Precision pointing compensation for DSN antennas with optical distance measuring sensors

The pointing control loops of Deep Space Network (DSN) antennas do not account for unmodeled deflections of the primary and secondary reflectors. As a result, structural distortions due to unpredictable environmental loads can result in uncompensated boresight shifts which degrade pointing accuracy. The design proposed here can provide real-time bias commands to the pointing control system to compensate for environmental effects on pointing performance. The bias commands can be computed in real time from optically measured deflections at a number of points on the primary and secondary reflectors. Computer simulations with a reduced-order finite-element model of a DSN antenna validate the concept and lead to a proposed design by which a ten-to-one reduction in pointing uncertainty can be achieved under nominal uncertainty conditions

Dynamical nucleus-nucleus potential at short distances

The dynamical nucleus-nucleus potentials for fusion reactions 40Ca+40Ca, 48Ca+208Pb and 126Sn+130Te are studied with the improved quantum molecular dynamics (ImQMD) model together with the extended Thomas-Fermi approximation for the kinetic energies of nuclei. The obtained fusion barrier for 40Ca+40Ca is in good agreement with the extracted fusion barrier from the measured fusion excitation function, and the depth of the fusion pockets are close to the results of time-dependent Hartree-Fock calculations. The energy dependence of fusion barrier is also investigated. For heavy fusion system, the fusion pocket becomes shallow and almost disappears for symmetric systems and the obtained potential at short distances is higher than the adiabatic potential.Comment: 6 figures, accepted for publication in Phys. Rev.

Pesquisas em reprodução fomentam mudanças tecnológicas na suinocultura.

Projeto: 11.11.11.111

Cyclic mutually unbiased bases, Fibonacci polynomials and Wiedemann's conjecture

We relate the construction of a complete set of cyclic mutually unbiased bases, i. e., mutually unbiased bases generated by a single unitary operator, in power-of-two dimensions to the problem of finding a symmetric matrix over F_2 with an irreducible characteristic polynomial that has a given Fibonacci index. For dimensions of the form 2^(2^k) we present a solution that shows an analogy to an open conjecture of Wiedemann in finite field theory. Finally, we discuss the equivalence of mutually unbiased bases.Comment: 11 pages, added chapter on equivalenc

Aumento do peso e redução da idade a puberdade de leitoas através dos cruzamentos.

bitstream/item/58795/1/CUsersPiazzonDocuments151.pd

Fêmeas cruzadas ou F1 recomendadas para a produção de suínos para o abate.

bitstream/item/58663/1/CUsersPiazzonDocuments184.pd

Autonomous frequency domain identification: Theory and experiment

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability