Where Has All the Commitment Gone

Two contemporary sociologists, Allan Bloom (The Closing of the American Mind) and Robert Bellah (Habits of the Heart: Individualism and Commitment in American Life), focus our attention to the growing problems in America brought about by our often individualistic and uncommitted values. Christian leadership struggles to find ways to bring Christians to a serious commitment to Christ

Physics-constrained Hyperspectral Data Exploitation Across Diverse Atmospheric Scenarios

Hyperspectral target detection promises new operational advantages, with increasing instrument spectral resolution and robust material discrimination. Resolving surface materials requires a fast and accurate accounting of atmospheric effects to increase detection accuracy while minimizing false alarms. This dissertation investigates deep learning methods constrained by the processes governing radiative transfer to efficiently perform atmospheric compensation on data collected by long-wave infrared (LWIR) hyperspectral sensors. These compensation methods depend on generative modeling techniques and permutation invariant neural network architectures to predict LWIR spectral radiometric quantities. The compensation algorithms developed in this work were examined from the perspective of target detection performance using collected data. These deep learning-based compensation algorithms resulted in comparable detection performance to established methods while accelerating the image processing chain by 8X

Where Has All the Commitment Gone

Two contemporary sociologists, Allan Bloom (The Closing of the American Mind) and Robert Bellah (Habits of the Heart: Individualism and Commitment in American Life), focus our attention to the growing problems in America brought about by our often individualistic and uncommitted values. Christian leadership struggles to find ways to bring Christians to a serious commitment to Christ

Cox, REAVIS. The Economics of Instalment Buying. Pp. xiii, 526. New York: The Ronald Press Company, 1948. $6.00

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67962/2/10.1177_000271624825900169.pd

Fast and Effective Techniques for LWIR Radiative Transfer Modeling: A Dimension-Reduction Approach

The increasing spatial and spectral resolution of hyperspectral imagers yields detailed spectroscopy measurements from both space-based and airborne platforms. These detailed measurements allow for material classification, with many recent advancements from the fields of machine learning and deep learning. In many scenarios, the hyperspectral image must first be corrected or compensated for atmospheric effects. Radiative Transfer (RT) computations can provide look up tables (LUTs) to support these corrections. This research investigates a dimension-reduction approach using machine learning methods to create an effective sensor-specific long-wave infrared (LWIR) RT model