The wireless market of the future will demand inexpensive hardware, expandability, interoperability, and the implementation of advanced signal processing functions--i.e. a software radio. Configurable computing machines are often ideal software radio platforms. In particular, the Stallion reconfigurable processor's efficient hardware reuse and scalability fulfill these radios' demands. The advantages of Stallion-based design inspired an FPGA-based software radio - the proto-Stallion receiver. This thesis introduces the proto-Stallion architecture and details its implementation on the SLAAC-1V FPGA platform. Although this thesis presents a specific radio implementation, this architecture is flexible; it can support a variety of applications within its fixed framework. This implemented single-user DS-CDMA receiver utilizes an LMS adaptive filter that can combat MAI and constructively combine multipath; most notably, this receiver employs an adaptive tracking algorithm that harnesses the LMS algorithm to maintain symbol synchronization. The proto-Stallion receiver demonstrates the dependence of adaptive tracking on channel noise; the algorithm requires significant noise levels to maintain synchronization. / Master of Science

Service robots need to be aware of persons in their vicinity in order to interact with them. People tracking enables the robot to perceive persons by fusing the information of several sensors. Most robots rely on laser range scanners and RGB cameras for this task. The thesis focuses on the detection and tracking of heads. This allows the robot to establish eye contact, which makes interactions feel more natural. Developing a fast and reliable pose-invariant head detector is challenging. The head detector that is proposed in this thesis works well on frontal heads, but is not fully pose-invariant. This thesis further explores adaptive tracking to keep track of heads that do not face the robot. Finally, head detector and adaptive tracker are combined within a new people tracking framework and experiments show its effectiveness compared to a state-of-the-art system.

Service robots need to be aware of persons in their vicinity in order to interact with them. People tracking enables the robot to perceive persons by fusing the information of several sensors. Most robots rely on laser range scanners and RGB cameras for this task. The thesis focuses on the detection and tracking of heads. This allows the robot to establish eye contact, which makes interactions feel more natural. Developing a fast and reliable pose-invariant head detector is challenging. The head detector that is proposed in this thesis works well on frontal heads, but is not fully pose-invariant. This thesis further explores adaptive tracking to keep track of heads that do not face the robot. Finally, head detector and adaptive tracker are combined within a new people tracking framework and experiments show its effectiveness compared to a state-of-the-art system.

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