• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.7
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• pp.1131-1138
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• 2001

This paper presents the new Compact 2D Haar-wavelet-based MultiResolution Time-Domain method (MRTD) as an accelerating algorithm for the conventional Compact BD Finite-Difference Time-Domain method (FDTD). To validate this algorithm, we analyzed the dispersion characteristics of the hollow rectangular waveguide and dielectric slab-loaded rectangular waveguide. The results of the proposed method are very weal agreed with those of both the conventional analytic method and the Compact 2D FDTD method. The CPU time for analysis of this method is reduced to about a half of the conventional Compact 2D FDTD method. The proposed method is valuable as a fast algorithm in the research of dispersion characteristics of waveguide structures.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.14-22
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• 2011

The conventional satellite image fusion methods usually add the same amount of higher frequency components extracted from the panchromatic image to all the multispectral images. However, it is noted that each of multispectral images has different amount of overlap with the panchromatic image in terms of its spectrum, and also has different intensities. Thus giving the same amount of high frequency contents to all the spectral bands does not match with this observation, which causes color distortion in the fused image. In this paper, we propose a new wavelet-domain satellite image fusion algorithm that can compensate for these differences in intensity and spectrum overlap. For the compensation of intensity differences, we first estimate the high resolution multispectral images from P, considering the relative intensity ratios. For the compensation of the amount of spectral overlap, their wavelet coefficients are appended to the conventional wavelet-domain method where the coefficients for the addition is determined by the amount of spectrum overlap. Experiments are conducted for the IKONOS satellite images whose spectrums are well known, and the results show that the proposed algorithm gives higher PSNR and correlation coefficients compared to the conventional methods.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.795-802
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from a noisy image. In order to estimate variance, information of neighboring region is used generally. The size of neighbor region is varied according to the regional characteristics of image. More accurate estimation of edge variance is due to smaller region of neighbor, on the other hands, larger region of neighbor is used to estimate the variance of flat region. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new method for determining the range of neighbors to estimate the variance in wavelet domain. Firstly, a significance map is constructed using the parent-child relationship of wavelet domain. Based on the number of the significant wavelet coefficients, the range of neighbors is determined and then the variance of the original signal is estimated using ML(maximum likelihood method. Experimental results show that the proposed method yields better results than conventional methods for image denoising.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.102-112
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• 2012

In this paper, we propose a texture classification using wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features with WPCA (whitened principal component analysis) classifier. In the proposed method, the wavelet transform is first applied to a query image. The BDIP and BVLC operators are next applied to the wavelet subbands. Global moments for each subband of BDIP and BVLC are then computed and fused into a feature vector. In classification, the WPCA classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the query feature vector. Experimental results show that the proposed method yields excellent texture classification with low feature dimension for test texture image DBs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.27-36
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• 2006

Lossless data embedding is a method to insert information into a host image that guarantees complete restoration when the extraction has been done. In this paper, we propose a noble reversible data embedding algorithm for images in wavelet domain. The proposed embedding technique, which modifies histogram of wavelet coefficient, is composed of two inserting steps. Data is embedded to wavelet coefficient using modification of histogram in first embedding process. Second embedding step compensates the distortion caused by the first embedding process as well as hides more information. Hence we achieve higher inserting capacity. In view of the relationship between the embedding capacity and the PSNR value, our proposed method shows considerably higher performance than the current reversible data embedding methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.19-26
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• 2005

Image interpolation in the wavelet domain usually takes advantage of the probabilistic models for the intrascale statistics and the interscale dependency. In this paper, we adopt the linear model for the absolute values of wavelet coefficients of interpolated image across scale to estimate the variances of extrapolated bands. The proposed algorithm uses randomly generated wavelet coefficients based on the estimated parameters for probabilistic model. Random number generation according to the estimated probabilistic model may induce the 'salt and pepper' noise in subbands. We reduce the noise power by Wiener filtering. We observe that the proposed method generates the histogram of the subband coefficients similar to the that of original image. Experimental results show that our method outperforms the previous wavelet-domain interpolation method as well as the conventional bicubic method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.58-64
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• 2009

Digital radiography (DR) images are corrupted by the additive noise, and also distorted by system impulse response. These unwanted phenomena are obstacles to obtain the desired image. To recover the original image, we applied multiscale Wiener filters in wavelet domain for DR images. The multiscale Wiener filter is first proposed by Chen for the restoration of fractal signals which are distorted by the system impulse response and additive noise. In this paper, we extended the multiscale Wiener filter to the two dimensional data. To compare the performance of ours with others, some simulations are given for a couple of wavelet filters with different wavelet levels, system impulse reponses and various noise power. When the addive noise powers are between 20-32 dB, the signal to noise ratio(SNR) of the proposed system is 0.5-2.0 dB better than that of the traditional Wiener filter method.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.415-425
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• 2002

In this paper, we present a content-based image retrieval method which is based on the feature extraction in the wavelet transform domain. In order to overcome the drawbacks of the feature vector making up methods which use the global wavelet coefficients in subbands, we utilize the energy value of wavelet coefficients, and the shape-based retrieval of objects is processed by moment which is invariant in translation, scaling, rotation of the objects The proposed methods reduce feature vector size, and make progress performance of classification retrieval which provides fast retrievals times. To offer the abilities of region-based image retrieval, we discussed the image segmentation method which can reduce the effect of an irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The region-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.443-453
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• 2008

In this paper, the 1-level DWT(Discrete Wavelet Transform) coefficients of a binary logo image are used as the watermark. The watermark should be inserted in the same band which is equivalent to the host image when the watermark is inserted in the wavelet domain. This is the image fusion of the proposed watermarking method. The watermark is inserted in relatively significant coefficients after the insertion area is defined. The more significant coefficients have the important information because they are identified as the edge and major surface in images. The significant coefficients are defined when their absolute value exceeds the threshold. The standard deviation is used as the weight value of watermark insertion in order to strengthen the weight of the watermark insertion according to the value of the coefficients. The proposed watermarking method is an adaptive scheme, and the proposed two detection algorithms can be adaptively used when the watermarked image is distorted by cropping, filtering, or compression.