• Structural Engineering and Mechanics
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• v.64 no.6
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• pp.803-817
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• 2017

Structural health monitoring has increasingly been a focus within the civil engineering research community over the last few decades. With increasing application of sensor networks in large structures and infrastructure systems, effective use and development of robust algorithms to analyze large volumes of data and to extract the desired features has become a challenging problem. In this paper, we grasp some precautions and key points of the signal processing approach, wavelet, establish a relative reliable framework, and analyze three problems that require attention when applying wavelet based damage detection approach. The cases studies how to use optimal scales for extracting mode shapes and modal curvatures in a reinforced concrete beam and how to effectively identify damages using maximum curves of wavelet coefficient differences. Moreover, how to make a recognition based on the wavelet multi-resolution analysis, wavelet packet energy, and fuzzy sets is a meaningful topic that has been addressed in this work. The relative systematic work that compasses algorithms, structures and evaluation paves a way to a framework regarding effective structural health monitoring, orientation, decision and action.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.4
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• pp.198-205
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• 2002

In this paper, we proposed a wavelet image coder based on the portable embedded microprocessor. The proposed coder stores the bit level information of the wavelet coefficient in the 2D significance array. Using this information, the coder make the significance check for coefficient and bit level scanning at the same pass. The proposed method has the advantage that we can reduce the scan iteratively and the memory usage for the coding process. Experimental results show that the proposed method outperforms popular image coders such as JPEG, EZW and SPIHT in based on the portable embedded system environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.140-143
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• 2004

The mage enhancement of dental On image using wavelet coefficients is presented. The processing is that make gray image from On image by preprocessing, extract high frequency from detail coefficient after acquisition detail coefficient by wavelet transform and emphasize edge appling input image. Experimental results show that enhanced contrast of dental On image, improved mage quality.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.253-256
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• 2000

In this paper, we propose the lossless image compression algorithm using the integer wavelet transform. Recently, the S＋P transform is widely used and computed with only integer addition and bit-shift operations, but not proper to remove the correlation of smooth images. then we compare the Harr wavelet of the S＋P transform with various integer coefficient filter banks and apply 9/7 ICFB to the wavelet transform. In addition, we propose a entropy-coding method that exploits the multiresolution structure and the feedback of the prediction error, and can efficiently compress the transformed image for progressive transmission. Simulation results are compared to the compression ratio using the S+P transform with different types of images.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.435-442
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• 1999

A fuzzy-ART(adaptive resonance theory) network for the PVC(premature ventricular contraction) classification using wavelet coefficient is designed. This network consists of the feature extraction and learning of the fuzzy-ART network. In the first step, we have detected the QRS from the ECG signal in order to set the threshold range for feature extraction and the detected QRS was divided into several frequency bands by wavelet transformation using Haar wavelet. Among the low-frequency bands, only the 6th coefficient(D6) are selected as the input feature. After that, the fuzzy-ART network for classification of the PVC is learned by using input feature which comprises of binary data converted by applying threshold to D6. The MIT/BIH database including the PVC is used for the evaluation. The designed fuzzy-ART network showed the PVC classification ratio of 96.52%.

• ETRI Journal
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• v.42 no.2
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• pp.217-229
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• 2020

Long-term electroencephalography (EEG) monitoring is time-consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short-term window size. Therefore, our method can be utilized to interpret long-term EEG results and detect momentary seizure waveforms in diagnostic systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.31-37
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• 2004

Wavelet transform is a useful tool for analysis and process of image. This showed good performance in image compression and noise reduction. Wavelet coefficients can be effectively modeled by hidden Markov tree(HMT) model. However, in application of HMT model to image interpolation, training procedure is needed. Moreover, the parameters obtained from training procedure do not match input image well. In this paper, the structure of HMT is used for image interpolation, and the parameters of HMT are obtained from statistical characteristics across wavelet subbands without training procedure. In the proposed method, wavelet coefficient is modeled as Gaussian mixture model(GMM). In GMM, state transition probabilities are determined from statistical transition characteristic of coefficient across subbands, and the variance of each state is estimated using the property of exponential decay of wavelet coefficient. In simulation, the proposed method shows improvement of performance compared with conventional bicubic method and the method using HMT model with training.

• Progress in Medical Physics
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• v.10 no.3
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• pp.141-149
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• 1999

This research analyzed the HRV signals by using wavelet transform to observe the activities of autonomous nervous system in a sleep state. This research also restructured the HRV signals from electrocardiogram and by using coefficient which was obtained through wavelet transform, analyzed the signals by frequency bandwidth. Then compared the analyzed results with existing frequency analyzing method using AR model techniques. The suggested wavelet coefficient from power spectrum component in the study shows a similar tendency with the results from FFT or AR model technique. Therefore, it can be found that power spectrum analyzing method by wavelet coefficient is a useful as a tool for analyzing autonomous nervous system activities using HRV signals. Since the suggested method able to clearly depict the progression of change in time zone, which was once impossible with the existing methods, it is presumed that it will be useful in other physiological signals.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.4
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• pp.19-28
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• 2009

This paper is a study on method that the EZW algorithm is proposed effective compression technique of wavelet transformed image. The EZW algorithm is encoded by zerotree coding technique using self-similarity of wavelet coefficients. If the coefficient is larger than the threshold a POS coded, if the coefficients is smaller than minus the threshold a NEG is coded. If the coefficient is the root of a zerotree than a ZTR is coded and finally, if the coefficient is smaller then the threshold but it is not the root of a zerotree, than an IZ is coded. This process is repeated until all the wavelet coefficients have been encoded completely. This paper was compared to EZW algorithm and a widely available version of JPEG. As the results of compare, it is shown that the PSNR of the EZW algorithm is better than JPEG.

• Journal of the Korean Mathematical Society
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• v.36 no.6
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• pp.1075-1090
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• 1999

This paper deals with Littlewood-Paley type estimates of the Besov spaces {{{{ { B}`_{p,q } ^{$\alpha$ } }}}} on the d-dimensional unit cube for 0< p,q<$\infty$ by two certain classes. These classes are including biorthogonal wavelet systems or dual multiscale systems but not necessarily obtained as the dilates or translates of certain fixed functions. The main assumptions are local supports of both classes, sufficient smoothness for one class, and sufficiently many vanishing moments for the other class. With these estimates, we characterize the Besov spaces by coefficient norms of decompositions with respect to biorthogonal wavelet systems on the cube.