• Journal of KIISE:Information Networking
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• v.30 no.4
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• pp.559-571
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• 2003

ENUM(tElephon NUmbering Mapping) is protocol that brings convergence between PSTN Networks and IP Networks using a unique worldwide E.164 telephone number as an identifier between different communication infrastructure. The mechanism provides a bridge between two completely different environments with E.164 number; IP based application services used in PSTN networks, and PSTN based application services used in IP networks. We propose a new way to organize and handle ENUM Tier 2 name servers to improve performance at the name resolution process in ENUM based application service. We build an ENUM based network model when NAPTR(Naming Authority PoinTeR) resource record is registered and managed by area code at the initial registration step. ENUM promises convenience and flexibility to both PSTN and IP users, yet there is no evidence how much patience is required when users decide to use ENUM instead of non-ENUM based applications. We have estimated ENUM response time, and proved how to improve performance up to 3 times when resources are managed by the proposed mechanism. The proposition of this thesis favorably influences users and helps to establish the policy for Tier 2 name server management.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Journal of IKEEE
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• v.25 no.4
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• pp.625-633
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• 2021

This paper describes a scalable architecture for flexible hardware implementation of Montgomery modular multiplication. Our scalable modular multiplier architecture, which is based on a one-dimensional array of processing elements (PEs), performs word parallel operation and allows us to adjust computational performance and hardware complexity depending on the number of PEs used, N_PE. Based on the proposed architecture, we designed a scalable Montgomery modular multiplier (sMM) core supporting eight field sizes defined in SEC2. Synthesized with 180-nm CMOS cell library, our sMM core was implemented with 38,317 gate equivalents (GEs) and 139,390 GEs for N_PE=1 and N_PE=8, respectively. When operating with a 100 MHz clock, it was evaluated that 256-bit modular multiplications of 0.57 million times/sec for N_PE=1 and 3.5 million times/sec for N_PE=8 can be computed. Our sMM core has the advantage of enabling an optimized implementation by determining the number of PEs to be used in consideration of computational performance and hardware resources required in application fields, and it can be used as an IP (intellectual property) in scalable hardware design of elliptic curve cryptography (ECC).

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.1-10
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• 2022

Learning C programming language in electronics education is an important basic education course for understanding computer programming and acquiring the ability to use microprocessors in embedded systems. In order to focus on understanding basic grammar and algorithms, it is a common teaching method to write programs based on C standard library functions in the console window and learn theory and practice in parallel. However, if a student wants to start a project activity or go to a deeper stage after acquiring some basic knowledge of the C language, using only the C standard library function in the console window limits what a student can express or control with the C program. For the purpose of making it easier for a student to use graphics or multimedia resources and increase educational value, this paper studies a case of applying Simple DirectMedia Layer (SDL), an open source software, into the C programming language learning process. The SDL-based programming course applied after completing the basic programming curriculum performed in the console window is introduced, and the educational value is evaluated through a survey. As a result, more than 56% of the respondents expressed positive opinions in terms of improved application ability, stimulating interest, and overall usefulness, and less than 4% of them had negative opinions.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.77-86
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• 2018

For the past few years, KISTI has been servicing an online simulation execution platform, called EDISON, allowing users to conduct simulations on various scientific applications supplied by diverse computational science and engineering disciplines. Typically, these simulations accompany large-scale computation and accordingly produce a huge volume of output data. One critical issue arising when conducting those simulations on an online platform stems from the fact that a number of users simultaneously submit to the platform their simulation requests (or jobs) with the same (or almost unchanging) input parameters or files, resulting in charging a significant burden on the platform. In other words, the same computing jobs lead to duplicate consumption computing and storage resources at an undesirably fast pace. To overcome excessive resource usage by such identical simulation requests, in this paper we introduce a novel framework, called IceSheet, to efficiently manage simulation data based on execution metadata, that is, provenance. The IceSheet framework captures and stores each provenance associated with a conducted simulation. The collected provenance records are utilized for not only inspecting duplicate simulation requests but also performing search on existing simulation results via an open-source search engine, ElasticSearch. In particular, this paper elaborates on the core components in the IceSheet framework to support the search and reuse on the stored simulation results. We implemented as prototype the proposed framework using the engine in conjunction with the online simulation execution platform. Our evaluation of the framework was performed on the real simulation execution-provenance records collected on the platform. Once the prototyped IceSheet framework fully functions with the platform, users can quickly search for past parameter values entered into desired simulation software and receive existing results on the same input parameter values on the software if any. Therefore, we expect that the proposed framework contributes to eliminating duplicate resource consumption and significantly reducing execution time on the same requests as previously-executed simulations.