise are discussed, while quantitative behaviors and useful approximations are provided.

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