In this paper, we propose a novel modified T-shaped planar monopole antenna in that two asymmetric horizontal strips are used as additional resonators to produce the lower and upper resonant modes. As a result, a dual-band antenna for covering 2.4-and 5-GHz wireless local area network (WLAN) bands is implemented. In order to expand the lower band, a multiband antenna for covering the digital communications systems, personal communications systems, Universal Mobile Telecommunications Systems, and 2.4/5-GHz WLAN bands is also developed. Prototypes of the multiband antenna have been successfully implemented. Good omnidirectional radiation in the desired frequency bands has been achieved. The proposed multiband antenna with relatively low profile is very suitable for multiband mobile communication systems.

In this paper, a new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory, and LaSalle invariance principle to solve linear and quadratic programming problems. In particular, a new function F(x, y) is introduced into the energy function E(x, y) such that the function E(x, y) is convex and differentiable, and the resulting network is more efficient. This network involves all the relevant necessary and sufficient optimality conditions for convex quadratic programming problems. For linear programming (LP) and quadratic programming (QP) problems with unique and infinite number of solutions, we have proven strictly that for any initial point, every trajectory of the neural network converges to an optimal solution of the QP and its dual problem. The proposed network is different from the existing networks which use the penalty method or Lagrange method, and the inequality (including nonnegativity) constraints are properly handled. The theory of the proposed network is rigorous and the performance is much better. The simulation results also showthat the proposed neural network is feasible and efficient.

In this paper, a special nonlinear bilevel programming problem (nonlinear BLPP) is transformed into an equivalent single objective nonlinear programming problem. To solve the equivalent problem effectively, we first construct a specific optimization problem with two objectives. By solving the specific problem, we can decrease the leader’s objective value, identify the quality of any feasible solution from infeasible solutions and the quality of two feasible solutions for the equivalent single objective optimization problem, force the infeasible solutions moving toward the feasible region, and improve the feasible solutions gradually. We then propose a new constraint-handling scheme and a specific-design crossover operator. The new constraint-handling scheme can make the individuals satisfy all linear constraints exactly and the nonlinear constraints approximately. The crossover operator can generate high quality potential offspring. Based on the constraint-handling scheme and the crossover operator, we propose a new evolutionary algorithm and prove its global convergence. A distinguishing feature of the algorithm is that it can be used to handle nonlinear BLPPs with nondifferentiable leader’s objective functions. Finally, simulations on 31 benchmark problems, 12 of which have nondifferentiable leader’s objective functions, are made and the results demonstrate the effectiveness of the proposed algorithm.

An optimal allocation model for the subprocessing-element (sub-PE) level redundancy is developed, which is solved by the genetic algorithms. In the allocation model, the average defect density D and the parameter δ are also considered in order to accurately analyze the element yield, where δ is defined as the ratio of the support circuit area to the total area of a PE. When the PE’s area is imposed on the constraint, the optimal solutions of the model with different D and δ are calculated. The simulation results indicate that, for any fixed average defect density D, both the number of the optimal redundant sub-circuit added into a PE and the PE’s yield decrease as δ increases. Moreover, for any fixed parameter δ, the number of the optimal redundant sub-circuit increases, while the optimal yield of the PE decreases, as D increases.

A new reflector shaping technique to improve the beam scanning capability of hybrid antennas (i.e., a reflector system illuminated by a phased array) is presented. Errors between the desired scan directions and the outgoing rays’ directions in the scanning range are minimized by adjusting the reflector shape. This technique can be used to decrease the active element number of the feed array during the antenna scans, or reduce the gain loss caused by beam scanning when the number of active elements is given. An offset reflector antenna system is designed and analyzed, through which the validity of the technique is demonstrated.