Structure formation in electromagnetically driven granular media

We report structure formation in submonolayers of magnetic microparticles subjected to periodic electrostatic and magnetic excitations. Depending on the excitation parameters, we observe the formation of a rich variety of structures: clusters, rings, chains, and networks. The growth dynamics and shapes of the structures are strongly dependent on the amplitude and frequency of the external magnetic field. We find that for pure ac magnetic driving at low densities of particles, the low-frequency magnetic excitation favors clusters while high frequency excitation favors chains and net-like structures. An abrupt phase transition from chains to a network phase was observed for a high density of particles.Comment: 4 pages, 5 figure

Magnetic nanoparticles as efficient bulk pinning centers in type-II superconductors

Enhancement of flux pinning by magnetic nanoparticles embedded into the bulk of type-2 superconductor is studied both theoretically and experimentally. Magnetic part of the pinning force associated with the interaction between a spherical magnetic inclusion and an Abrikosov vortex was calculated in the London approximation. Calculations are supported by the experimental results obtained on sonochemically modified MgB2 superconductor with embedded magnetic Fe2O3 nanoparticles and compared to MgB2 with nonmagnetic Mo2O5 pinning centers of similar concentration and particle size distribution. It is shown that ferromagnetic nanoparticles result in a considerable enhancement of vortex pinning in large-kappa type-2 superconductors.Comment: PDF, 14 page

Velocity Distributions of Granular Gases with Drag and with Long-Range Interactions

We study velocity statistics of electrostatically driven granular gases. For two different experiments: (i) non-magnetic particles in a viscous fluid and (ii) magnetic particles in air, the velocity distribution is non-Maxwellian, and its high-energy tail is exponential, P(v) ~ exp(-|v|). This behavior is consistent with kinetic theory of driven dissipative particles. For particles immersed in a fluid, viscous damping is responsible for the exponential tail, while for magnetic particles, long-range interactions cause the exponential tail. We conclude that velocity statistics of dissipative gases are sensitive to the fluid environment and to the form of the particle interaction.Comment: 4 pages, 3 figure

The Bean model of the critical state in a magnetically shielded superconductor filament

We study the magnetization of a cylindrical type-II superconductor filament covered by a coaxial soft-magnet sheath and exposed to an applied transverse magnetic field. Examining penetration of magnetic flux into the superconductor core of the filament on the basis of the Bean model of the critical state, we find that the presence of a non-hysteretic magnetic sheath can strongly enhance the field of full penetration of magnetic flux. The average magnetization of the superconductor/magnet heterostructure under consideration and hysteresis AC losses in the core of the filament are calculated as well.Comment: 4 pages, 3 figures; Proceedings of the 7th European Conference on Applied Superconductivity, Vienna, Austria, September 11-15, 200

Comment on ”Long-Lived giant number fluctuations in a swarming granular nematic

Narayan et al. (Reports, 6 July 2007, p. 105) reported giant number fluctuations attributed to curvature-driven active currents specific for nonequilibrium nematic systems. We present data demonstrating that similar results can be found in systems of spherical particles due either to inelastic clustering or persistent density inhomogeneity, suggesting two alternative explanations for their results. (1) presented experimental evidence that a fluidized monolayer of macroscopic granular rods in the active nematic phase exhibits giant number fluctuations consistent with a standard deviation growing linearly with the mean, in contrast to the behavior expected for any situation in which the central limit theorem applies. These giant number fluctuations were attributed to curvature-driven active currents specific for nonequilibrium nematic systems. Granular systems often exhibit statistical properties sharply distinct from their equilibrium counterparts, for example, non-Gaussian velocity distributions in dissipative gases (2, 3). Giant number fluctuations were predicted on the basis of perturbation analysis of a nearly spatially uniform state of a generic phenomenological model for active nematics (4). Although we do not question the possibility that giant fluctuations associated with nematic ordering may be present in the system analyzed by Narayan et al., on the basis of two complementary experiments with spherical particles we demonstrate that the linear growth of the standard deviation DN with the mean N can arise either from dynamic inelastic clustering or from persistent density inhomogeneity. We performed experiments with monolayers of spherical grains energized either by mechanical vertical vibration (5) or by an alternating vertical electric field (6). Although the driving mechanisms are very different, the observed behavior is similar: a transition from the uniform gas state for high amplitude driving (vibration or electric field amplitude) to inhomogeneous phaseseparated states at lower amplitudes of the driving. We analyzed DN versus N using two different coarse-graining procedures. The first procedure (P1, temporal averaging first) is identical to that used by Narayan et al. (1). We partitioned the experimental system into M small subsystems of equal size L and measured the number of particles N i in each subsystem. The fluctuation in a given subsystem was calculated from the series of N i versus time by measuring the mean square deviation from the average N for that subsystem. The values of DN for all of the subsystems in the frame were then averaged and plotted against the average N for the experiment. The results of our analyses . This dependence follows from the bimodal probability distribution. , which implies that the standard deviation DN~N To highlight the importance of spatial heterogeneity, we also employed a second procedure (P2, spatial averaging first). The fluctuations in a single image were calculated as the root mean square deviation of the number of particles N i in each subsystem from the global mean N = N 0 /M for that image (N 0 is the total number of particles in all of the subsystems). Then, the standard deviation extracted from a single image was averaged over all images and plotted versus the average of the global mean. For a homogeneous system with spatial and temporal correlations that are small compared with the system size and experiment duration, respectively, the two procedures should give the same result. For those conditions in which the data analyzed included only a single phas

Распознавание подстилающей поверхности земли с помощью сверточной нейронной сети на одноплатном микрокомпьютере

The article presents development results for hardware and software system (micromodule), which detects and classifies underlying surface images of Earth. Given device has size 5.2×7.4×3.1 cm, mass 52 g and uses convolutional neural network based on MobileNetV2 architecture for image classification. The micromodule can be installed on board of a small spacecraft or a light unmanned aerial vehicle (drone). The information provided in this paper could be useful for engineers and researchers who are developing compact budget mobile systems for processing, analyzing and recognition of images