Petr Boriskov

This study proposes a method for analyzing synchronization in oscillator systems, illustrated by modeling the dynamics of a circuit of two resistively coup

This paper studies the removal of salt-and-pepper noise from images using median filter (MF) and simple three-layer autoencoder (AE) within recursive threshold algorithm. The performance of denoising is assessed with two metrics: the standard Structural Similarity Index SSIMImg of restored and clean images and a newly applied metric SSIMMap - the SSIM of entropy maps of these images computed via 2D Sample Entropy in sliding windows. We shown that SSIMMap is more sensitive to blur and local intensity transitions and complements SSIMImg. Experiments on low- and high-resolution grayscales images demonstrate that recursive threshold MF robustly restores images even under strong noise (50-60 %), whereas simple AE is only capable of restoring images with low levels of noise (<30 %). We propose two scalable schemes: (i) 2MF, which uses two MFs with different window sizes and a final thresholding step, effective for highlighting sharp local details at low resolution; and (ii) MFs-AE, which aggregates features from multiple MFs via an AE and is beneficial for restoring the overall scene structure at higher resolution. Owing to its simplicity and computational efficiency, MF remains preferable for deployment on resource-constrained platforms (edge/IoT), whereas AE underperforms without prior denoising. The results also validate the practical value of SSIMMap for objective blur assessment and denoising parameter tuning.

Understanding and quantifying chaos from data remains challenging. We present a data-driven method for estimating the largest Lyapunov exponent (LLE) from one-dimensional chaotic time series using machine learning. A predictor is trained to produce out-of-sample, multi-horizon forecasts; the LLE is then inferred from the exponential growth of the geometrically averaged forecast error across the horizon, which serves as a proxy for trajectory divergence. We validate the approach on four canonical 1D maps—logistic, sine, cubic, and Chebyshev—achieving Rpos2 > 0.99 against reference LLE curves with series as short as M = 450. Among baselines, k-nearest neighbor (KNN) yields the closest fits (KNN-R comparable; random forest larger deviations). By design the estimator targets positive exponents: in periodic/stable regimes, it returns values indistinguishable from zero. Noise robustness is assessed by adding zero-mean white measurement noise and summarizing performance vs the average signal-to-noise ratio (SNR) over parameter sweeps: accuracy saturates for SNRm ≳ 30 dB and collapses below ≈27 dB, a conservative sensor-level benchmark. The method is simple, computationally efficient, and model-agnostic, requiring only stationarity and the presence of a dominant positive exponent. It offers a practical route to LLE estimation in experimental settings where only scalar time-series measurements are available, with extensions to higher-dimensional and irregularly sampled data left for future work.

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R²~0.9. The Hindmarsh–Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R²~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.

The study presents 1D discrete map (DM) to describe the dynamics of the oscillator with chaotic pulse position modulation (PPM). The model circuit has pulse voltage-controlled oscillator (PVCO) and feedback (FB) loop with a threshold of pulse rate coding, which performs non-retriggerable monostable multivibrator (MMV). DM is based on the analysis of this circuit using a simple approximation of the frequency modulation, which includes a threshold condition on the pulse period and sigmoid function of rate coding. The model circuit and DM demonstrate dynamic chaos in a wide range of control parameters. The transition to the chaos occurs by a jump either from a fixed point (tangent bifurcation), or from a limit cycle. An experimental (digital-analog) circuit of the chaotic pulse oscillator, in which the FB unit is MMV with a microcontroller (MC), is implemented. The relationship between the presented DM and the well-known sawtooth (Bernoulli) map (STM), widely used in engineering, is discussed.

Due to the increase in CO₂ emissions as a pollutant, irreversible damages are inflicted on humans and environment. Therefore, policies must be implemented to control and reduce CO₂ emissions. Analysis and forecasting of CO₂ emission is a crucial subject for determining suitable policies. The aim of this paper is to use grey system theory for predicting the CO₂ emissions. The factors oil, gas and coal burning are considered as the inputs that affect the CO₂ emissions. The GM(1,4) model is used for predicting CO₂ emissions. The effect of each item in the CO₂ emissions is investigated. A fitting polynomial function with weighted data is proposed for evaluating CO₂ emissions in different situations. This is essential for selecting the most appropriate policy scenario to reduce air pollution. Numerical results show that the method is robust and accurate.

The paper presents an experimental implementation of a chaotic oscillator based on leaky integrate-and-fire (LIF) neuron circuit with a switching element, which

This paper presents a model and experimental study of a chaotic spike oscillator based on a leaky integrate-and-fire (LIF) neuron, which has a switching element with an S-type current-voltage characteristic (S-switch). The oscillator generates spikes of the S-switch in the form of chaotic pulse position modulation driven by the feedback with rate coding instability of LIF neuron. The oscillator model with piecewise function of the S-switch has resistive feedback using a second order filter. The oscillator circuit is built on four operational amplifiers and two field-effect transistors (MOSFETs) that form an S-switch based on a Schmitt trigger, an active RC filter and a matching amplifier. We investigate the bifurcation diagrams of the model and the circuit and calculate the entropy of oscillations. For the analog circuit, the “regular oscillation-chaos” transition is analysed in a series of tests initiated by a step voltage in the matching amplifier. Entropy values are used to estimate the average time for the transition of oscillations to chaos and the degree of signal correlation of the transition mode of different tests. Study results can be applied in various reservoir computing applications, for example, in choosing and configuring the LogNNet network reservoir circuits.

We analyze the effect of tungsten doping of sol–gel derived polycrystalline vanadium dioxide films on the temperature dependence of their resistivity across the metal–semiconductor transition. Experimental resistivity hysteresis data demonstrate that, with increasing doping level, the phase transition temperature of the films decreases, their hysteresis loop extends over a broader temperature range, and their resistivity jump drops. We have constructed coercive temperature distribution functions and compared them with size distributions of crystalline grains in the undoped and doped films. The results lead us to conclude that tungsten doping of vanadium dioxide has no effect (at least up to 6 at % W) on structure formation in polycrystalline films. The observed changes in phase transition parameters upon doping are, most likely, caused by the increase in donor defect density and the large scatter in dopant concentration in individual grains.