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Many modern programming languages rely on memory management environments that are responsible for allocation and deallocation of objects. Garbage collection phases are used in order to detect inaccessible objects on the heap so they can be deallocated. The performance of garbage collection techniques depends heavily on the environment, implementation specific parameters and the benchmark used. The contribution of this publication is an extendable memory management simulator, which aims to assist developers in memory management evaluation and research. The simulator is capable of reading operations from a trace file extracted from a virtual machine and simulating the memory management needed by the simulated mutator. The framework aims to provide an isolated experimentation and comparison platform in the field of automatic memory management. New algorithms can be added to the framework in order to compare them to established algorithms.

Flood forecasting is a process that relies on hydrologic models to predict water levels and flow rates in different basins. These hydrologic models depend on the predicted amount of water in rain clouds. A common form of this data for these models comes from color-configured forecast map images. These images are manually interpreted. However, manual interpretation is slow, tedious, and prone to error especially if there are numerous images. We propose a method to automate the interpretation of these images for a faster and more efficient means to predict the amount of water in the clouds. We identify two computational sub-problems: (1) localization and recognition of the region of interest (ROI), and (2) interpretation of the values in the ROI. We use the Speed-up Robust Features (SURF) technique to localize the ROI‟s, and a look-up table which makes use of Hue Saturation Value (HSV) color space. Experimental results show higher accuracy compared to the manual interpretation, and a significantly faster processing time.