scholarly journals Phase Congruential White Noise Generator

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 118
Author(s):  
Aleksei Deon ◽  
Oleg Karaduta ◽  
Yulian Menyaev
Keyword(s):  
White Noise ◽  
Random Variables ◽  
Noise Generator ◽  
Signal Generator ◽  
White Noise Process ◽  
New Approach ◽  
Noise Process ◽  
White Noise Generator ◽  
Phase Signal ◽  
Simulation Results

White noise generators can use uniform random sequences as a basis. However, such a technology may lead to deficient results if the original sequences have insufficient uniformity or omissions of random variables. This article offers a new approach for creating a phase signal generator with an improved matrix of autocorrelation coefficients. As a result, the generated signals of the white noise process have absolutely uniform intensities at the eigen Fourier frequencies. The simulation results confirm that the received signals have an adequate approximation of uniform white noise.

A consideration of the effect of ear canal resonance and hearing loss upon white noise generators for tinnitus retraining therapy

1997 ◽  
Vol 111 (9) ◽  
pp. 810-813 ◽  
Author(s):  
David M. Baguley ◽  
Graham J. Beynon ◽  
Frances Thornton
Keyword(s):  
Hearing Loss ◽  
White Noise ◽  
Noise Generator ◽  
Major Advance ◽  
Ear Canal ◽  
Tinnitus Retraining Therapy ◽  
Hearing Sensitivity ◽  
Actual Spectrum ◽  
Retraining Therapy ◽  
White Noise Generator

AbstractTinnitus retraining therapy has been heralded as a major advance in the alleviation of tinnitus perception. A cornerstone of this technique is to use white noise produced by a white noise generator (WNG) over a period of several months in order to assist the patient to habituate to their tinnitus. There are three factors which influence the frequency spectrum of the perceived noise such that the perception of white noise from a WNG is unlikely. These factors are the actual spectrum of the emitted noise, the ear canal resonance of the patient and the hearing sensitivity of the patient.Advocates of tinnitus retraining therapy state that white noise is the optimal stimulation to assist habituation of tinnitus. This paper demonstrates that this optimal situation is unlikely to be achieved and that this may account for the long periods needed for patients to achieve benefit from the technique. The development of devices that allow for the above factors to be countered is suggested.

scholarly journals Empirical White Noise Processes and the Subjective Probabilistic Approaches

2019 ◽  
Vol 48 (1) ◽  
pp. 19-30
Author(s):  
András Rövid ◽  
László Palkovics ◽  
Péter Várlaki
Keyword(s):  
White Noise ◽  
Random Number ◽  
White Noise Process ◽  
Random Number Generators ◽  
Noise Process ◽  
Complementary Approach ◽  
Fuzzy Random

The paper discusses the identification of the empirical white noise processes generated by deterministic numerical algorithms.The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of auto-correlated white noise processes change as the order of autocorrelation increases. Although in this paper we rely on random number generators to get approximate white noise processes, in our upcoming research we are planning to turn the focus on physical white noise processes in order to validate our hypothesis.

Twister Generator of Poisson Random Numbers with the Use of Cumulative Frequency Technology

2020 ◽  
pp. 101-123
Author(s):  
A.F. Deon ◽  
D.D. Dmitriev ◽  
Yu.A. Menyaev
Keyword(s):  
Poisson Distribution ◽  
Probability Distributions ◽  
Random Variables ◽  
Convergence In Probability ◽  
Random Numbers ◽  
Cumulative Frequency ◽  
New Approach ◽  
Frequency Distributions ◽  
Simulation Results

The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This paper offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilistic and frequency distributions of the obtained stochastic numbers completely coincide with the theoretical Poisson distribution. Moreover, combining this new approach with the tuning algorithm of basic twister generation allows for a significant increase in length of the created sequences without using additional RAM of the computer

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