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A quadtree-like representation for storing gridded elevation data is described. In its simplest form, the data structure is a pyramid with each node containing a two bit code. The root of the pyramid has associated with it the minimum elevation for the grid and the range (the greatest power of 2 less than or equal to the difference between the minimum and maximum elevation values). Any specified elevation value is determined by traversing a path from the root to a leaf node. As the traversal proceeds, the minimum and range values are refined by interpreting the codes stored at each node along the path. At the leaf level, the final minimum value equals the associated elevation value. Since the total number of nodes in the pyramid is 4/3 the number of elevation grid cells, the amortized storage cost is less than 3 bits per grid cell. When the difference between elevation values is not "too great", this basic representation is quite effective. For data where greater elevation differences occur between neighboring cells, this basic method is modified to improve the representation, but at a cost in storage. Our method is most appropriate for efficient secondary storage archival, such as on CD-ROM. It also allows efficient retrieval of complete elevation data from any subregion, at multiple scales, within the entire elevation database. / Master of Science

This thesis explores one possible method of preventing helicopter crashes caused by brownout using the noise generated by the helicopter rotor as an altimeter. The hypothesis under consideration is that the helicopter's height, velocity, and obstacle locations with respect to the helicopter, can be determined by comparing incident and reflected rotor noise signals, provided adequate bandwidth and signal to noise ratio. Heights can be determined by measuring the cepstrum of the reflected helicopter noise. The velocity can be determined by measuring small amounts of Doppler distortion using the Mellin-Scale Transform. Height and velocity detection algorithms are developed, optimized for this application, and tested using a microphone array. The algorithms and array are tested using a hemianechoic chamber and outside in Georgia Tech's Burger Bowl. Height and obstacle detection are determined to be feasible with the existing array. Velocity detection and surface mapping are not successfully accomplished.