Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.

No abstract

The preliminary design of a robot for future planetary space applications is presented. This hexapod robot has legs inspired by the spider, which it uses to manoeuvre across horizontal surfaces. Designed as a scientific platform for future research, mechanically, this robot is lightweight, compact and modular. A Field Programmable Gate Array (FPGA) is used as a controller, with one soft processor controlling each leg, adding additional modularity. This robot is shown to be capable of walking across horizontal surfaces, and future versions will be capable of climbing vertically, using bio-inspired dry adhesives.