An Approach to Physical Performance Analysis for Judo

Sport performance analysis is a technique that is becoming every year more important for athletes of every level. Many techniques have been developed to measure and analyse efficiently the performance of athletes in some sports, but in combat sports these techniques found in many times their limits, due to the high interaction between the two opponents during the competition. In this paper the problem will be framed. Moreover the physical performance measurement problem will be analysed and three different techniques to manage it will be presented. All the techniques have been used to analyse the performance of 22 high level Judo athletes

Scattering from finite extent frequency selective surface illuminated by arbitrary sources

A method is proposed for the computation of the field scattered from a dichroic plate placed in the near-field region of the feeds. The primary pattern of the feed and dichroic plate assembly has been computed

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

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Definition for polarization P and magnetization M fully consistent with Maxwell's equations

Dealing with the project of metamaterials scientists often have to design circuit elements at a subwavelength (or ``microscopic'') scale. At that scale, they use the set of Maxwell's equations in free-space, and neither permittivity ε nor permeability μ are formally defined. However, the objective is to use the unit cells in order to build a bulk material with some desired ``macroscopic'' properties. At that scale the set of Maxwell's equations in matter is adopted. To pass from one approach to the other is not obvious. In this paper we analyse the classic definitions of polarization P and magnetization M, highlighting their limits. Then we propose a definition for P and M fully consistent with Maxwell's equations at any scale

SNO Based Optimization for Shaped Beam Reflectarray Antennas

The design of a shaped beam reflectarray is a challenging issues. The problem can be hardly addressed by deterministic techniques or standard optimization due to the elevated number of design variables and its non-convex nature while can be much easily solved adopting Evolutionary Optimization Algorithms. In particular, in this paper a recently introduced evolutionary approach, named Social Network Optimization (SNO) has been applied to the design of a reduced size shaped beam reflectarray: the obtained numerical results are promising and prove the effectiveness of the adopted method

Optimization of a dual ring antenna by means of artifcial neural network

In literature, heuristic algorithms have been successfully applied to a number of electromagnetic problems. The associated cost functions are commonly linked to full-wave analysis, leading to complexity and high computational expense. Arti-cial Neural Network is one of the most e®ective biological inspired techniques. In this article, an e±cient surrogate model is trained to replace the full-wave analysis in optimizing the bandwidth of microstrip antenna. The numerical comparison between ANN substitution model and full-wave characterization shows signi-cant improvements in time convergence and computational cost. To verify the robustness of this approach, all these concepts are integrated into a case study represented by a rectangular ring antenna with proximity-coupled feed antenna

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications