scholarly journals APPLICATION OF THE SPECTRAL METHOD TO STOCHASTIC FILTER ANALYSIS

2019 ◽  
pp. 128-133
Author(s):  
O.I. Kharchenko
Keyword(s):  
Input Signal ◽  
Gaussian Noise ◽  
Output Signal ◽  
Transfer Functions ◽  
Social Systems ◽  
Harmonic Signal ◽  
White Gaussian Noise ◽  
Periodic Signal ◽  
Noise Power ◽  
Stochastic Filter

The problem of standing out a signal from an additive mixture of a harmonic signal and white Gaussian noise is considered. The analysis is based on the phenomenon of stochastic resonance (SR), which consists in amplifying a periodic signal under the influence of noise of a certain power. SR is a universal physical phenomenon that is typical of some nonlinear systems, and is came out not only in technical, but also in biological and social systems. When calculating the spectral characteristics of the output signal, Volterra series were used. The problem is solved using the transfer functions of Volterra without the initial definition of kernels. Volterra transfer functions are obtained by the harmonic input signal method. The influence of the input signal parameters, in particular the amplitude and frequency of the harmonic signal and the noise power, on the spectral power density of the output signal is studied. Optimal parameters values are determined. Criteria are formulated for using a stochastic filter to standing out a harmonic signal on the background white Gaussian noise.

scholarly journals Calibration of NMR Receiver using Spectrometer Characteristics

2021 ◽  
Vol 21 (6) ◽  
pp. 205-208
Author(s):  
Peter Andris ◽  
Tomáš Dermek ◽  
Ivan Frollo
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Thermal Noise ◽  
Harmonic Signal ◽  
Measured Data ◽  
Signal Generator ◽  
Digital Converter ◽  
Advantages And Disadvantages ◽  
Input And Output ◽  
Analogue To Digital

Abstract This article describes the measurement of the relation between input and output signals using two techniques: with a signal generator and with the thermal noise of a known resistance. Each of the techniques has its advantages and disadvantages. Both methods are tested and the results are compared. The input signal of the receiver is known in volts, while the output signal is in ADC (analogue-to-digital converter) units. It is the main difference versus the gain. Knowledge of the relation enables recalculation of the output signal into the input of the receiver or vice versa. It is important in some experiments. The method with the harmonic signal requires a suitable NMR spectroscopic console, generator of the harmonic signal and an attenuator, the method with the noise requires only the NMR console. It indicates that both methods are simple and cheap. The measured data are processed on a standard PC using common programs.

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.

scholarly journals On Asymptotic Efficiency of the M2M4 Signal-to-Noise Estimator for Deterministic Complex Sinusoids

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4950
Author(s):  
Gianmarco Romano
Keyword(s):  
Covariance Matrix ◽  
Gaussian Noise ◽  
Asymptotic Efficiency ◽  
Noise Power ◽  
Finite Sample ◽  
Signal To Noise ◽  
Numerical Examples ◽  
Unknown Phase ◽  
The Moment ◽  
Asymptotically Efficient

The moment-based M2M4 signal-to-noise (SNR) estimator was proposed for a complex sinusoidal signal with a deterministic but unknown phase corrupted by additive Gaussian noise by Sekhar and Sreenivas. The authors studied its performances only through numerical examples and concluded that the proposed estimator is asymptotically efficient and exhibits finite sample super-efficiency for some combinations of signal and noise power. In this paper, we derive the analytical asymptotic performances of the proposed M2M4 SNR estimator, and we show that, contrary to what it has been concluded by Sekhar and Sreenivas, the proposed estimator is neither (asymptotically) efficient nor super-efficient. We also show that when dealing with deterministic signals, the covariance matrix needed to derive asymptotic performances must be explicitly derived as its known general form for random signals cannot be extended to deterministic signals. Numerical examples are provided whose results confirm the analytical findings.

Control Theory and Decision Making in Organisations A Reconnaissance

1970 ◽  
Vol 3 (3) ◽  
pp. T46-T48 ◽  
Author(s):  
G. L. Mallen
Keyword(s):  
Control Theory ◽  
Control Systems ◽  
Decision Process ◽  
Transfer Functions ◽  
Social Systems ◽  
Simulation Methods ◽  
Industrial Dynamics ◽  
Simulation Approach ◽  
Decision Systems ◽  
Classical Control

Differences between the domains of application of classical control theory and applied cybernetics are examined. It is suggested that a unifying concept for the understanding of both simple mechanical control systems and complex social systems is that of the decision process. Simple decision systems are equated to those for which transfer functions can be specified. Complex systems demand a simulation approach. No prescriptive organisational control theory based on simulation methods yet exists but one is required and is seen to be emerging from such diverse fields as artificial intelligence and Industrial Dynamics.

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