Investigations into an optimal transmission scheme for a MIMO system operating in a ricain fading environment

This paper describes investigations into an optimal transmission scheme for a multiple input multiple output (MIMO) system operating in a Rician fading environment. The considerations are reduced to determining a covariance matrix of transmitted signals which maximizes the MIMO capacity under the condition that the receiver has perfect knowledge of the channel while the transmitter has the information about selected statistical quantities which are measured at the receiver. An optimal covariance matrix, which requires information of the Rice factor and the signal to noise ratio, is determined. The transmission scheme relying on the choice of the proposed covariance matrix outperforms the other transmission schemes which were reported earlier in the literature. The proposed scheme realizes an upper bound limit for the MIMO capacity under arbitrary Rician fading conditions. ©2005 IEEE

Expansion tube operating conditions for studying nonequilibrium radiation relevant to Titan Aerocapture

The predictions of nonequilibrium radiation in the shock layer for a Titan aerocapture aeroshell vary significantly amongst Computational Fluid Dynamics (CFD) analyses and are limited by the physical models of the nonequilibrium flow processes. Of particular interest are nonequilibrium processes associated with the CN molecule which is a strong radiator. It is necessary to have experimental data for these radiating shock layers which will allow for validation of the CFD models. This paper describes the development of a test flow condition for subscale aeroshell models in a superorbital expansion tunnel. We discuss the need for a Titan gas condition that closely simulates the atmospheric composition and present experimental data of the free stream test flow conditions. Furthermore, we present finite-rate CFD calculations of the facility to estimate the remaining free stream conditions, which cannot be directly measured during experiments.

Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application.