Many modern mobile applications, such as Unmanned Aerial Vehicles (UAVs), require sophisticated processing capability with low power consumption in a small form factor. UAVs, for example, may require a platform capable of controlling a camera, performing digital signal processing techniques on the pictures to detect faces or motion, and guiding the vehicle based on decisions made from the processed data. Additionally, since the vehicle is mobile and aerial, its effectiveness is heavily dependent on the size and power consumption of the platform. In this report, we explore this set of requirements and how well they are met with a Texas Instruments OMAP SoC on a BeagleBoard. Specifically, we report on the computational performance and power drawn by the OMAP General Purpose Processor (GPP) when performing a facial detection algorithm with OpenCV. We also analyze the performance enhancement possible by offloading the facial detection algorithm to the OMAP DSP coprocessor. In summary we find that the Beagleboard would be an appropriate platform for a simpler UAV capable of pre-processing still images taken every few seconds, but not for processing video data real-time. We conclude by describing other applications that are suitable for the Beagleboard. / text

Software Defined Radios (SDRs) are radio implementations in software versus the classic method of using discrete electronics. Considering the various classes of radio applications ranging from mobile-handsets to cellular base-stations, SDRs cover a wide range of power and computational needs. As a result, computing heterogeneity, in terms of Field-Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs), and General Purpose Processors (GPPs), is needed to balance the computing and power needs of such radios. Whereas SDR represents radio implementation, Cognitive Radio (CR) represents a layer of intelligence and reasoning that derives reconfiguration of an SDR to suit an application's need. Realizing CR requires a new dimension for radios, dynamically creating new radio implementations during runtime so they can respond to changing channel and/or application needs. This thesis explores the use of integrated GPP and DSP based processors for realizing SDR and CR applications. With such processors a GPP realizes the mechanism driving radio reconfiguration, and a DSP is used to implement the SDR by performing the signal processing necessary. This thesis discusses issues related to implementing radios in this computing environment and presents a sample solution for integrating both processors to create SDR-based applications. The thesis presents a sample application running on a Texas Instrument (TI) OMAP3530 processor, utilizing its GPP and DSP cores, on a platform called the Beagleboard. For the application, the Center for Wireless Telecommunications' (CWT) Public Safety Cognitive Radio (PSCR) is ported, and an Android based touch screen interface is used for user interaction. In porting the PSCR to the Beagleboard USB bandwidth and memory access latency issues were the main system bottlenecks. Latency measurements of these interfaces are presented in the thesis to highlight those bottlenecks and can be used to drive GPP/DSP based system design using the Beagleboard. / Master of Science