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The design of an optimized Electronically Steerable Passive Array Radiator (ESPAR) antenna is presented. A genetic algorithm using a finite element based cost function optimized the antenna's structure and loading conditions for maximal main lobe gain in a single azimuth direction. Simulated gain results of 7.3 dBi at 2.4 GHz were attained along the antenna's elemental axis. The optimized antenna was fabricated and tested with the corresponding experimental gain better than 8 dBi. The 0.7 dB error between simulated and measured gain was constant for numerous structures and therefore did not affect the optimization. The optimized antenna reduced average main lobe elevation by 15.3 to just 9.7 above the horizontal.

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SUMMARYThis paper studies the problem of minimizing the sum of convex functions that all share a common global variable, each function is known by one specific agent in the network. The underlying network topology is modeled as a time-varying sequence of directed graphs, each of which is endowed with a nondoubly stochastic matrix. We present a distributed method that employs gradient-free oracles and push-sum algorithms for solving this optimization problem. We establish the convergence by showing that the method converges to an approximate solution at the expected rate of O.ln T = p T /, where T is the iteration counter. A numerical example is also given to illustrate the proposed method.