• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.487-494
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• 2013

Medical image watermarking has received extensive attention as wide security services in the healthcare information system. This paper proposes a blind medical image watermarking approach on the segmented gray-matter (GM) images by utilizing discrete wavelet transform (DWT) and discrete cosine transform (DCT) along with enhanced suppressed fuzzy C-means (EnSFCM) for the optimal selection of sub-blocks position to insert a watermark. Experimental results show that the proposed approach outperforms other methods in terms of peak signal to noise ratio (PSNR) and M-SVD. In addition, the proposed approach shows better robustness than other methods in normalized correlation (NC) values against several attacks, such as noise addition, filtering, JPEG compression, blurring, histogram equalization, and cropping.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.783-786
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• 2000

본 논문에서는 웨이브렛 변환을 적용한 광대역 음성부호화 알고리즘을 제안하였다. 제안한 음성부호화 알고리즘은 split-band 구조를 가지며, 16 kHz로 sampling된 입력신호를 QMF를 이용해서 동일한 대역폭을 갖는 두 개의 subband 신호로 나누고 이를 8kHz의 sampling율을 갖도록 downsampling 한다. 그리고 저대역 신호는 GSM-EFR 음성부호화 알고리즘을 이용하여 부호화하고, 고대역 신호는 DWT(Discrete Wavelet Transform)을 적용하여 subband로 나누어 부호화하였다. 각 subband에서 양자화 된 파라미터는 IDWT(Inverse DWT)과정을 거쳐서 upsampling되고 합성 QMF를 통과시켜 최종 합성음을 구하였다. 제안한 음성부호화기는 저대역 신호의 GSM-EFR 부호화에 12.2 kbps, 웨이브렛 변환을 이용한 고대역 신호의 부호화에 7.8 kbps로 전체 20 kbps의 전송율을 가지면서 G.722 표준안의 56 kbps에서의 합성음과 비슷한 음질을 나타내었다.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.362-368
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• 2009

Electrical fires have been occurred continuously in spite of installing ELB. Therefore the concern with the electrical arc-fault that cause the fire has growing. This paper measured series arc fault currents by the method of arc generator test in UL standard 1699. The used analysis methods in this paper are three different ways using DWT(discrete wavelet transform) those are frequently used for the arc fault current signal analysis. The arc fault detection probability is 100 % by method using noise-energy/shoulder-duration ratio of approximation coefficient. As these results, the variation of noise-energy and shoulder-duration ratio of approximation coefficient are founded important factors for the analysis of arc fault.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.282-285
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• 2000

Three representations are known to implement the discrete wavelet transform (DWT) ; i.e., direct, lifting and matrix forms. In these representations, direct and lifting forms are well known so far. This paper derives the matrix form of the DWT from the direct form. Then, we implement these three representations on a programmable digital signal processor (in the following, DSP processor) and compare them in terms of the number of calculations and instruction cycles. As a result, we confirm that the lifting form has the lowest number of calculations and cycles, and the matrix form has an effective decrease in the number of cycles than other representations on the DSP processor.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1795-1796
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• 2007

본 논문은 DWT(Discrete Wavelet Transform)기반의 블라인드 워터마킹 기법을 제안 하였다. DWT를 하였을 때, 두 개의 서브밴드들의 계수 값을 변환하여 워터마크를 삽입한다. 기존에는 워터마크를 로고나 signature등을 많이 사용 하였으나, 여기서는 이차원 바코드인 Data Matrix를 워터마크로 사용 하였다. Data Matrix자체가 오류 검출 및 복원 알고리즘을 가지고 있어, 워터마크 추출 시 비교적 작은 에러는 Data Matrix의 복원 알고리즘에 의해 Data Matrix의 암호화된 정보를 복원 할 수 있다.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.440-441
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• 2006

The fault location algorithm based on wavelet transform was developed to locate the fault more accuracy after the operation of relay. The stationary wavelet transform(SWT) was introduced instead of conventional discrete wavelet transform(DWT) because SWT has redundancy properties which is more useful in noise signal processing. The algorithm was based on the correlation of wavelet coefficients at multi-scales. Fault location algorithm was tested by simulation on real power cable system. From these results, the fault can be located even in very difficult situations, such as at different inception angle and fault resistance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.184-190
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• 2008

The fault location algorithm based on stationary wavelet transform was developed to locate the fault point more accurately. The stationary wavelet transform(SWT) was introduced instead of conventional discrete wavelet transform(DWT) because SWT has redundancy properties which is more useful in noise signal processing. In previous paper, noise cancellation technique based on the correlation of wavelet coefficients at multi-scales was introduced, and the efficiency was also proved in full. In this paper, fault section discrimination and fault location algorithm using noise cancellation technique were tested by ATP simulation on real power cable systems. From these results, the fault can be located even in very difficult and complicated situations such as different inception angle and fault resistance.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.640-641
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• 2007

In this paper, the discrete wavelet transform (DWT) was applied a fault detection of a synchronous generator being superior to a transient state signal analysis and being easy to real time realization. The fault signals after executing a terminal fault modeling collect using a MATLAB package, and calculate the wavelet coefficients through the process of a multi-level decomposition (MLD). The proposed algorithm of a fault detection of a generator using Daubechies WT (wavelet transform) was executed with a C language for the commend line function and for the real time realization after analyzing MATLAB's graphical interface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.883-900
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• 2017

In this paper block based blind secure gray image watermarking scheme based on discrete wavelet transform and singular value decomposition is proposed. In devising the proposed scheme, security is given high importance along with other two requirements: robustness and imperceptibility. The use of discrete wavelet transform not only improves robustness but the selection of bands with high tolerance towards noise caused an improvement in terms of imperceptibility. The robustness further improved due to the involvement of singular vectors along with singular values in watermark embedding and extraction process. Finally, to achieve security, the selected DWT band is decomposed into smaller blocks and random blocks are chosen for modification. Furthermore, the elements of left and right singular vectors of selected blocks are chosen based on their dependence upon each other for watermark embedding. Various experiments using different images as host and watermark were conducted to examine and validate the proposed technique. Additionally, the proposed technique is tested against various attacks like compression, affine transformation, cropping, translation, X shearing, scaling, Y shearing, filtering, blurring, different kinds of noises, histogram equalization, rotation, etc. Lastly, the proposed technique is compared with state-of-the-art watermarking techniques and their comparison shows significant improvement of proposed scheme over existing techniques.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.