An algorithm is presented to automatically detect near surface ice layers in images from the Shallow Subsurface Radar (SHARAD) on NASA's Mars Reconnaissance Orbiter. Mars' ice-rich Northern Polar Layered Deposits (NPLD) represents an extensive geologic record of climate history. Identifying ice layers in cross-sectional images leads to understanding the three-dimensional structure of ice layers. Scientists have manually identified layers in large data volumes, but the automated algorithm will allow studying more images from over a thousand orbital crossings. A unique coordinate transformation, based upon the surface reflection, makes subsequent filtering and detection more effective on near surface layers. Results show promising capabilities for automatically detecting ice layers on Mars.

Model driven engineering (MDE) of software product lines (SPLs) merges two increasing important paradigms that synthesize programs by transformation. MDE creates programs by transforming models, and SPLs elaborate programs by applying transformations called features. In this paper, we present the design and implementation of a transformational model of a product line of scalar vector graphics and JavaScript applications. We explain how we simplified our implementation by lifting selected features and their compositions from our original product line (whose implementations were complex) to features and their compositions of another product line (whose specifications were simple). We used operators to map higher-level features and their compositions to their lower-level counterparts. Doing so exposed commuting relationships among feature compositions in both product lines that helped validate our model and implementation.

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