The finite element analysis of shells of revolution with a branching meridian

We present an algorithm for calculating the shells of revolution with the branching meridian using the triangular finite elements, the rigidity matrices of which are formed based on the vector method of displacement interpolation [1]. The correct kinematic and static conditions of shell conjugation on the line of their coupling have been developed. The shell structure consisting of a cylinder and adjacent shells is calculated under various conditions of support.     

TerraSAR-X active radar ground calibrator system

In April 2006, the TerraSAR-X satellite was launched. This paper describes the development of a novel and highly integrated, digitally-controlled active SAR system calibrator (DARC). It consists of both an active transponder path for absolute radiometric calibration and a calibrated receiver chain for antenna pattern evaluation of the satellite antenna. A total of 16 active transponder and receiver systems and 17 receiver-only systems will be fabricated for a calibration campaign in 2006.     

Analysis of shells of revolution under symmetrical loading using the quadrature method

The application of the quadrature method [1] to analyzing the shells of revolution under symmetrical loading relative to the zero meridian is considered. The method can be treated as a generalization of the method presented in [2] to solve the Volterra integral equation of the second kind.     

Almost instant time inference for hybrid partially dynamic Bayesian networks

A Bayesian network (BN) is a compact representation for probabilistic models and inference. They have been used successfully for many military and civilian applications. It is well known that, in general, the inference algorithms to compute the exact a posterior probability of a target node given observed evidence are either computationally infeasible for dense networks or impossible for general hybrid networks. In those cases, one either computes the approximate results using stochastic simulation methods or approximates the model using discretization or a Gaussian mixture model before applying an exact inference algorithm. This paper combines the concept of simulation and model approximation to propose an efficient algorithm for those cases. The main contribution here is a unified treatment of arbitrary (nonlinear non-Gaussian) hybrid (discrete and continuous) BN inference having both computation and accuracy scalability. The key idea is to precompile the high-dimensional hybrid distribution using a hypercube representation and apply it for both static and dynamic BN inference. Since the inference process essentially becomes a combination of table look-up and some simple operations, the method is shown to be extremely efficient. It can also he scaled to achieve any desirable accuracy given sufficient preprocessing time and memory for the cases where exact inference is not possible     

A method for constructing an airfoil using a specified chord velocity diagram

A problem of determining a shape of the airfoil being streamlined by a potential incompressible inviscid flow is solved by the successive approximation method using a specified chord velocity diagram. It is shown that a closed airfoil that possesses a specified chord velocity diagram can be constructed with a sufficient accuracy; if the chord diagram is unsuccessfully specified, the closed airfoil may prove to be not univalent, that is, physically unrealizable.     

Recent Breakthroughs in RF Photonics for Radar Systems

Radar systems require transmission of very high purity signals. Photonics is now mature enough to achieve analog transmission with very low noise, strong immunity, and wide-bandwidth even in harsh environments. We present our recent developments of optimized optical links dedicated to radar and multifunction systems     

Performance evaluation for MAP state estimate fusion

This paper presents a quantitative performance evaluation method for the maximum a posteriori (MAP) state estimate fusion algorithm. Under ideal conditions where data association is assumed to be perfect, it has been shown that the MAP or best linear unbiased estimate (BLUE) fusion formula provides the best linear minimum mean squared estimate (LMMSE) given local estimates under the linear Gaussian assumption for a static system. However, for a dynamic system where fusion is recursively performed by the fusion center on local estimates generated from local measurements, it is not obvious how the MAP algorithm will perform. In the past, several performance evaluation methods have been proposed for various fusion algorithms, including simple convex combination, cross-covariance combination, information matrix, and MAP fusion. However, not much has been done to quantify the steady state behavior of these fusion methods for a dynamic system. The goal of this work is to present analytical fusion performance results for MAP state estimate fusion without extensive Monte Carlo simulations, using an approach developed for steady state performance evaluation for track fusion. Two different communication strategies are considered: fusion with and without feedback to the sensors. Analytic curves for the steady state performance of the fusion algorithm for various communication patterns are presented under different operating conditions.     

Optimal number of phased array faces and signal processors for horizon surveillance

The problem of the optimal number of phased array faces for performing 360/spl deg/ horizon surveillance is considered. Assuming the detection performance is the same in all beam positions and the total number of T/R modules is constant, it is shown that the optimal number of array faces is three. This is true whether the arrays are operating simultaneously or sequentially. A parametric analysis is performed between the number of array faces operating simultaneously and the associated cost of simultaneous operation in terms of the size of the array.     

Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detection systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.     

Carbon dioxide and oxygen budgets of a plant cultural system in a CELSS--a case of cultivation of lettuce and turnips.

In order to collect basic data about CO2 and O2 budgets of a plant cultural system in a CELSS, the variation of the CO2 absorption rates of lettuce and turnips were observed during the growing period, under different conditions. The O2 release rates were deduced from the CO2 absorption rates multiplied by 32/44. As a result, when the light intensity, the photoperiod and the atmospheric CO2 concentration increased, the rates also increased. The effects on the turnips were more significant than those on the lettuce. Turnips at 310 micromoles/m2/s of PPFD, 24 hours of photoperiod and 1100 ppm of CO2 concentration grew most actively in the present experimental conditions. One turnip absorbed 32.3 g CO2 and released 23.5 g O2 for 6 days between 24 days and 30 days after sowing.