Wallis, Allen Bruce. The Design and Implementation of a Distributed Data Capture and Processing Framework for Ground Penetrating Radar. MSc Dissertation. Department of Electrical Engineering, University of Cape Town, 2001.
This dissertation describes the development of a distributed data-capture and data-processing framework for use with a network-aware ground penetrating radar. The software that was developed addresses weaknesses in existing data processing software, with the main focus being on the distributed capabilities of the framework. The framework was designed from an object oriented perspective, using the Unified Modeling Language to describe the architecture. The Java programming language was used to implement the design. The Common Object Request Booker Architecture was used as the messaging protocol, however the framework was designed such that an alternative messaging protocol such as Java Remote Method Invocation could also be added at a later stage. The Extensible Markup Language was used for storing data as well as configuration information.
The framework was designed to be modular such that additonal functionality could be added later in the form of modules. Three modules were developed, a data-viewer module, a data-persister module and a radar controller module.
Performance tests were completed to measure the maximum number of profiles that could be transmitted per second for two scenarios, one involving a stand-alone machine, and another involving two networked machines using 10 Mbit ethernet. The highest data rate of 346 was achieved when there were no viewer modules active in the system and no processing being applied. For a more useful scenario involving the inverse Fourier transform and a data viewer, it was found that the highest data rate was measured when the radar server was located on a separate machine to the processing framework. The maximum data rate measured under these circumstances was 177 profiles per second. Since the maximum data rate that the hardware can currently support is ten profiles per second, the data rate for the framework is more than sufficient.