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Since its introduction in 2006, Hadoop technology has evolved dramatically and the Hadoop ecosystem has flourished. The Hadoop ecosystem is now composed of more than 60 components, ranging from HDFS and MapReduce. Hadoop’s architecture makes it far superior to other products in large-scale data processing and analysis, making it the best choice for all industries. This makes Hadoop the preferred framework for data analysis and processing in a variety of industries. With the widespread application of Hadoop, people are increasingly concerned about the data trustworthiness of HDFS such as the explicit storage of data and over-reliance on authentication mechanisms. In order to ensure the trustworthiness of Hadoop data and to take into account the performance factors of the big data framework, this study focuses on the HDFS-based data trustworthiness problem and the use of node classification, data encryption, trustworthiness measures and other measures to comprehensively enhance the trustworthiness of HDFS file system, improve the existing HDFS system, and ensure the data storage communication in a trustworthy environment.

The goal of the optic nerve stimulation is to place multielectrode electrodes around the optic nerve and evoke multiplephosphenes covering the entire visual space by using complex patterns of electrical stimulation. This paper mainly studies the best image processing strategies applying to the optic nerve stimulation. Four image processing strategies that could be used in the optic nerve stimulation such as histogram equalization, edge detection, edge following, binary morphological operations. As we learn more about the neural coding of visual system, the better image processing strategies will be applied to the artificial vision.