Conical cut radar cross section calculations for a thin, perfectly conducting plate

Radar Cross Section (RCS) calculations for flat, perfectly conducting plates are readily available through the use of conventional frequency domain techniques such as the Method of Moments. However, if time domain scattering or wideband frequency domain results are desired, then the Finite Difference Time Domain (FDTD) technique is a suitable choice. In this paper, we present the application of the Finite Difference Time Domain (FDTD) technique to the problem of electromagnetic scattering and RCS calculations from a thin, perfectly conducting plate for a conical cut in the scattering angle phi. RCS calculations versus angle phi will be presented and discussed

Conical cut radar cross section calculations for a thin, perfectly conducting plate

Radar cross section (RCS) calculations for flat, perfectly conducting plates are readily available through the use of conventional frequency domain techniques such as the method of moments. However, if time domain scattering or wideband frequency domain results are desired, then the finite difference time domain (FDTD) technique is a suitable choice. We present the application of the FDTD technique to the problem of electromagnetic scattering and RCS calculations from a thin, perfectly conducting plate for a conical cut in the scattering angle phi. RCS calculations versus angle phi are presented and discussed

Time domain scattering and radar cross section calculations for a thin, coated perfectly conducting plate

Radar cross section (RCS) calculations for flat, perfectly conducting plates are readily available through the use of conventional frequency domain techniques such as the Method of Moments (MOM). However, if the plate is covered with a dielectric material that is relatively thick in comparison with the wavelength in the material, these frequency domain techniques become increasingly difficult to apply. We present the application of the Finite Difference Time Domain (FDTD) Technique to the problem of electromagnetic scattering and RCS calculations from a thin, perfectly conducting plate that is coated with a thick layer of lossless dielectric material. Both time domain and RCS calculations are presented and disclosed

Neuroevolution on the Edge of Chaos

Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Furthermore, the echo state networks are compared to networks evolved via neuroevolution. The evolved networks outperform the echo state networks, however, the evolution consumes significant computational resources. It is demonstrated that echo state networks with local connections combine the best of both worlds, the simplicity of random echo state networks and the performance of evolved networks. Finally, it is shown that evolution tends to stay close to the ordered side of the edge of chaos.Comment: To appear in Proceedings of the Genetic and Evolutionary Computation Conference 2017 (GECCO '17

The Cortex and the Critical Point

How the cerebral cortex operates near a critical phase transition point for optimum performance. Individual neurons have limited computational powers, but when they work together, it is almost like magic. Firing synchronously and then breaking off to improvise by themselves, they can be paradoxically both independent and interdependent. This happens near the critical point: when neurons are poised between a phase where activity is damped and a phase where it is amplified, where information processing is optimized, and complex emergent activity patterns arise. The claim that neurons in the cortex work best when they operate near the critical point is known as the criticality hypothesis. In this book John Beggs—one of the pioneers of this hypothesis—offers an introduction to the critical point and its relevance to the brain. Drawing on recent experimental evidence, Beggs first explains the main ideas underlying the criticality hypotheses and emergent phenomena. He then discusses the critical point and its two main consequences—first, scale-free properties that confer optimum information processing; and second, universality, or the idea that complex emergent phenomena, like that seen near the critical point, can be explained by relatively simple models that are applicable across species and scale. Finally, Beggs considers future directions for the field, including research on homeostatic regulation, quasicriticality, and the expansion of the cortex and intelligence. An appendix provides technical material; many chapters include exercises that use freely available code and data sets