Multilayer Bolometric Structures for Efficient Wideband Communication Signal Reception.

It is known that the dielectric layer (resonator) located behind the conducting plate of the bolometer system can significantly increase its sensitivity near the resonance frequencies. In this paper, the possibility of receiving broadband electromagnetic signals in a multilayer bolometric meta-material made of alternating conducting (e.g.

Peculiarities of Resonant Absorption of Electromagnetic Signals in Multilayer Bolometric Sensors.

We examine the effect of resonant absorption of electromagnetic signals in a silicon semiconductor plasma layer when the dielectric plate is placed behind it both experimentally and numerically. It is shown that such plate acts as a dielectric resonator and can significantly increase the electromagnetic energy absorption in the semiconductor for certain frequencies determined by the dielectric plate parameters. Numerical modelling of the effect is performed under the conditions of conducted experiment.

Tunable superconducting neurons for networks based on radial basis functions.

The hardware implementation of signal microprocessors based on superconducting technologies seems relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron.

Issues with Modeling a Tunnel Communication Channel through a Plasma Sheath.

We consider two of the most relevant problems that arise when modeling the properties of a tunnel radio communication channel through a plasma layer. First, we studied the case of the oblique incidence of electromagnetic waves on a layer of ionized gas for two wave polarizations. The resonator parameters that provide signal reception at a wide solid angle were found.

Adiabatic superconducting cells for ultra-low-power artificial neural networks.

We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.