Identification of fast-changing signals by means of adaptive chaotic transformations

The adaptive approach of strongly non-linear fast-changing signals identification is discussed. The approach is devised by adaptive sampling based on chaotic mapping “in yourself” of a signal. Presented sampling way may be utilized online in the automatic control of chemical reactor (throughout identification of concentrations and temperature oscillations in real-time), in medicine (throughout identification of ECG and EEG signals in real-time), etc. In this paper, we presented it to identify the Weierstrass function and ECG signal.

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Identification of the Oscillation Period of Chemical Reactors by Chaotic Sampling of the Conversion Degree

Chemical and Process Engineering ◽

10.2478/cpe-2014-0029 ◽

2014 ◽

Vol 35 (3) ◽

pp. 387-393 ◽

Cited By ~ 4

Author(s):

Marcin Lawnik ◽

Marek Berezowski

Keyword(s):

Real Time ◽

Adaptive Sampling ◽

Tubular Reactor ◽

Chemical Reactor ◽

Oscillation Period ◽

Precise Determination ◽

Conversion Degree ◽

Chaotic Mapping ◽

State Variable

Abstract To stabilise the periodic operation of a chemical reactor the oscillation period should be determined precisely in real time. The method discussed in the paper is based on adaptive sampling of the state variable with the use of chaotic mapping to itself. It enables precise determination of the oscillation period in real time and could be used for a proper control system, that can successfully control the process of chemical reaction and maintain the oscillation period at a set level. The method was applied to a tank reactor and tubular reactor with recycle.

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Design of an Automatic Voltage Regulator Using MATLAB for Real-Time ECG Signal Transmission Monitoring applications

International Journal of Emerging Trends in Engineering Research ◽

10.30534/ijeter/2020/31872020 ◽

2020 ◽

Vol 8 (7) ◽

pp. 3063-3070

Author(s):

Aaron Don M. Africa

Keyword(s):

Real Time ◽

Signal Transmission ◽

Voltage Regulator ◽

Ecg Signal ◽

Automatic Voltage Regulator ◽

Monitoring Applications

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Coastal Ocean Modeling and Observation Program: Real-time Adaptive Sampling Networks

10.21236/ada629699 ◽

1999 ◽

Author(s):

Scott M. Glenn ◽

Dale B. Haidvogel ◽

Oscar M. Schofield

Keyword(s):

Real Time ◽

Adaptive Sampling ◽

Coastal Ocean ◽

Ocean Modeling ◽

Observation Program

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Real-time subsurface scattering with single pass variance-guided adaptive importance sampling

Proceedings of the ACM on Computer Graphics and Interactive Techniques ◽

10.1145/3384536 ◽

2020 ◽

Vol 3 (1) ◽

pp. 1-21

Author(s):

Tiantian Xie ◽

Marc Olano ◽

Brian Karis ◽

Krzysztof Narkowicz

Keyword(s):

Real Time ◽

Adaptive Sampling ◽

Monte Carlo Sampling ◽

Diffusion Profile ◽

Correct Result ◽

Subsurface Scattering ◽

Stochastic Sampling ◽

Light Gradient ◽

Temporal Variance ◽

Importance Function

In real-time applications, it is difficult to simulate realistic subsurface scattering with differing degrees translucency. Burley's reflectance approximation by empirically fitting the diffusion profile as a whole makes it possible to achieve realistic looking subsurface scattering for different translucent materials in screen space. However, achieving a physically correct result requires real-time Monte Carlo sampling of the analytic importance function per pixel per frame, which seems prohibitive to achieve. In this paper, we propose an approximation of the importance function that can be evaluated in real-time. Since subsurface scattering is more pronounced in certain regions (e.g., with light gradient change), we propose an adaptive sampling method based on temporal variance to lower the required number of samples. We propose a one phase adaptive sampling pass that is unbiased, and able to adapt to scene changes due to motion and lighting. To further improve the quality, we explore temporal reuse with a guiding pass prior to the final temporal anti-aliasing (TAA) phase that further improves the quality. Our local guiding pass does not constrain the TAA implementation, and only requires one additional texture to be passed between frames. Our proposed variance-guided algorithm has the potential to make stochastic sampling algorithm effective for real-time rendering.

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