• The KIPS Transactions:PartC
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• v.9C no.3
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• pp.375-384
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• 2002

In the current Internet, the QoS of interactive applications is hardly guaranteed because of variable bandwidth, packet loss and delay. Moreover, VoIP which is becoming an important part of the information infra-structure in these days, is susceptible to network packet loss and end-to-end delay. Therefore, it needs error control mechanisms in network level or application level. The FEC-based error control mechanisms are used for interactive audio application such as VoIP. The FEC sends a main information along with redundant information to recover the lost packets and adjusts redundant information depending on network conditions to reduce the bandwidth overhead. However, because most of the error control mechanisms do not consider end-to-end delay but packet loss rate, their performances are poor. In this paper, we propose a new error control algorithm, SCCRP, considering packet loss rate as well as end-to-end delay. Through experiments, we confirm that the SCCRP has a lower packet loss rate and a lower end-to-end delay after reconstruction.

• Journal of Digital Contents Society
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• v.12 no.2
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• pp.151-156
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• 2011

The analysis and image evaluation the Ray-sum technique and Shaded Surface Display (under SSD) technique which is the reconstruction image processing technique after the CT scan was evaluated and the usability of the three-dimensional information offering was confirmed in the patient with stomach cancer. After obtaining the raw data by using 64-MDCT in 20 patient with stomach cancers, the image reconstruction processing was done. It was evaluated to describe accurately the analyzed result Ray-sum and SSD reconstruction image everyone anatomical structure. In the precision estimation of the image, the lesion location could coincide in the Ray-sum and SSD reconstruction image majority with the gastro fiberscope and we can know than the gastro fiberscope over 6cm that there was the error. In addition, We could know that degree of accordance of the results of the image interpretation about the lesion and endoscope and pathological opinion were high.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.7
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• pp.646-652
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• 2016

This paper presents a tumor detection for breast cancer that utilizes two-dimensional(2D) image reconstruction with microwave tomographic imaging. The breast cancer detection system under development consists of 16 transmit/receive antennas, and the microwave tomography system operates at 1,700 MHz. The four types of breast(ED-, HD-, SC-, and FT-type) are used for image reconstruction. To solve a 2D inverse scattering problem, the method of moments(MoM) is employed for forward problem solving, and the simplex method employed as an optimization algorithm. The results of the reconstructed image show that the ED- and HD-types of breasts are well reconstructed, but SC- and FT-type breasts are not well because of the error including.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.25-32
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• 2005

This paper presents a method for point-based 3D building reconstruction using Lidar data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression) in the first place. If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LIDAR data and digital map could be a feasible method of modeling 3D building reconstruction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.93-100
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• 2005

We propose an optimal thresholding method for the voxel coloring in the reconstruction of a 3D shape. Our purposed method is a new approach to resolve the trade-off error of the threshold value on determining the photo-consistency in the conventional method. Optimal thresholding value is decided to compare the photo-consistency of a surface with inside voxel on the optic ray of the center camera. As iterating the process of the vokels, the threshold is approached to the optimal value for the individual surface voxel. And also, graph cut method is reduced to the surface noise on eliminating neighboring voxel. To verify the proposed algorithm, we simulated in the virtual and real environment. It is advantaged to speed up and accuracy of a 3D face reconstruction by applying the methods of optimal threshold and graph as compare with conventional algorithms.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.157-161
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• 2009

Minimum Letter Flips(MLF) and Weighted Minimum Letter Flips(WMLF) can perform the haplotype reconstruction more accurately from SNP fragments when they have many errors and gaps by introducing the related genotype information. And it is known that WMLF is more accurate in haplotype reconstruction than those based on the MLF. In the paper, we analyze two models under the conditions that the different rates of homozygous site in the genotype information and the different confidence levels according to the sequencing quality. We compare the performance of the two models using neural network and genetic algorithm. If the rate of homozygous site is high and sequencing quality is good, the results of experiments indicate that WMLF/GI has higher accuracy of haplotype reconstruction than that of the MCIH especially when the error rate and gap rate of SNP fragments are high.

• Korean Journal of Optics and Photonics
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• v.25 no.5
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• pp.273-278
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• 2014

We present an algorithm for surface reconstruction from the second-derivative data for free-form aspherics, which uses a subaperture scanning system that measures the local surface profile and determines the three second-derivative values at those local sampling points across the free-form surface. The three second-derivative data were integrated to get a map of x- and y-slopes, which went through a second Southwell integration step to reconstruct the surface profile. A synthetic free-form surface 200 mm in diameter was simulated. The simulation results show that the reconstruction error is 19 nm RMS residual difference. Finally, the sensitivity to noise is diagnosed for second-derivative Gaussian random noise with a signal to noise ratio (SNR) of 16, the simulation results proving that the suggested method is robust to noise.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.