Biomimetic Chaotic Sensor for Moderate Static Magnetic Field

Abstract

The effects of a static magnetic field on systems with chaotic dynamical behavior have attracted little attention so far. Here, Chua’s electronic circuit with an inductor placed in a static uniform magnetic field operating in a chaotic double-scroll regime is studied experimentally. The effect of the magnetic field on the duty cycle factor and the spike count rate, with spikes defined by crossings between the scrolls of the double-scroll attractor, is described. A slow monotonic variation in the duty cycle factor and constant spike count rate is observed for magnetic field intensities up to the threshold, where both these metrics change severely; the dynamic trajectory remains on one scroll and spikes disappear. The dependence of the static magnetic field intensity on Chua’s circuit resistivity at the threshold is given. Two biomimetic magnetic chaotic sensors are proposed: one based on one Chua’s circuit and another that can have various transfer functions and is composed of several independent Chua’s circuits.

Sensitivity Optimization and Experimental Study of the Long-Range Metal Detector Based on Chaotic Duffing Oscillator

“…Hu and Liu [ 8 ] proposed to calculate the largest Lyapunov exponent of the output signal, but this algorithm is computationally expensive. As alternatives, the following methods can be proposed: the analysis of the output signal spectrum [ 18 ] and the analysis of the switching rate between attractor scrolls [ 14 ]. The advantage of the first method is that it can be implemented in a completely analog way.…”

“…One of the possible conversion algorithms is presented in [ 14 ], though we used another method based on a conventional peak detector. The oscillation mode can be determined from the distribution of spikes: When there are no spikes, the oscillator is in a periodic mode.…”

“…A possible schematic of an analog detector is shown in Figure 7. The second type of detector converts the signal of a chaotic oscillator into a signal similar to neuron spikes [14], which makes it possible to apply various effective methods to it, for example, analysis of the distribution of inter-spike intervals and classification of the resulting output using machine learning [15]. This method is applicable to digital computers only, but it is simple and provides great capabilities of recognition using machine learning.…”

“…Such methods as the attractor geometric dimensions analysis [ 3 ], the use of recurrence quantification analysis (RQA) [ 11 ], the number of orbits calculation [ 12 ], feature extraction from attractor with the subsequent interpretation by look-up table (LUT) or machine learning [ 13 ] and other methods were proposed. The convenience of the Duffing and Chua oscillators lies in the fact that the moments when their attractor scrolls are switching can be interpreted as spikes of neurons [ 14 ], which make it possible to apply various effective methods for neuron activity analysis, for example, inter-spike intervals distribution visualization. The spike analysis method proved to be very effective for sensing in our previous study [ 15 ]: we implemented a single-coil metal detector based on a Sprott Case N chaotic oscillator with spiking behavior and demonstrated that the developed metal detector has a continuous response depending on the distance to the target and the target material (steel, aluminum or brass/copper).…”

“…Meanwhile, each of the detection methods requires the choice of the appropriate operating mode for the chaotic system. For example, the attractor geometric dimensions analyzing [ 3 ] requires stable chaotic oscillations, while the analysis of switching between scrolls [ 14 ] is more efficient at the boundary of the periodic and chaotic regimes. Searching for bifurcation parameters providing the highest sensitivity of a chaotic sensor is a non-trivial and little-explored problem.…”