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Uncertain databases have been widely developed to deal with the vast amount of data that contain uncertainty. To extract valuable information from the uncertain databases, several methods of frequent itemset mining, one of the major data mining techniques, have been proposed. However, their performance is not satisfactory because handling uncertainty incurs high processing costs. In order to address this problem, we utilize GPGPU (General-Purpose computation on GPU). GPGPU implies using a GPU (Graphics Processing Unit), which is originally designed for processing graphics, to accelerate general purpose computation. In this paper, we propose a method of frequent itemset mining from uncertain databases using GPGPU. The main idea is to speed up probability computations by making the best use of GPU's high parallelism and low-latency memory. We also employ an algorithm to manipulate a bitstring and dataparallel primitives to improve performance in the other parts of the method. Extensive experiments show that our proposed method is up to two orders of magnitude faster than existing methods.

No abstract

This paper presents a bi-level quadraturemodulation (QM) low-pass (LP) envelope-pulse-widthmodulation (EPWM) transmitter using a half side of tri-level EPWM. Simulation including digital signal processing with the QM LP-EPWM and microwave circuit simulation with a class-D power amplifier (PA) using n-MOSFETs shows an excellent error vector magnitude (EVM) of 36 dB at 1 GHz. The transmitter with a class-D PA for bi-level EPWM signal shows much better EVM performance than the one for trilevel signal.Index Terms -transmitterpower amplifierclass-D envelope pulse-width modulationquadrature modulation, error vector magnitudepower-added efficiency