Benefitting from the high measurement efficiency, off-axis digital holography (DH) has become a most powerful DH technique for fast and high-accuracy measurement. Owing to carrier frequency, real image can be isolated easily in Fourier spectrum of one hologram, so that transform algorithm (FTA) is widely used realize phase retrieval. In FTA, important tasks figure out accurate peak position the...

Blockchain technology is one among the latest innovations in computing industry. Blockchains have gathered widespread interest industry due to their potential as secure data storage. Despite benefits of blockchains, there are several limitations which hinders mass deployment, these being low throughput. Low throughput has limited blockchain adoption large scale applications such banking or mobi...

Nowadays, bulk of the designers prefer to outsource some parts of their design and fabrication process to the third-part companies due to the reliability problems, manufacturing cost and time-to-market limitations. In this situation, there are a lot of opportunities for malicious alterations by the off-shore companies. In this paper, we proposed a new placement algorithm that hinders the hardwa...

This paper aims to research the position control of the hydraulic cylinder controlled by the high-speed on-off valve to realize the precise position control. The strategy combining the pulse width modulation with pulse frequency modulation is proposed to compensate the dead and saturated zones of the high-speed on-off valve, which is on the groundwork of the analysis of the flow characteristic ...

Off-policy learning refers to the problem of learning the value function of a behaviour, or policy, while selecting actions with a different policy. Gradient-based off-policy learning algorithms, such as GTD (Sutton et al., 2009b) and TDC/GQ (Sutton et al., 2009a), converge when selecting actions with a fixed policy even when using function approximation and incremental updates. In control prob...

In this paper a reinforcement learning methodology for automatic online algorithm selection is introduced and empirically tested. It is applicable to automatic algorithm selection methods that predict the performance of each available algorithm and then pick the best one. The experiments confirm the usefulness of the methodology: using online data results in better performance. As in many onlin...