We have designed and fabricated an anatomically accurate human head phantom that is capable of generating realistic electric scalp potential patterns. This phantom was developed for performance evaluation of new electroencephalography (EEG) caps, hardware, and measurement techniques that are designed for environments high in electromagnetic and mechanical noise. The phantom was fabricated using conductive composite materials that mimic the electrical and mechanical properties of scalp, skull, and brain. The phantom prototype was calibrated and testing was conducted using a 32-electrode EEG cap. Test results show that the phantom is able to generate diverse scalp potential patterns using a finite number of dipole antennas internal to the phantom. This phantom design could provide a valuable test platform for wearable EEG technology.

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Hydrological terrain analysis is important for applications such as environmental resource, agriculture, and flood risk management. It is based on processing of high-resolution, tiled digital elevation model (DEM) data for geographic regions of interest. A major challenge in global hydrological terrain analysis is addressing cross-tile dependencies that arise from the tiled nature of the underlying DEM data, which is too large to hold in memory as a single array. We are not aware of existing tools that can accurately and efficiently perform global terrain analysis within current memory and computational constraints. We solved this problem by implementing a new algorithm in Python, which uses a simple but robust file-based locking mechanism to coordinate the work flow between an arbitrary number of independent processes operating on separate DEM tiles.We used this system to analyze the conterminous US's terrain at 1 arcsecond resolution in under 3 days on a single compute node, and global terrain at 3 arc-second resolution in under 4 days. Our solution is implemented and made available as pyDEM, an open source Python/Cython library that enables global geospatial terrain analysis. We will describe our algorithm for calculating various terrain analysis parameters of interest, our file-based locking mechanism to coordinate the work between processors, and optimization using Cython. We will demonstrate pyDEM on a few example test cases, as well as real DEM data.

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This paper describes the development of a laparoscopic tissue tracking system for use during minimally-invasive, image-guided abdominal surgery. The system is designed to measure organ position and shape to permit coregistration of preoperative, volumetric image data with the actual anatomy encountered during surgery.The laparoscopic tissue tracking system relies on projection of a scanned laser beam through a conventional laparoscope. The projected laser is then imaged using a second laparoscope oriented obliquely to the projecting laparoscope. Knowledge of the optical characteristics of the laparoscopes, along with their relative positions in space, allows determination of the three-dimensional coordinates of the illuminated point. Rapid localization permits tracking of tissue motion due to respiration or surgical manipulation. This paper provides a brief overview of the system, discusses system accuracy measured during laboratory testing, and shows data obtained from use of the system during surgery on an experimental animal.