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Abstract-This paper examines several promising throughput enhancements to the Lempel-Ziv-Oberhumer (LZO) 1x-1-15 data compression algorithm. Of many algorithm variants present in the current library version, 2.06, LZO 1x-1-15 is considered to be the fastest, geared toward speed rather than compression ratio. We present several algorithm modifications tailored to modern multi-core architectures in this paper that are intended to increase compression speed while minimizing any loss in compression ratio. On average, the experimental results show that on a modern quad core system, a 3.9x speedup in compression time is achieved over the baseline algorithm with no loss to compression ratio.Allowing for a 25% loss in compression ratio, up to a 5.4x speedup in compression time was observed.

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This paper presents a rapid prototype approach for the development of a real-time capable neural-machine-interface (NMI) for control of artificial legs based on mobile processor technology (Intel Atom TM Z530 Processor.) By effectively exploiting the architectural features of a mobile embedded CPU, we implemented a decision-making algorithm, based on neuromuscular-mechanical fusion and gait phase-dependent support vector machines (SVM) classification to meet the demanding performance constraints. To demonstrate the feasibility of a real-time mobile computing based NMI, real-time experiments were performed on an able bodied subject with window increments of 50ms. The experiments showed that the mobile computing based NMI provided fast and accurate classifications of four major human locomotion tasks (levelground walking, stair ascent, stair descent, and standing) and a 46X speedup over an equivalent MATLAB implementation. The testing yielded accuracies of 96.31% with low power consumption. An offline analysis showed the accuracy could be increased to 98.87% with minor modifications to the application.