• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.97-103
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• 2019

Electronic contract systems are widely used to integrate and manage the contract management process based on the development of ICT technology. Recently, improvement methods using block chain technology are being studied. However, contract management systems have processing performance, security vulnerabilities, data entry, and service accessibility issues. In this paper, we propose a block - chain based contract management platform with block chain, smart contract, and Rest API. The suggested platform includes the RPBFT algorithm which solves the processing performance and security vulnerability of the existing consensus authentication algorithm, and the algorithm to prevent data entry and enhance transparency of participants. The block-chain-based contract management platform proposed in this paper provides a use environment with improved processing performance, security, reliability, and transparency, and can be used through API without burdening construction. Therefore, The effect can be expected.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.81-87
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• 2013

In this paper, we present an implementation of Long Term Evolution (LTE) Uplink (UL) system on a Software Defined Radio (SDR) platform using a conventional Personal Computer (PC), which adopts Graphic Processing Units (GPU) and Universal Software Radio Peripheral2 (USRP2) with URSP Hardware Driver (UHD) for SDR software modem and Radio Frequency (RF) transceiver, respectively. We have adopted UHD because UHD provides flexibility in the design of transceiver chain. Also, Cognitive Radio (CR) engine have been implemented by using libraries from UHD. Meanwhile, we have implemented the software modem in our system on GPU which is suitable for parallel computing due to its powerful Arithmetic and Logic Units (ALUs). From our experiment tests, we have measured the total processing time for a single frame of both transmit and receive LTE UL data to find that it takes about 5.00ms and 6.78ms for transmit and receive, respectively. It particularly means that the implemented system is capable of real-time processing of all the baseband signal processing algorithms required for LTE UL system.

• Current Optics and Photonics
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• v.2 no.5
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• pp.453-459
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• 2018

We have developed a high-performance signal-processing and image-rendering heterogeneous computation system for optical coherence tomography (OCT) on mobile processor. In this paper, we reveal it by demonstrating real-time OCT image processing using a Snapdragon 800 mobile processor, with the introduction of a heterogeneous image visualization architecture (HIVA) to accelerate the signal-processing and image-visualization procedures. HIVA has been designed to maximize the computational performances of a mobile processor by using a native language compiler, which targets mobile processor, to directly access mobile-processor computing resources and the open computing language (OpenCL) for heterogeneous computation. The developed mobile image processing platform requires only 25 ms to produce an OCT image from $512{\times}1024$ OCT data. This is 617 times faster than the naïve approach without HIVA, which requires more than 15 s. The developed platform can produce 40 OCT images per second, to facilitate real-time mobile OCT image visualization. We believe this study would facilitate the development of portable diagnostic image visualization with medical imaging modality, which requires computationally expensive procedures, using a mobile processor.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.321-326
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• 2021

In a multi-cloud environment, it is necessary to minimize physical movement for efficient interoperability of distributed source data without building a data warehouse or data lake. And there is a need for a data platform that can easily access data anywhere in a multi-cloud environment. In this paper, we propose a new platform based on data fabric centered on a distributed platform suitable for cloud environments that overcomes the limitations of legacy systems. This platform applies the knowledge graph database technique to the physical linkage of source data for interoperability of distributed data. And by integrating all data into one scalable platform in a multi-cloud environment, it uses the holochain technique so that companies can easily access and move data with security and authority guaranteed regardless of where the data is stored. The knowledge graph database mitigates the problem of heterogeneous conflicts of data interoperability in a decentralized environment, and Holochain accelerates the memory and security processing process on traditional blockchains. In this way, data access and sharing of more distributed data interoperability becomes flexible, and metadata matching flexibility is effectively handled.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2292-2313
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• 2023

With improving computing performance, various digital platforms are being developed to enable easily utilization of high-performance computing environments. EDISON 1.0 is an online simulation platform widely used in computational science and engineering education. As the research paradigm changes, the demand for developing the EDISON 1.0 platform centered on simulation into the EDISON 2.0 platform centered on data and artificial intelligence is growing. Herein, a data framework, a core module for data-centric research on EDISON 2.0 digital platform, is proposed. The proposed data framework provides the following three functions. First, it provides a data repository suitable for the data lifecycle to increase research reproducibility. Second, it provides a new data model that can integrate, manage, search, and utilize heterogeneous data to support a data-driven interdisciplinary convergence research environment. Finally, it provides an exploratory data analysis (EDA) service and data enrichment using an AI model, both developed to strengthen data reliability and maximize the efficiency and effectiveness of research endeavors. Using the EDISON 2.0 data framework, researchers can conduct interdisciplinary convergence research using heterogeneous data and easily perform data pre-processing through the web-based UI. Further, it presents the opportunity to leverage the derived data obtained through AI technology to gain insights and create new research topics.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.137-143
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• 2013

This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.353-357
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• 2014

This paper, presents an efficient hardware architecture of high performance SVC(Scalable Video Coding). This platform uses dedicated hardware architecture to improve its performance. The architecture was prototyped in Verilog HDL and synthesized using the Synopsys Design Compiler with a 65nm standard cell library. At a clock frequency of 266MHz, This platform contains 2,500,000 logic gates and 750,000 memory gates. The performance of the platform is indicated by 30 frames/s of the SVC encoder Full HD($1920{\times}1080$), HD($1280{\times}720$), and D1($720{\times}480$) at 266MHz.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.553-558
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• 2008

Advancement in information technology have enabled applying vision sensor to railway, such as CCTV. CCTV has been widely used in railway application, however the CCTV is a passive system that provide limited capability to maintain safety from boarding platform. The station employee should monitor continuously CCTV monitors. Therefore immediate recognition and response to the situation is difficultin emergency situation. Recently, urban transit operators are pursuing applying an unattended station operation system for their cost reduction. Therefore, an intelligent monitoring system is need for passenger's safety in railway. The paper proposes a vision based monitoring system and object detection algorithm for passenger's safety in railway platform. The proposed system automatically detects accident in platform and analyzes level of danger using image processing technology. The system uses stereo vision technology with multi-sensors for minimizing detection error in various railway platform conditions.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.253-258
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• 2021

In this paper, we developed a hardware and software platform of the real-time data logging system to verify radar FEM (Front-end Module) and signal-processing algorithms. We developed a hardware platform based on FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) and implemented firmware software to verify the various FEMs. Moreover, we designed PC based software platform to control radar logging parameters and save radar data. The developed platform was verified using 24 GHz multiple channel FMCW (Frequency Modulated Continuous Wave) in an environment of stationary and moving targets of chamber room.

• Journal of Advanced Navigation Technology
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• v.18 no.4
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• pp.401-408
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• 2014

Hadoop is composed with MapReduce programming model for distributed processing and HDFS distributed file system. Hadoop is suitable framework for big data processing, but processing of mass small files have many problems. The processing of mass small file in hadoop have problems to created one mapper per one file, and it have problems to needed many memory for store of meta information of file. This paper have comparison evaluation processing method of mass small file with various method in hadoop platform. The processing of general compression format is inadequate because of processing by one mapper regardless of data size. The processing of sequence and hadoop archive file is removed memory problem of namenode by compress and combine of small file. Hadoop archive file is faster then sequence file about combine time of small file. The processing using CombineFileInputFormat class is needed not combine of small file, and it have similar speed big data processing method.