• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.11
• /
• pp.2251-2259
• /
• 2009

We propose an algorithm and system which generates 3D stereo image by composition of 2D image from 4 multiple clusters which 1 cluster was composed of 4 multiple cameras based on network. Proposed Schemes have a network-based client-server architecture for load balancing of system caused to process a large amounts of data with real-time as well as multiple cluster environments. In addition, we make use of JPEG compression and RAM disk method for better performance. Our scheme first converts input images from 4 channel, 16 cameras to binary image. And then we generate 3D stereo images after applying edge detection algorithm such as Sobel algorithm and Prewiit algorithm used to get disparities from images of 16 multiple cameras. With respect of performance results, the proposed scheme takes about 0.05 sec. to transfer image from client to server as well as 0.84 to generate 3D stereo images after composing 2D images from 16 multiple cameras. We finally confirm that our scheme is efficient to generate 3D stereo images in multiple view and multiple clusters environments with real-time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.2C
• /
• pp.182-192
• /
• 2010

This paper proposes the hardware IP design of JBIG2 encoder. In order to facilitate the next generation FAX after the standardization of JBIG2, major modules of JBIG2 encoder are designed and implemented, such as symbol extraction module, Huffman coder, MMR coder, and MQ coder. ImpulseC Codeveloper and Xilinx ISE/EDK program are used for the synthesis of VHDL code. To minimize the memory usage, 128 lines of input image are processed succesively instead of total image. The synthesized IPs are downloaded to Virtex-4 FX60 FPGA on ML410 development board. The four synthesized IPs utilize 36.7% of total slice of FPGA. Using Active-HDL tool, the generated IPs were verified showing normal operation. Compared with the software operation using microblaze cpu on ML410 board, the synthesized IPs are better in operation time. The improvement ratio of operation time between the synthesized IP and software is 17 times in case of symbol extraction IP, and 10 times in Huffman coder IP. MMR coder IP shows 6 times faster and MQ coder IP shows 2.2 times faster than software only operation. The synthesized H/W IP and S/W module cooperated to succeed in compressing the CCITT standard document.

• Journal of Internet Computing and Services
• /
• v.21 no.1
• /
• pp.169-178
• /
• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.