• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3185-3199
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• 1997

This paper suggests techniques to reduce coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same com- pression rate.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.398-407
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• 2015

Compressive sensing (CS) has drawn much interest as a novel sampling technique that enables sparse signal to be sampled under the Nyquitst/Shannon rate. By noting that the block-based CS can still keep spatial correlation in measurement domain, this paper proposes to adapt sampling rate of each block in frame according to its characteristic defined by edge information. Specifically, those blocks containing more edges are assigned more measurements utilizing block-wise correlation in measurement domain without knowledge about full sampling frame. For natural image, the proposed adaptive rate allocation shows considerable improvement compared with fixed subrate block-based CS in both terms of objective (up to 3.29 dB gain) and subjective qualities.

• The KIPS Transactions:PartA
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• v.13A no.2 s.99
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• pp.101-110
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• 2006

One of the most effective ways to improve cache performance is to exploit both temporal and spatial locality given by any program executional characteristics. This paper proposes a data cache with small space for low power but high performance on multimedia applications. The basic architecture is a split-cache consisting of a direct-mapped cache with small block sire and a fully-associative buffer with large block size. To overcome the disadvantage of small cache space, two mechanisms are enhanced by considering operational behaviors of multimedia applications: an adaptive multi-block prefetching to initiate various fetch sizes and an efficient block filtering to remove rarely reused data. The simulations on MediaBench show that the proposed 5KB-cache can provide equivalent performance and reduce energy consumption up to 40% as compared with 16KB 4-way set associative cache.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.17-31
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• 2020

With the rapid development of block chain technology, attempts have been made to put the block chain technology into practical use in various fields such as finance and logistics, and also in the public sector where data integrity is very important. Defense Operations In addition, strengthening security and ensuring complete integrity of the command communication network is crucial for operational operation under the network-centered operational environment (NCOE). For this purpose, it is necessary to construct a command communication network applying the block chain network. However, the block chain technology up to now can not solve the security issues such as the 51% attack. In particular, the Practical Byzantine fault tolerance (PBFT) algorithm which is now widely used in blockchain, does not have a penalty factor for nodes that behave maliciously, and there is a problem of failure to make a consensus even if malicious nodes are more than 33% of all nodes. In this paper, we propose a Adaptive Consensus Bound PBFT (ACB-PBFT) algorithm that incorporates a penalty mechanism for anomalous behavior by combining the Trust model to improve the security of the PBFT, which is the main agreement algorithm of the blockchain.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.46-54
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• 2011

In this paper, we propose a low-complexity loop filtering method depending on transform-block regions. Block adaptive loop filter (BALF) was developed to improve about 10% in compression performance for the next generation video coding. The BALF employs the Wiener filter that makes reconstructed frames close to the original ones and transmits filter-related information. However, the BALF requires high computational complexity, while it can achieve high compression performance because the block adaptive loop filter is applied to all the pixels in blocks. The proposed method is a new loop filter that classifies pixels in a block into inner and boundary regions based on the characteristics of the integer transform and derives optimum filters for each region. Then, it applies the selected filters for the inner and/or boundary regions. The decoder complexity can be adjusted by selecting region-dependent filter to be used in the decoder side. We found that the proposed algorithm can reduce 35.5% of computational complexity with 2.56% of compression loss, in case that only boundary filter is used.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.4
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• pp.295-306
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• 2013

In this paper, we consider the adaptive multigrid method for solving the Black-Scholes equation to improve the efficiency of the option pricing. Adaptive meshing is generally regarded as an indispensable tool because of reduction of the computational costs. The Black-Scholes equation is discretized using a Crank-Nicolson scheme on block-structured adaptively refined rectangular meshes. And the resulting discrete equations are solved by a fast solver such as a multigrid method. Numerical simulations are performed to confirm the efficiency of the adaptive multigrid technique. In particular, through the comparison of computational results on adaptively refined mesh and uniform mesh, we show that adaptively refined mesh solver is superior to a standard method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.229-232
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• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.758-760
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• 1995

The purpose of noise cancellation is to estimating signals corrupted by additive noise or interference. In this paper, an adaptive noise canceller is built from a Walsh filter with a new adaptive algorithm. The Walsh filter consists of a Walsh function. Since the Walsh functions are either even or odd functions, the covariance matrix in the tap gain adjustment algorithm can be reduced to a simple form. In this paper, minimization of the mean squre error is accomplished by a proposed adaptive algorithm. The conventional adaptation techniques use a fixed time constant convergence factor by trial and error methods. In this paper, a convergence factor is obtained that is tailored for each adaptive filter coefficient and is updated at each block iteration.

• Journal of IKEEE
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• v.20 no.1
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• pp.1-8
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• 2016

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.190-192
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• 2004

In this paper, we propose a low bit-rate embedded video compression scheme with 3-D block partition coding in the wavelet domain. The proposed video compression scheme includes multi-level 3-dimensional dyadic wavelet decomposition, raster scanning within each subband, formation of block, 3-D partitioning of block, and adaptive arithmetic entropy coding. Although the proposed video compression scheme is quit simple, it produces bit-stream with good features, including SNR scalability from the embedded nature. Experimental results demonstrate that the proposed video compression scheme is quit competitive to other good wavelet-based video coders in the literature.