• Journal of Communications and Networks
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• v.6 no.3
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• pp.209-215
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• 2004

In this paper, a butterfly Log-MAP decoding algorithm for turbo code is proposed. Different from the conventional turbo decoder, we derived a generalized formula to calculate the log-likelihood ratio (LLR) and drew a modified butterfly states diagram in 8-states systematic turbo coded system. By comparing the complexity of conventional implementations, the proposed algorithm can efficiently reduce both the computations and work units without bit error ratio (BER) performance degradation.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.455-462
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• 2001

In this paper, we propose a deterministic algorithm to solve the resource discovery problem, that is, some subset of machines to learn the existence of each other in a large distributed network. Harchol et al. proposed a randomized algorithm solving this problem within O($log^2\;n$) rounds with high probability, which requires O($nlog^2\;n$) connection communication complexity and O($n^2log^2\;n$) pointer communication complexity, where n is the number of machines in the network. His solution is based on randomization method and it is difficult to determine convergence time. We propose an efficient algorithm which improve performance and the non-deterministic characteristics. Our algorithm requires O(log n) rounds which shows O(mlog n) connection communication complexity and O($n^2log\;n$) pointer communication complexity, where m is the number of links in the network.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.33-41
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• 2016

Big data management is becoming an increasingly important issue in both industry and academia of information science community today. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used to improve system reliability. In this paper, we propose a novel big data management architecture specialized for log data. The proposed architecture provides a scalable log management system that consists of client and server side modules for efficient handling of log data. To support large and simultaneous log data from multiple clients, we adopt the Hadoop infrastructure in the server-side file system for storing and managing log data efficiently. We implement the proposed architecture to support various client environments and validate the efficiency through measurement studies. The results show that the proposed architecture performs better than the existing logging architecture by 42.8% on average. All components of the proposed architecture are implemented based on open source software and the developed prototypes are now publicly available.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2629-2636
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• 2015

Recently Big Data management have a rapid increases interest in IT field, much research conducting to solve a problem of real-time processing to Big Data. Lots of resources are required for the ability to store data in real-time over the network but there is the problem of introducing an analyzing system due to aspect of high cost. Need of redesign of the system for low cost and high efficiency had been increasing to solve the problem. In this paper, the document type of database, MongoDB, is used for design a log management system based a document type of database, that is good at big data managing. The suggested log management system is more efficient than other method on log collection and processing, and it is strong on data forgery through the performance evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.36-41
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• 2006

In this paper, we present the performance of iterative decoding with a Turbo decoder and a M-ary QAM(Quadrature Amplitude Modulation) demapper. The demappers are designed with Max-Log-MAP algorithm and it's approximated one. In addition, we provide implementing block for the approximated algorithm. From the results of computer simulations, the approximated algorithm of the Max-Log-MAP has little bit worse than the Max-Log-MAP but suggests low complexity for practical implementation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.141-150
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• 2010

In this paper, performance of a turbo-coded OFDM system is analyzed and simulated in a power line communication channel. Since the power line communication system typically operates in a hostile environment, turbo code has been employed to enhance reliability of transmitted data. The performance is evaluated in terms of bit error probability. As turbo decoding algorithms, MAP (maximum a posteriori), Max-Log-MAP, and SOVA (soft decision viterbi output) algorithms are chosen and their performances are compared. From simulation results, it is demonstrated that Max-Log-MAP algorithm is promising in terms of performance and complexity. It is shown that performance is improved 3dB by increasing the number of iterations, 2 to 8, and interleaver length of a turbo encoder, 100 to 5000. The results in this paper can be applied to OFDM-based high-speed power line communication systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.157-163
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• 2017

The binary and interpolation search algorithms are the most famous among search area algorithms, the former running in $O(log_2n)$ on average, and the latter in $O(log_2log_2n)$ on average and O(n) at worst. Also, the interpolation search use only the probability of key value location without priori information. This paper proposes another search algorithm, which I term a 'hybrid block and interpolation search'. This algorithm employs the block search, a method by which MSB index of a data is determined as a block, and the interpolation search to find the exact location of the key. The proposed algorithm reduces the search range with priori information and search the reduced range with uninformed situation. Experimental results show that the algorithm has a time complexity of $O(log_2log_2n_i)$, $n_i{\simeq}0.1n$ both on average and at worst through utilization of previously acquired information on the block search. The proposed algorithm has proved to be approximately 10 times faster than the interpolation search on average.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.37 no.2
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• pp.1-6
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• 2000

In this paper, simulations are performed using standards proposed by 3GPP and 3GPP2. From the view point of performance analysis based on Turbo code specification, performance of Turbo code in AWGN, proposed by 3GPP, is compared. To make comparison, experimental result of Turbo code that is performed on one of the many block sizes proposed by 3GPP2 in Rayleigh fading channel, is provided. Performance of Turbo decoders using Max_Log MAP and Log MAP are compared.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.366-370
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• 1998

One major limitation of the efficiency of parallel computer designs has been the prohibitively high cost of parallel communication between processors and memories. Linear order concentrators can be used to build theoretically optimal interconnection schemes. Current designs call for building superconcentrators from concentrators, then using these to recursively partition the connection streams O(log2N) times to achieve point-to-point routing. Since the superconcentrators each have O(N) hardware complexity but O(log2N) depth, the resulting networks are optimal in hardware, but they are of O(log2N) depth. This pepth is not better than the O(log2N) depth Bitonic sorting networks, which can be implemented on the O(N) shuffle-exchange network with message passing. This paper introduces a new method of constructing networks using linear order concentrators and expanders, which can be used to build interconnection networks with O(log2N) depth as well as O(Nlog2N) hardware cost. (All logarithms are in base 2 throughout paper)

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.1-7
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• 2013

Big data management is becoming increasingly important in both industry and academia of information science community. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used as information for qualification. This paper presents a big data management architecture specialized for log data. Specifically, it provides the aggregation of log messages sent from multiple clients and provides intelligent functionalities such as analyzing log data. The proposed architecture supports an asynchronous process in client-server architectures to prevent the potential bottleneck of accessing data. Accordingly, it does not affect the client performance although using remote data store. We implement the proposed architecture and show that it works well for processing big log data. All components are implemented based on open source software and the developed prototypes are now publicly available.