• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1236-1248
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• 1991

A new fast algorithm for computing the discrete cosine transform(DCT) Is developed decomposing N-point DCT into an N /2-point DCT and two N /4 point transforms(transpose of an N /4-point DCT. TN/t'and)It has an important characteristic that in this method, the roundoff noise power for a fixed point arithmetic can be reduced significantly with respect to the wellknown fast algorithms of Lee and Chen. since most coefficients for multiplication are distributed at the nodes close to the output and far from the input in the signal flow graph In addition, it also shows three other versions of factorization of DCT matrix with the same number of operations but with the different distributions of multiplication coefficients.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.74-83
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• 1997

In this paper, we propose a new fractal image decoding algorithm with fast convergence speed by using the data dependence and the improved initial image estimation. Conventional method for fractal image decoding requires high-degrdd computational complexity in decoding process, because of iterated contractive transformations applied to whole range blocks. On proposed method, Range of reconstruction imagte is divided into referenced range and data dependence region. And computational complexity is reduced by application of iterated contractive transformations for the referenced range only. Data dependence region can be decoded by one transformations when the referenced range is converged. In addition, more exact initial image is estimated by using bound () function in case of all, and an initial image more nearer to a fixed point is estimated by using range block division estimation. Consequently, the convergence speed of reconstruction iamge is improved with 40% reduction of computational complexity.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.429-436
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• 2004

This paper proposes an independent component analyses(ICAs) of the fixed-point (FP) algorithm based on Newton and secant method by adding the kurtosis, respectively. The kurtosis is applied to cluster the analyzed components, and the FP algorithm is applied to get the fast analysis and superior performance irrelevant to learning parameters. The proposed ICAs have been applied to the problems for separating the 6-mixed signals of 500 samples and 10-mixed images of $512\times512$ pixels, respectively. The experimental results show that the proposed ICAs have always a fixed analysis sequence. The results can be solved the limit of conventional ICA without a kurtosis which has a variable sequence depending on the running of algorithm. Especially. the proposed ICA can be used for classifying and identifying the signals or the images. The results also show that the secant method has better the separation speed and performance than Newton method. And, the secant method gives relatively larger improvement degree as the problem size increases.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.91-100
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• 2006

A scheme is proposed for implementing the block LMS algorithm in a block floating point framework that permits processing of data over a wide dynamic range at a processor complexity and coat as low as that of a fixed point processor. The proposed scheme adopts appropriate formats for representing the filter coefficients and the data. Using these and a new upper bound on the step size, update relations for the filter weight mantissas and exponent are developed, taking care so that neither overflow occurs, nor are quantifies which are already very small multiplied directly. It is further shown how the mantissas of the filter coefficients and also the filter output can be evaluated faster by suitably modifying the approach of the fast block LMS algorithm

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.4
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• pp.309-324
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• 1987

In recent years has been an increasing interest with respect to using the discrete cosine transform(DCT) of which performance is found close to that of the Karhumen-Loeve transform, known to be optimal in the area of digital image processing for tha purpose of the image data compression. Among most of reported algorithms aimed at lowering the coputation complexity. Chen's algorithm is is found to be most popular, Recently, Lee proposed a now algorithm of which the computational complexity is lower than that of Chen's. but its performance is significantly degraded by FWL(Finite-Word-Lenght) effects as a result of employinga a fixed-poing arithmetic. In this paper performance evaluation of these two algorithms and error analysis of FWL effect are described. Also a scaling technique which we call Up & Down-scaling is proposed to allevaiate a performance degradation due to fixed-point arithmetic. When the 16x16point 2DCT is applied on image data and a 16-bit fixed-point arithmetic is employed, both the analysis and simulation show that is colse to that of Chen's.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.745-765
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• 2015

A nonlinear Finite Element (FE) algorithm is proposed to analyze the Reinforced Concrete (RC) columns subjected to Cyclic Loading (CL), Cyclic Oriented Lateral Force and Axial Loading (COLFAL), Monotonic Loading (ML) or Oriented Pushover Force and Axial Loading (OPFAL) in any direction. In the proposed algorithm, the following parameters are considered: uniaxial behavior of concrete and steel elements, the pseudo-plastic hinge produced in the critical sections, and global behavior of RC columns. In the proposed numerical simulation, the column is discretized into two Macro-Elements (ME) located between the pseudo-plastic hinges at critical sections and the inflection point. The critical sections are discretized into Fixed Rectangular Finite Elements (FRFE) in general cases of CL, COLFAL or ML and are discretized into Variable Oblique Finite Elements (VOFE) in the particular cases of ML or OPFAL. For pushover particular case, a fairly fast converging and properly accurate nonlinear simulation method is proposed to assess the behavior of RC columns. The proposed algorithm has been validated by the results of tests carried out on full-scale RC columns.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1317-1328
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• 1996

In this paper, we propose a fast image coding algorithm to shorten long time to take on fractal image encoding. For its Performance evaluation, the algorithm compares with other traditional fractal coding methods. In the traditional fractal image coding methods, an original image is contracted by a factor in order to make the corresponding image to be compared with. Them, the whole area of the contracted image is searched in order to find the fixed point of contractive transformation of the orignal image corresponding to the contracted image. It needs a lot of searching time on encoding However, the proposed algorithm considerable reduces encoding time by using scaling method and limited search area method. On comparison of the proposed algorithm with Joaquin's method, the proposed algorithm is at least 180 times as fast as that of Jacquin's method on encoding time with a little degradation of the decoded image quality and a little increase of the compression rate. There-for, it is found that the proposed algorithm largely improves the performance in the aspect of encoding time when compared with other fractal image coding methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4814-4834
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• 2018

This paper presents an architecture for wireless sensor networks (WSNs) with blind source separation (BSS) applied to retrieve the received mixing signals of the sink nodes first. The little-to-no need of prior knowledge about the source signals of the sink nodes in the BSS method is obviously advantageous for WSNs. The optimization problem of the BSS of multiple independent source signals with complex and noncircular distributions from observed sensor nodes is considered and addressed. This paper applies Castella's reference-based scheme to Novey's negentropy-based algorithms, and then proposes a novel fast fixed-point (FastICA) algorithm, defined as the reference-signal negentropy complex FastICA (RSNT-cFastICA) for complex-valued noncircular-distribution source signals. The proposed method for the sink nodes is substantially more efficient than Novey's quasi-Newton algorithm in terms of computational speed under large numbers of samples, can effectively improve the power consumption effeciency of the sink nodes, and is significantly beneficial for WSNs and wireless communication networks (WCNs). The effectiveness and performance of the proposed method are validated and compared with three related BSS algorithms through theoretical analysis and simulations.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2S
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• pp.638-648
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• 2000

This paper introduces a new technique for block is matching motion estimation. Since the temporal correlation of the image sequence, the motion vector of a block is highly related to the motion vector of the same coordinate block in the previous image frame. If we can obtain useful and enough information from the motion vector of the same coordinate block of the previous frame, the total number of search points used to find the motion vector of the current block may be reduced significantly. Using that idea, an efficient predicted direction search algorithm (PDSA) for block matching algorithm is proposed. Based on the direction of the blocks of the two successive previous frames, if the direction of the to successive blocks is same, the first search point of the proposed PDSA is moved two pixels to the direction of the block. The searching process after moving the first search point is processed according to the fixed search patterns. Otherwise, full search is performed with search area $\pm$2. Simulation results show that PSNR values are improved up to the 3.4dB as depend on the image sequences and improved about 1.5dB on an average. Search times are reduced about 20% than the other fast search algorithms. Simulation results also show that the performance of the PDSA scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS(Full Search) algorithm.

• Annual Conference of KIPS
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• 2014.04a
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• pp.904-907
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• 2014

In this work we present a robust and fast approach to estimate 3D vehicle pose that can provide results under a specific traffic surveillance conditions. Such limitations are expressed by single fixed CCTV camera that is located relatively high above the ground, its pitch axes is parallel to the reference plane and the camera focus assumed to be known. The benefit of our framework that it does not require prior training, camera calibration and does not heavily rely on 3D model shape as most common technics do. Also it deals with a bad shape condition of the objects as we focused on low resolution surveillance scenes. Pose estimation task is presented as PnP problem to solve it we use well known "POSIT" algorithm [1]. In order to use this algorithm at least 4 non coplanar point's correspondence is required. To find such we propose a set of techniques based on model and scene geometry. Our framework can be applied in real time video sequence. Results for estimated vehicle pose are shown in real image scene.