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We designed a graphics processing unit (GPU)-based acceleration to reconstruct the optical coherence tomography (OCT) images as sub-micrometer resolution with the spectral domain OCT (SD-OCT) system. GPU-based acceleration is the use of general purpose GPU (GP-GPU) together with a CPU to accelerate the specific operation. As a result, by applying GPU acceleration to the tomographic inspection of biological samples, SD-OCT imaging can be obtained in excess of 40 frames per second (FPS) for the K6000 GPU-accelerated SD-OCT with frame size 4096 (axial) × 512 (lateral), and more than 512x512x500 volumes can be reconstructed with a speed increase of 7x or more (compared to a non-GPU).

Pattern matching and pattern searching in time series data have been active issues in a number of disciplines. This paper suggests a novel pattern matching technology which can be used in the field of stock market analysis as well as in forecasting stock market trend. First, we define conceptual patterns, and extract data forming each pattern from given time series, and then generate learning model using Hidden Markov Model. The results show that the context-based pattern matching makes the matching more accountable and the method would be effectively used in real world applications. This is because the pattern for new data sequence carries not only the matching itself but also a given context in which the data implies.