In an integral imaging display, the computer-generated integral imaging method has been widely used to create the elemental images from a given three-dimensional object data. Long processing time, however, has been problematic especially when the three-dimensional object data set or the number of the elemental lenses are large. In this paper, we propose an image space parallel processing method, which is implemented by using Open Computer Language (OpenCL) for rapid generation of the elemental images sets from large three-dimensional volume data. Using the proposed technique, it is possible to realize a real-time interactive integral imaging display system for 3D volume data constructed from computational tomography (CT) or magnetic resonance imaging (MRI) data.

This paper proposes an open computer language (OpenCL) parallel processing method to generate the elemental image arrays (EIAs) for hexagonal lens array from a three-dimensional (3D) object such as a volume data. Hexagonal lens array has a higher fill factor compared to the rectangular lens array case; however, each pixel of an elemental image should be determined to belong to the single hexagonal lens. Therefore, generation for the entire EIA requires very large computations. The proposed method reduces processing time for the EIAs for a given hexagonal lens array. By using the proposed image space parallel processing (ISPP) method, it can enhance the processing speed that generates the 3D display of real-time interactive integral imaging for hexagonal lens array. In our experiment, we implemented the EIAs for hexagonal lens array in real-time and obtained a good processing time for a large of volume data for multiple cases of lens arrays.

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

A real-time interactive orthographic-view image display of integral imaging (II) microscopy that includes the generation of intermediate-view elemental images (IVEIs) for resolution enhancement is proposed. Unlike the conventional II microscopes, parallel processing through a graphics processing unit is required for real-time display that generates the IVEIs and interactive orthographic-view images in high speed, according to the user interactive input. The real-time directional-view display for the specimen for which 3D information is acquired through II microscopy is successfully demonstrated by using resolution-enhanced elemental image arrays. A user interactive feature is also satisfied in the proposed real-time interactive display for II microscopy.

The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment.