firooz keyvani | Iran University of Science and Technology (IUST) (original) (raw)
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Papers by firooz keyvani
IET Signal Processing, 2018
An optimal placement for planar antenna array elements is proposed to achieve a precise direction... more An optimal placement for planar antenna array elements is proposed to achieve a precise direction of arrival (DOA) estimation on a limited bound. For this purpose, the Cramer-Rao lower bound is chosen as a suitable and useful criterion. In order to have an accurate isotropic DOA estimation, the placement of the antenna elements in the array configuration is achieved by using the invasive weed optimisation algorithm. Moreover, the optimisation results show higher efficiency compared with the similar uniform circular array. It is proved that the optimised arrays are ambiguity free, also.
International Journal of Aviation, Aeronautics, and Aerospace, 2019
Domain-Driven Solver (DDS) is a MATLAB-based software package for convex optimization problems in... more Domain-Driven Solver (DDS) is a MATLAB-based software package for convex optimization problems in Domain-Driven form [19]. The current version of DDS accepts every combination of the following function/set constraints: (1) symmetric cones (LP, SOCP, and SDP); (2) quadratic constraints that are SOCP representable; (3) direct sums of an arbitrary collection of 2-dimensional convex sets defined as the epigraphs of univariate convex functions (including as special cases geometric programming and entropy programming); (4) generalized power cone; (5) epigraphs of matrix norms (including as a special case minimization of nuclear norm over a linear subspace); (6) vector relative entropy; (7) epigraphs of quantum entropy and quantum relative entropy; and (8) constraints involving hyperbolic polynomials. DDS is a practical implementation of the infeasible-start primal-dual algorithm designed and analyzed in [19]. This manuscript contains the users' guide, as well as theoretical results needed for the implementation of the algorithms. To help the users, we included many examples. We also discussed some implementation details and techniques we used to improve the efficiency and further expansion of the software to cover the emerging classes of convex optimization problems.
IET Signal Processing, 2018
An optimal placement for planar antenna array elements is proposed to achieve a precise direction... more An optimal placement for planar antenna array elements is proposed to achieve a precise direction of arrival (DOA) estimation on a limited bound. For this purpose, the Cramer-Rao lower bound is chosen as a suitable and useful criterion. In order to have an accurate isotropic DOA estimation, the placement of the antenna elements in the array configuration is achieved by using the invasive weed optimisation algorithm. Moreover, the optimisation results show higher efficiency compared with the similar uniform circular array. It is proved that the optimised arrays are ambiguity free, also.
International Journal of Aviation, Aeronautics, and Aerospace, 2019
Domain-Driven Solver (DDS) is a MATLAB-based software package for convex optimization problems in... more Domain-Driven Solver (DDS) is a MATLAB-based software package for convex optimization problems in Domain-Driven form [19]. The current version of DDS accepts every combination of the following function/set constraints: (1) symmetric cones (LP, SOCP, and SDP); (2) quadratic constraints that are SOCP representable; (3) direct sums of an arbitrary collection of 2-dimensional convex sets defined as the epigraphs of univariate convex functions (including as special cases geometric programming and entropy programming); (4) generalized power cone; (5) epigraphs of matrix norms (including as a special case minimization of nuclear norm over a linear subspace); (6) vector relative entropy; (7) epigraphs of quantum entropy and quantum relative entropy; and (8) constraints involving hyperbolic polynomials. DDS is a practical implementation of the infeasible-start primal-dual algorithm designed and analyzed in [19]. This manuscript contains the users' guide, as well as theoretical results needed for the implementation of the algorithms. To help the users, we included many examples. We also discussed some implementation details and techniques we used to improve the efficiency and further expansion of the software to cover the emerging classes of convex optimization problems.