• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.89-94
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• 2013

This paper presents color video flame detection algorithm based on wavelet transform to remove detection of flickering non-flame objects. Conventional flame detection algorithms consist of simple or mixed functions using colors, temporal and spatial characteristics. But those algorithms detect non-flame objects as flame regions sometimes. False alarm reasons are flame-like objects with regular flickering lights such as car signal lamps, alarm lights etc. The proposed algorithm is to reduce false detection which is occurred in periodic flickering lights. At first, It segments the candidate flame regions by using frame difference, flame colors. Then it distinguish flame regions and non flame regions including flickering car lights by analyzing wavelet coefficients. Computer simulation results showed that the proposed algorithm removes false detection due to the periodic flickering lamps by performing 97.9% of correct detection rate while false detection rate is 7.3%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.27-35
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• 2009

Differential Power Analysis (DPA) based on the statistical characteristics of collected signals has been known as an efficient attack for uncovering secret key of crypto-systems. However, the attack performance of this method is affected very much by the temporal misalignment and the noise of collected side channel signals. In this paper, we propose a new method based on wavelet analysis to surmount the temporal misalignment and the noise problem simultaneously in DPA. The performance of the proposed method is then evaluated while analyzing the power consumption signals of Micro-controller chips during a DES operation. The experimental results show that our proposed method based on wavelet analysis requires only 25% traces compared with those of the previous preprocessing methods to uncover the secret key.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1155-1166
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• 2016

In this paper, we propose a new embedded video compression scheme which uses three-dimensional rate-distortion optimization based block coder. After the proposed scheme removes temporal redundancy by applying the motion compensated temporal filtering(MCTF) on input video frames, two dimensional discrete wavelet transform is applied on video frames to remove spatial redundancy. The three-dimensional wavelet coefficients generated in this way are sorted according to their expected rate-distortion slope and encoded by using the three-dimensional block partition coding method. The proposed scheme also uses both the effective color video coding method which maintains embedded features, and the efficient bit-rate control method. Experimental results demonstrate that the proposed scheme not only produces embedded bit-streams, but also outperforms existing video compression schemes.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.36-39
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• 2003

본 논문에서는 비디오 컨텐츠 내에 소유자와 구매자 정보를 함께 포함하는 핑거프린팅 정보를 삽입하여 불법으로 배포된 핑거프린팅 컨텐츠로부터 배포자가 누구인지를 추적할 수 있는 기법을 보인다. 특히, 문헌[1]에서 제시된 시간축 웨이블릿 변환을 이용하여 핑거프린팅 정보가 삽입될 영역을 분리해 주고, 역 변환을 통해 전 영역의 비디오 프레임에 정보가 삽입되게 된다. 이로 인해 핑거프린팅된 컨텐츠의 상이성을 이용한 기존의 여러 공모공격에도 강인함을 보이고 있다. 또한, 비디오 컨텐츠의 특성상 MPEG2의 압축에도 불법 배포자를 추적할 수 있는 강인함을 보인다.

• Journal of the Korea Concrete Institute
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• v.22 no.6
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• pp.843-850
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• 2010

The dynamic modulus of elasticity of concrete can be determined nondestructively using impact echo test as prescribed in KS F 2437. The fundamental longitudinal frequency of the concrete cylinders with free-free boundary condition was estimated by the wavelet transform theory. The advantage of the wavelet transform over either a pure spectral or temporal decomposition of the signal is that the features of the pertinent signals can be characterized in the time-frequency plane. For the concrete mix design utilized in this study, no significant difference between the dynamic and the static moduli of elasticity was observed. This was contrary to the perceived general notion of having the dynamic modulus considerably higher than the static modulus. It has been shown that the modulus from static and dynamic by impact echo test are comparable to each other fairly well, when the effect of strain level was properly taken into account. In this experimental test, it was shown that the dynamic modulus is approximately equal to the tangent modulus at $1{\times}10^{-4}$ strain level.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• Journal of IKEEE
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• v.22 no.4
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• pp.1175-1179
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• 2018

In this paper, we propose recurrent CNN(Convolutional Neural Networks) for detecting seizures among patients using EEG signals. In the proposed method, data were mapped by image to preserve the spectral characteristics of the EEG signal and the position of the electrode. After the spectral preprocessing, we input it into CNN and extracted the spatial and temporal features without wavelet transform. Results from the Children's Hospital of Boston Massachusetts Institute of Technology (CHB-MIT) dataset showed a sensitivity of 90% and a false positive rate (FPR) of 0.85 per hour.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.355-363
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• 2007

The 3D DWT(The Three Dimensional Discrete Wavelet Transform) scheme is potentially regarded as useful one on analyzing both spatial and spectral information. Nevertheless, few researchers have attempted to process or classified remotely sensed images using the 3D DWT. This study aims to apply the 3D DWT to the land cover classification of optical and SAR(Synthetic Aperture Radar) images. Then, their results are evaluated quantitatively and compared with the results of traditional classification technique. As the experimental results, the 3D DWT shows superior classification results to conventional techniques, especially dealing with the high-resolution imagery and SAR imagery. It is thought that the 3D DWT scheme can be extended to multi-temporal or multi-sensor image classification.

• Journal of the Korea Concrete Institute
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• v.18 no.3 s.93
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• pp.347-354
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• 2006

In this paper we have attempted to bring the wavelet transform theory to the dynamic response conversion algorithm. This algorithm is proposed for the problem of estimating the displacement data by defining the transformed responses. In this algerian, the displacement response can be obtained from the measured acceleration records by integration without requiring the knowledge of the initial velocity and displacement information. The advantage of the wavelet transform over either a pure spectral or temporal decomposition of the signal is that the pertinent signals features can be characterized in the time-frequency plane. In the response conversion procedure using the wavelet decomposition signals, not only the static component can be extracted, but also the dynamic displacement component can be separated by the structural mode from the identified displacement response. The applicability of the technique is tested by an example problem using the real bridge's superstructure under several cases of moving load. If the reliability of the identified responses is ensured, it is expected that the proposed method for estimating the impact factor can be useful in the bridge's dynamic test. This method can be useful in those practical cases when the direct measurement of the displacement is difficult as in the dynamic studies of huge structure.

• Journal of IKEEE
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• v.24 no.4
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.