• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.77-83
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• 2012

In this paper, a low-complexity embedded compression (EC) Codec algorithm for the wavelet video coder is applied to reduce excessive external memory requirements. The EC algorithm is used to achieve a fixed compression ratio of 50 % under the near-lossless-compression constraint. The EC technique can reduce the 50 % memory requirement for intermediate low-frequency coefficients during multiple discrete wavelet transform stages compared with direct implementation of the wavelet video encoder of this paper. Furthermore, the EC scheme based on a forward adaptive quantization and fixed length coding can save bandwidth and size of buffer between DWT and SPIHT to 50 %. Simulation results show that our EC algorithm present only PSNR degradation of 0.179 and 0.162 dB in average when the target bit-rate of the video coder are 1 and 0.5 bpp, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.52-61
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• 2006

Orthogonal wavelet tansform which is generally used in image and signal processing applications has limited performance because of lack of shift invariance and low directional selectivity. To overcome these demerits complex wavelet transform has been proposed. In this paper, we present an efficient image denoising method using dual-tree complex wavelet transform and Bernoulli-Gauss prior model. In estimating hyper-parameters for Bernoulli-Gaussian model, we present two simple and non-iterative methods. We use hypothesis-testing technique in order to estimate the mixing parameter, Bernoulli random variable. Based on the estimated mixing parameter, variance for clean signal is obtained by using maximum generalized marginal likelihood (MGML) estimator. We simulate our denoising method using dual-tree complex wavelet and compare our algorithm to well blown denoising schemes. Experimental results show that the proposed method can generate good denoising results for high frequency image with low computational cost.

• The Journal of Korean Association of Computer Education
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• v.5 no.4
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• pp.43-52
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• 2002

A theory of binary wavelets which are performed over binary field has been recently proposed. Binary wavelet transform (BWT) of binary images can be used as an alternative to the real-valued wavelet transform of binary images in image processing applications such as compression, edge detection, and recognition. The BWT, however, requires large amount of computations for binary wavelet reconstruction since its operation is accomplished by matrix multiplication. In this paper, an efficient binary wavelet reconstruction method which utilizes filtering operation instead of matrix multiplication is presented. Experimental results show that the proposed algorithm can significantly reduce the computational complexity of the BWT. For the reconstruction of an $N{\times}N$ image, the proposed technique requires only $2MN^2$ multiplications and $2N(M-1)^2$ additions when the filter length M, while the BWT needs $2N^3$ multiplications and $2N(N-1)^2$ additions.

• Journal of KIISE
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• v.43 no.8
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• pp.835-840
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• 2016

Monte Carlo ray tracing has been widely used for simulating a diverse set of photo-realistic effects. However, this technique typically produces noise when insufficient numbers of samples are used. As the number of samples allocated per pixel is increased, the rendered images converge. However, this approach of generating sufficient numbers of samples, requires prohibitive rendering time. To solve this problem, image filtering can be applied to rendered images, by filtering the noisy image rendered using low sample counts and acquiring smoothed images, instead of naively generating additional rays. In this paper, we proposed a Stein's Unbiased Risk Estimator (SURE) based $\grave{A}$-Trous wavelet to filter the noise in rendered images in a near-interactive rate. Based on SURE, we can estimate filtering errors associated with $\grave{A}$-Trous wavelet, and identify wavelet coefficients reducing filtering errors. Our approach showed improvement, up to 6:1, over the original $\grave{A}$-Trous filter on various regions in the image, while maintaining a minor computational overhead. We have integrated our propsed filtering method with the recent interactive ray tracing system, Embree, and demonstrated its benefits.

• Smart Structures and Systems
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• v.5 no.2
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.2
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• pp.122-129
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• 2014

Identification of the whipping response out of the combined wave-vibration response of a flexible sea going vessel is one of the most interesting research topic from ship designer's point of view. In order to achieve this goal, a novel methodology based on the wavelet cross-correlation technique was proposed in this paper. The cross-correlation of the wavelet power spectrum averaged across the frequency axis was introduced to check the similarity between the combined wave-vibration response and impulse response. The calculated cross-correlation of the wavelet power spectrum was normalized by the auto-correlation of the each spectrum with zero time lag, eventually providing the cross-correlation coefficient that stays between 0 and 1, precisely indicating the existence of the impulse response buried in the combined wave-vibration response. Additionally, the weight function was introduced while calculating the cross-correlation of the two spectrums in order to filter out the signal of lower frequency so that the accuracy of the similarity check becomes as high as possible. The validity of the proposed methodology was checked through the application to the artificially generated ideal combined wave-vibration signal, together with the more realistic signal obtained by running 3D hydroelasticity program WISH-Flex. The correspondence of the identified whipping instances between the results, one from the proposed method and the other from the calculated slamming modal force, was excellent.

• 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 Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1222-1230
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• 2022

As the number of signal sources with low detectability by using various modulation techniques increases, research to classify signal modulation methods is steadily progressing. Recently, a Convolutional Neural Network (CNN) deep learning technique using FFT as a preprocessing process has been proposed to improve the performance of received signal classification in signal interference or noise environments. However, due to the characteristics of the FFT in which the window is fixed, it is not possible to accurately classify the change over time of the detection signal. Therefore, in this paper, we propose a CNN model that has high resolution in the time domain and frequency domain and uses wavelet transform as a preprocessing process that can express various types of signals simultaneously in time and frequency domains. It has been demonstrated that the proposed wavelet transform method through simulation shows superior performance regardless of the SNR change in terms of accuracy and learning speed compared to the FFT transform method, and shows a greater difference, especially when the SNR is low.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.223-226
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• 2001

In this paper, we propose a robust and fragile watermarking technique for tamper proofing of still images. Robust watermarks are embedded by quantization with a robust quantization step-size, and it is imperceptible value for human visual system. Fragile watermarks are embedded by thresholding and quantization with EW(Embedded Zerotree Wavelet) algorithm. The proposed method enables us to distinguish malicious change from non-malicious change. Futhermore this technique enables us to find tampering regions and degrees.