Selection of Psychophysiological Features across Subjects for Classifying Workload Using Artificial Neural Networks

The issue of pilot workload is important to the United States Air Force because pilot overload or task saturation leads to decreases in mission effectiveness. Additionally, in the most extreme cases, pilot overload may lead to the loss of aircraft and crewmember lives. Current research efforts are utilizing psychophysiological data including electroencephalography (EEG), cardiac, eye-blink, and respiration measures in an attempt to identify workload levels. The primary focus of this effort is to determine if a single parsimonious set of psychophysiological features exists for accurately classifying workload levels between multiple test subjects. To accomplish this objective, the signal-to-noise (SNR) saliency measure is used to determine the usefulness of psychophysiological features in feedforward artificial neural networks (ANN). The SNR saliency measure determines the saliency, or relative value, of a feature by comparing it to a feature of injected noise. For this effort, 36 psychophysiological features were derived from the data collected as each subject completed simulated crewmember tasks using the Multi-Attribute Task Battery developed by NASA. These tasks were randomly presented to the subjects in blocks with three distinct levels: low, medium, and an overload level in which subjects could not complete all tasks

Optimization of Automatic Target Recognition with a Reject Option Using Fusion and Correlated Sensor Data

This dissertation examines the optimization of automatic target recognition (ATR) systems when a rejection option is included. First, a comprehensive review of the literature inclusive of ATR assessment, fusion, correlated sensor data, and classifier rejection is presented. An optimization framework for the fusion of multiple sensors is then developed. This framework identifies preferred fusion rules and sensors along with rejection and receiver operating characteristic (ROC) curve thresholds without the use of explicit misclassification costs as required by a Bayes\u27 loss function. This optimization framework is the first to integrate both vertical warfighter output label analysis and horizontal engineering confusion matrix analysis. In addition, optimization is performed for the true positive rate, which incorporates the time required by classification systems. The mathematical programming framework is used to assess different fusion methods and to characterize correlation effects both within and across sensors. A synthetic classifier fusion-testing environment is developed by controlling the correlation levels of generated multivariate Gaussian data. This synthetic environment is used to demonstrate the utility of the optimization framework and to assess the performance of fusion algorithms as correlation varies. The mathematical programming framework is then applied to collected radar data. This radar fusion experiment optimizes Boolean and neural network fusion rules across four levels of sensor correlation. Comparisons are presented for the maximum true positive rate and the percentage of feasible thresholds to assess system robustness. Empirical evidence suggests ATR performance may improve by reducing the correlation within and across polarimetric radar sensors. Sensitivity analysis shows ATR performance is affected by the number of forced looks, prior probabilities, the maximum allowable rejection level, and the acceptable error rates

An evolutionary perspective on field cancerization

The authors acknowledge funding from Cancer Research UK (grants A19771 to T.A.G. and A21870 to N.A.W.), the Wellcome Trust (202778/Z/16/Z t