• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.3
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• pp.155-162
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• 2008

In this paper we proposed a video watermarking algorithm based on a three dimension discrete wavelet transform (3D DWT) and direct spread spectrum (DSS). In the proposed method, the information watermark is embedded into followed frames, after sync watermark is embedded into the first frame. Input frames are divided into sub frames which are located odd row and even row. The sub frames are arranged as 3D frames, and transformed into 3D wavelet domain. In this domain the watermark is embedded using DSS. Existing video watermarking using 3D DWT is non-blind method but, proposed algorithm uses blind method. The experimental results show that the proposed algorithm is robust against frame cropping, noise addition, compression, etc. acquiring BER of 10% or below and sustains level of 40dB or above on the average.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2315-2325
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• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.1-8
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• 2015

This paper proposes a study for accurate surface localization system using DWT(Discrete Wavelet Transform) and GPS/INS fusion algorithm. Because the propagation in the underwater is not passed by characteristics of the medium unlike the ground, the sonar system like DVL is used instead of GPS. But since these systems are installed on the seafloor and operated, a long time is required for installation and navigation systems are limited outside of the range area. And it is difficult to estimate position in a three-dimensional considering the depth in actual marine environment. In this paper, before the development of underwater localization system, precisely estimated position system is proposed in a two-dimensional by developing surface localization system using removing noise and disturbance with DWT and relatively inexpensive GPS and INS sensor.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.295-312
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• 2017

Digital video content is the most information-intensive and high-value content. Therefore, it is necessary to protect the intellectual property rights of these contents, and this paper also proposes a watermarking method of digital image for this purpose. The proposed method uses the frequency characteristics of 2-Dimensional Discrete Wavelet Transform (2D-DWT) for digital images and digital watermark on global data without using local or specific data of the image for watermark embedding. The method to insert digital watermark data uses a simple Quantization Index Modulation (QIM) and a multiple watermarking method that inserts the same watermark data in multiple. When extracting a watermark, multiple watermarks are extracted and the final watermark data is determined by a simple statistical method. This method is an empirical method for experimentally determining the parameters in the watermark embedding process. The proposed method performs experiments on various images against various attacks and shows the superiority of the proposed method by comparing the performance with the representative existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2146-2152
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• 2008

In In this paper, we proposed a new JPEG2000 codec using multiresolution-based rendering (MRBR) technique for video compression of 3-dimensional digital cinema. We introduced discrete wavelet transform (DWT) for stereoscopic image and stereo matching technique in the wavelet domain. The disparity was extracted using stereo matching and transmitted with the reference (left) image. Since the generated right image was degraded by the occlusion lesion, the residual image which is generated from difference between the original right image and the generated one was transmitted at the same tine. The disparity data was extracted using the dynamic programming method in the disparity domain. There is high correlation between the higher and lower subbands. Therefore we decreased the calculation amount and enhanced accuracy by restricting the search window and applying the disparity information generated from higher subband.

• Journal of the Korean Society of Radiology
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• v.2 no.2
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• pp.11-16
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• 2008

The most difficult of implementation PACS is large amount of data. Therefore, PACS needs mass storage, as well as rapid transmission time. Consequently, medical images needs compression when stored in PACS. WT(wavelet transform) was announced by Ingrid Daubechies and Stephane Mallat, WT was methods of signal analysis by a base functions set same as Fourie transform. This paper estimated an efficiency, that experimental medical images compressed by DWT. The result of estimated, we are knows effectiveness that display to remained signal in low frequency region after 4-level DWT form $512{\times}512{\times}2^8$ input images. Compression ratio of images by 4-level DWT was 1:16. It is a high compression ratio, the other side has a problem appears on staircase phenomenon.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.129-138
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• 2013

This paper is a study on Dual Tree Complex Wavelet Transform, which improved directional information for efficient image processing. Dual Tree Complex Wavelet Transform satisfies characteristics of shift invariance, and includes 6 directional information, which is more than previous Discrete Wavelet Transform. However, in images of buildings, there are many horizontal and vertical edge components. Therefore, all the high-frequency components of image are not expressed by 6 directional information subbands. This paper proposes 8-directional Complex Wavelet Transform with excellent high-frequency separation features by creating horizontal vertical($0^{\circ}$, $90^{\circ}$) subband besides 6 directional information subband of previous Dual Tree Complex Wavelet Transform. The proposed method can create and combine various directional information subbands according to features of image. Performance is evaluated by applying the method to noise removal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.464-472
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• 2012

In this paper, we propose a new super-resolution algorithm that uses successive frames by applying the block matching motion estimation algorithm. Usually, single frame super-resolution algorithms are based on probability or discrete wavelet transform (DWT) approach to extract high-frequency components of the input image, but only limited information is available for these algorithms. To solve this problem, various multiple-frame based super-resolution algorithms are proposed. The accuracy of registration between frames is a very important factor for the good performance of an algorithm. We therefore propose an algorithm using 6-Tap FIR filter to increase the accuracy of the image registration with sub-pixel unit. Proposed algorithm shows better performance than other conventional interpolation based algorithms such as nearest neighborhood, bi-linear and bi-cubic methods and results in about the same image quality as DWT based super-resolution algorithm.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.319-324
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• 2005

An integrated approach for prostate cancer detection using proteomic data is presented. Due to the high-dimensional feature of proteomic data, the discrete wavelet transform (DWT) is used in the first-stage for data reduction as well as noise removal. After the process of DWT, the dimensionality is reduced from 43,556 to 1,599. Thus, each sample of proteomic data can be represented by 1599 wavelet coefficients. In the second stage, a voting method is used to select a common set of wavelet coefficients for all samples together. This produces a 987-dimension subspace of wavelet coefficients. In the third stage, the Autoassociator algorithm reduces the dimensionality from 987 to 400. Finally, the artificial neural network (ANN) is applied on the 400-dimension space for prostate cancer detection. The integrated approach is examined on 9 categories of 2-class experiments, and also 3- and 4-class experiments. All of the experiments were run 10 times of ten-fold cross-validation (i. e. 10 partitions with 100 runs). For 9 categories of 2-class experiments, the average testing accuracies are between 81% and 96%, and the average testing accuracies of 3- and 4-way classifications are 85% and 84%, respectively. The integrated approach achieves exciting results for the early detection and diagnosis of prostate cancer.