Complex-valued Time Series Modeling for Improved Activation Detection in fMRI Studies

A complex-valued data-based model with th order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model. Such improved accuracy in mapping the left functional central sulcus has important implications in neurosurgical planning for tumor and epilepsy patients. Additionally, we develop magnitude and phase detrending procedures for complex-valued time series and examine the effect of spatial smoothing. These methods improve the power of complex-valued data-based activation statistics. Our results advocate for the use of the complex-valued data and the modeling of its dependence structures as a more efficient and reliable tool in fMRI experiments over the current practice of using only magnitude-valued datasets

A spatial disorientation predictor device to enhance pilot situational awareness regarding aircraft attitude

An effort was initiated at the Armstrong Aerospace Medical Research Laboratory (AAMRL) to investigate the improvement of the situational awareness of a pilot with respect to his aircraft's spatial orientation. The end product of this study is a device to alert a pilot to potentially disorienting situations. Much like a ground collision avoidance system (GCAS) is used in fighter aircraft to alert the pilot to 'pull up' when dangerous flight paths are predicted, this device warns the pilot to put a higher priority on attention to the orientation instrument. A Kalman filter was developed which estimates the pilot's perceived position and orientation. The input to the Kalman filter consists of two classes of data. The first class of data consists of noise parameters (indicating parameter uncertainty), conflict signals (e.g. vestibular and kinesthetic signal disagreement), and some nonlinear effects. The Kalman filter's perceived estimates are now the sum of both Class 1 data (good information) and Class 2 data (distorted information). When the estimated perceived position or orientation is significantly different from the actual position or orientation, the pilot is alerted

Toughened uni-piece fibrous insulation

A porous body of fibrous, low density silica-based insulation material is at least in part impregnated with a reactive boron oxide containing borosilicate glass frit, a silicon tetraboride fluxing agent and a molybdenum silicide emittance agent. The glass frit, fluxing agent and emittance agent are separately milled to reduce their particle size, then mixed together to produce a slurry in ethanol. The slurry is then applied to the insulation material and sintered to produce the porous body

The Impact of Insurance on the Law of Torts

An experimental study of "Model-on-Demand" (MoD) identification is made on a pilot-scale brine-water mixing tank. MoD estimation is compared against semi-physical modeling techniques using identification data generated from a systematically designed m-level Pseudo Random Sequence (PRS) input. The estimated models are the basis for evaluating the usefulness of MoD-based Model Predictive Control (MPC). For this application, MoD-MPC is shown to provide better performance at high bandwidths compared to a linear MPC controller