Browsing by Subject "Data Acquisition"
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Item Acquisition and Mining of the Whole Mouse Brain Microstructure(2010-10-12) Kwon, Jae-RockCharting out the complete brain microstructure of a mammalian species is a grand challenge. Recent advances in serial sectioning microscopy such as the Knife- Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical sectioning technique, have the potential to finally address this challenge. Nevertheless, there still are several obstacles remaining to be overcome. First, many of these serial sectioning microscopy methods are still experimental and are not fully automated. Second, even when the full raw data have been obtained, morphological reconstruction, visualization/editing, statistics gathering, connectivity inference, and network analysis remain tough problems due to the unprecedented amounts of data. I designed a general data acquisition and analysis framework to overcome these challenges with a focus on data from the C57BL/6 mouse brain. Since there has been no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. To address the problems, I constructed a general software framework for automated data acquisition and computational analysis of the KESM data, and conducted two scientific case studies to discuss how the mouse brain microstructure from the KESM can be utilized. I expect the data, tools, and studies resulting from this dissertation research to greatly contribute to computational neuroanatomy and computational neuroscience.Item Wireless data acquisition system for multi-phase electric power equipment(2009-05-15) Goodsell, Douglas AndreasIndustrial facilities that plan the shutdown of equipment for service have large financial savings compared to those managing unplanned shutdowns. To this end, a variety of algorithms have been developed and published in the literature that can monitor a machine's health and indicate when the machine starts to develop a fault. In order for such algorithms to be effective, they require raw data collected from machines. Often this involves the placement of accelerometers and other sensory devices for measurements of mechanical behavior. It is possible to extract much of the required information from the electrical signals of the equipment. This is normally a less expensive installation since one only needs access to the lines supplying electric power to the equipment. If these data acqusistion modules are accessible wirelessly, then one can monitor all the interfaced equipment from a central location. To successfully monitor such electrical equipment, a data acquisition unit is required that can sample on five or six channels simultaneously, depending on the switch gear configuration. This thesis details the development of an "endpoint" device that samples the required number of channels to monitor the electrical signals of industrial equipment, and interfaces to a wireless network. The hardware and software design of the "endpoint" is discussed in detail. Also, the software design of the server that receives the data from the "endpoint" is presented. The designed "endpoint" samples up to six channels simultaneously, at a rate of at least 8 kHz per channel, and a data resolution of 16 bits. The data are then transmitted wirelessly to a central server for processing. The system has been tested both in a laboratory environment and at an industrial environment. The desired specifications of the "endpoint" have been verified in both environments. Several "endpoints" have been assembled to form a network and have been tested in a laboratory setting. This work has resulted in the demonstration that an "endpoint" can be constructed using of the shelf components that is suitable for the continuous health monitoring of multi-phase electric machines through a wireless network.