A novel CPW-fed broadband circularly polarised square slot antenna is proposed and fabricated. The proposed antenna is fed by a CPW with a widened L-type strip along the diagonal line of the square slot. Measured results show that the 3 dB axial-ratio bandwidth of the proposed antenna can reach 17%. The proposed antenna has simple coplanar geometry which can be easily fabricated on inexpensive substrate.

A folded slot with open end is introduced to achieve a dual-band coplanar waveguide (CPW)-fed monopole antenna for radio frequency identification (RFID) applications. The designed antenna, which, including the substrate, is only 32 mm in height and 20 mm in width, can operate simultaneously at the 2.45 and 5.8 GHz bands with -1.8 and 2.3 dBi gains, respectively. Its properties make the antenna suitable for RFID tags.

A novel small CPW-fed T-shaped antenna for MIMO application is presented. The size of the antenna is only 35% of that of conventional CPW-fed T-shaped antenna, which has satisfied return loss bandwidth and omnidirectional radiation characteristics. A multifeed 8-element planar array is fabricated and measured. It shows that the features of small size, uniplanar structure, good radiation characteristics and small mutual coupling are promising for MIMO applications in compact portable devices.

In this paper, a hybrid genetic algorithm for solving constrained optimization problems is addressed. First, a real-coded genetic algorithm is presented. The simplified quadratic interpolation method is then integrated into the genetic algorithm to improve its local search ability and the accuracy of the minimum function value. Simulation results on 13 benchmark problems show that the proposed hybrid algorithm is able to avoid the premature convergence and find much better solutions with high speed compared to other existing algorithms.

The particle swarm optimization algorithm presents a new way for finding an optimal solution of complex optimization problems, where each particle represents a solution to the problem. In this paper a modified particle swarm optimization algorithm is applied to the optimization of the antenna array. Adding an item of integral control and the contractive factor in the modified algorithm can improve its global search ability. Simulation results show that the optimal pattern of the antenna array is able to approach the desired pattern. The results also demonstrate that the modified algorithm is superior to the original algorithm and the nonlinear least-square method.

Adomian’s decomposition method (ADM) is a nonnumerical method which can be adapted for solving nonlinear ordinary differential equations. In this paper, the principle of the decomposition method is described, and its advantages as well as drawbacks are discussed. Then an aftertreatment technique (AT) is proposed, which yields the analytic approximate solution with fast convergence rate and high accurscy through the application of Padé approximation to the series solution derived from ADM. Some concrete examples are also studied to show with numerical results how the AT works efficiently.

The decomposition method is a nonnumerical method of solving strongly nonlinear differential equations. In this paper, the method is adapted for the solution of the one-dimensional nonlinear Poisson’s equations governing the linearly graded p-n junctions in semiconductor devices, and the error analysis for the approximate analytic solutions obtained by the decomposition method is carried out. The simulation results show that the solutions obtained by the method are accurate and reliable, and that the quantitative analysis of the linearly graded p-n junctions can be conducted. This work indicates that the decomposition method has some advantages, which opens up a new way for the numerical analysis of semiconductor devices.

In this two-part series of papers, a new generalized minimax optimization model, termed variable programming (VP), is developed to solve dynamically a class of multi-objective optimization problems with non-decomposable structure. It is demonstrated that such type of problems is more general than existing optimization models. In this part, the VP model is proposed first, and the relationship between variable programming and the general constrained nonlinear programming is established. To illustrate its practicality, problems on investment and the low-side-lobe conformal antenna array pattern synthesis to which VP can be appropriately applied are discussed for substantiation. Then, theoretical underpinnings of the VP problems are established. Difficulties in dealing with the VP problems are discussed. With some mild assumptions, the necessary conditions for the unconstrained VP problems with arbitrary and specific activated feasible sets are derived respectively. The necessary conditions for the corresponding constrained VP problems with the mild hypotheses are also examined. Whilst discussion in this part is concentrated on the formulation of the VP model and its theoretical underpinnings, construction of solution algorithms is discussed in Part II.

In this part of the two-part series of papers, algorithms for solving some variable programming (VP) problems proposed in Part I are investigated. It is demonstrated that the non-differentiability and the discontinuity of the maximum objective function, as well as the summation objective function in the VP problems constitute difficulty in finding their solutions. Based on the principle of statistical mechanics, we derive smooth functions to approximate these non-smooth objective functions with specific activated feasible sets. By transforming the minimax problem and the corresponding variable programming problems into their smooth versions we can solve the resulting problems by some efficient algorithms for smooth functions. Relevant theoretical underpinnings about the smoothing techniques are established. The algorithms, in which the minimization of the smooth functions is carried out by the standard quasi-Newton method with BFGS formula, are tested on some standard minimax and variable programming problems. The numerical results show that the smoothing techniques yield accurate optimal solutions and that the algorithms proposed are feasible and efficient.

The application of the new nonlinear optimization algorithms to arrays design is demonstrated by the low-side-lobe pattern synthesis of conformal arrays. By adopting the information of array element realized gain patterns (RGP’s), we formulate synthesis problems as nonlinearly constrained optimization problems, and propose a new direct method to solve them. The technique allows one to find a new direct method to solve them. The technique allows one to find a set of array coefficients that yield a pattern meeting a specified side-lobe level and achieving the maximum directivity, if such a set of array coefficients exists. If the side-lobe specifications cannot be met with the given array, the technique will result in a set of coefficients that yield a pattern meeting the best attainable side-lobe level and having directivity as high as possible. Also, simplified synthesis problems for an axial dipole array in an infinite, perfectly conducting cylinder are discussed, and numerical synthesis results are given. Our synthesis technique works for general array patterns.