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Minimum entropy techniques for determining the period of W UMA starsMcArthur, Ian Albert 08 1900 (has links)
This MSc report discusses the attributes of W Ursae Majoris (W UMa) stars and an investigation into the Minimum Entropy (ME) method, a digital technique applied to the determination of their periods of variability. A Python code programme was written to apply the ME method to photometric data collected on W UMa stars by the All Sky
Automated Survey (ASAS). Starting with the orbital period of the binaries estimated by ASAS, this programme systematically searches around this period for the period which corresponds to the lowest value of entropy. Low entropy here means low scatter (or spread) of data across the phase-magnitude plane. The ME method divides the light curve plot area into a number of elements of the investigators choosing. When a particular orbital period is applied to this photometric data, the resulting distribution of this data in the light curve plane corresponds to a speci c number of data points in each element into which this plane has been divided. This data spread is measured and calculated in terms of entropy and the lowest value of entropy corresponds to the lowest spread of data across the light curve plane. This should correspond to the best light curve shape available from the data and therefore the most accurate orbital period available. Subsequent to the testing of this Python code on perfect sine waves, it was applied, and its results compared, to the 62 ASAS eclipsing binary stars which were investigated by Deb and Singh (2011). The method was then applied to selected stars from the ASAS data base. / School of Environmental Sciences / M. Sc. (Astronomy)
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Minimum entropy techniques for determining the period of W UMA starsMcArthur, Ian Albert 08 1900 (has links)
This MSc report discusses the attributes of W Ursae Majoris (W UMa) stars and an investigation into the Minimum Entropy (ME) method, a digital technique applied to the determination of their periods of variability. A Python code programme was written to apply the ME method to photometric data collected on W UMa stars by the All Sky
Automated Survey (ASAS). Starting with the orbital period of the binaries estimated by ASAS, this programme systematically searches around this period for the period which corresponds to the lowest value of entropy. Low entropy here means low scatter (or spread) of data across the phase-magnitude plane. The ME method divides the light curve plot area into a number of elements of the investigators choosing. When a particular orbital period is applied to this photometric data, the resulting distribution of this data in the light curve plane corresponds to a speci c number of data points in each element into which this plane has been divided. This data spread is measured and calculated in terms of entropy and the lowest value of entropy corresponds to the lowest spread of data across the light curve plane. This should correspond to the best light curve shape available from the data and therefore the most accurate orbital period available. Subsequent to the testing of this Python code on perfect sine waves, it was applied, and its results compared, to the 62 ASAS eclipsing binary stars which were investigated by Deb and Singh (2011). The method was then applied to selected stars from the ASAS data base. / Environmental Sciences / M. Sc. (Astronomy)
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An intuitive motion-based input model for mobile devicesRichards, Mark Andrew January 2006 (has links)
Traditional methods of input on mobile devices are cumbersome and difficult to use. Devices have become smaller, while their operating systems have become more complex, to the extent that they are approaching the level of functionality found on desktop computer operating systems. The buttons and toggle-sticks currently employed by mobile devices are a relatively poor replacement for the keyboard and mouse style user interfaces used on their desktop computer counterparts. For example, when looking at a screen image on a device, we should be able to move the device to the left to indicate we wish the image to be panned in the same direction.
This research investigates a new input model based on the natural hand motions and reactions of users. The model developed by this work uses the generic embedded video cameras available on almost all current-generation mobile devices to determine how the device is being moved and maps this movement to an appropriate action.
Surveys using mobile devices were undertaken to determine both the appropriateness and efficacy of such a model as well as to collect the foundational data with which to build the model. Direct mappings between motions and inputs were achieved by analysing users' motions and reactions in response to different tasks.
Upon the framework being completed, a proof of concept was created upon the Windows Mobile Platform. This proof of concept leverages both DirectShow and Direct3D to track objects in the video stream, maps these objects to a three-dimensional plane, and determines device movements from this data.
This input model holds the promise of being a simpler and more intuitive method for users to interact with their mobile devices, and has the added advantage that no hardware additions or modifications are required the existing mobile devices.
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