• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.179-182
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• 2001

This paper has been studied a wavelet based still image transmission over the wireless channel. EZW(Embedded Zerotree Wavelet) is an efficient and scalable wavelet based image coding technique, which provides progressive transfer of signal resulted in multi-resolution representation. It reduces therefore the reduce cost of storage media. Although EZW has many advantages, it is very sensitive on error. Because coding are performed in subband by subband, and it uses arithmetic coding which is a kind of variable length coding. Therefore only 1∼2bit error may degrade quality of the entire image. So study of error localization and recovery are required. This paper investigates the use of reversible variable length codes(RVLC) and data partitioning. RVLC are known to have a superior error recovery property due to their two-way decoding capability and data partitioning is essential to applying RVLC. In this work, we show that appropriate data partitioning length for each SNR(Signal-to-Noise Power Ratio) and error localization in wireless channel.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.46-56
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• 2016

Dynamic textures are videos that exhibit a stationary property with respect to time (i.e., they have patterns that repeat themselves over a large number of frames). These patterns can easily be tracked by a linear dynamic system. In this paper, a model that identifies the underlying linear dynamic system using wavelet coefficients, rather than a raw sequence, is proposed. Content based threshold filtering based on Set Partitioning in a Hierarchical Tree (SPIHT) helps to get another representation of the same frames that only have low frequency components. The main idea of this paper is to apply SPIHT based threshold filtering on different bands of wavelet transform so as to have more significant information in fewer parameters for singular value decomposition (SVD). In this case, more flexibility is given for the component selection, as SVD is independently applied to the different bands of frames of a dynamic texture. To minimize the time complexity, the proposed model is implemented on a graphics processing unit (GPU). Test results show that the proposed dynamic system, along with a discrete wavelet and SPIHT, achieve a highly compact model with better visual quality, than the available LDS, Fourier descriptor model, and higher-order SVD (HOSVD).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.4
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• pp.22-27
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• 2000

In this paper, new neural network called dynamic multidimensional wavelet neural network (DMWNN) is proposed. The resulting network from wavelet theory provides a unique and efficient representation of the given function. Also the proposed DMWNN have ability to store information for later use. Therefore it can represent dynamic mapping and decreases the dimension of the inputs needed for network. This feature of DMWNN can compensate for the weakness of diagonal recurrent neural network(DRNN) and feedforward wavelet neural network(FWNN). The efficacy of this type of network is demonstrated through experimental results.

• Geomechanics and Engineering
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• v.2 no.4
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• pp.229-252
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• 2010

In this paper, Soil-Structure Interaction (SSI) effect is investigated using a new and integrated approach. Faster solution of time dependant differential equation of motion is achieved using numerical representation of wavelet theory while dynamic Infinite Elements (IFE) concept is utilized to effectively model the unbounded soil domain. Combination of the wavelet theory with IFE concept lead to a robust, efficient and integrated technique for the solution of complex problems. A direct method for soil-structure interaction analysis in a two dimensional medium is also presented in time domain using the frequency dependent transformation matrix. This matrix which represents the far field region is constructed by assembling stiffness matrices of the frequency dependant infinite elements. It maps the problem into the time domain where the equations of motion are to be solved. Accuracy of results obtained in this study is compared to those obtained by other SSI analysis techniques. It is shown that the solution procedure discussed in this paper is reliable, efficient and less time consuming as compared to other existing concepts and procedures.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.9 no.5
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• pp.640-647
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• 1998

Microstrip patch antennas are analyzed by a multiresolution wavelet method. The spectral Green's dyad of the structure is obtained and its joint spatial-spectral domain representations are presented. Based on the joint spatial-spectral domain representation, we show that the spectral-domain wavelets are useful in the analysis of this problem. We obtain the matrix equations of the integral equations of this Green's dyad by using the method of moment(MoM), and efficiently solve the problem using the spectral domain wavelet transform concepts in conjuction with the conjugate gradient method. The results for a single-layered square patch are compared with those of conventional MoM and CG-FFT.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.42-45
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• 2003

This paper describes a new iris recognition method using shift-invariant subbands. First an iris image is preprocessed to compensate the variation of the iris image. Then, the preprocessed iris image is decomposed into multiple subbands using a shift invariant wavelet transform. The best subband among them, which have rich information for various iris pattern and robust to noises, is selected for iris recognition. The quantized pixels of the best subband yield the feature representation. Experimentally, we show that the proposed method produced superb performance in iris recognition.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.126-132
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• 2000

This paper presents a facial expression recognition based on Gabor wavelets that uses a fuzzy C-means(FCM) clustering algorithm and neural network. Features of facial expressions are extracted to two steps. In the first step, Gabor wavelet representation can provide edges extraction of major face components using the average value of the image's 2-D Gabor wavelet coefficient histogram. In the next step, we extract sparse features of facial expressions from the extracted edge information using FCM clustering algorithm. The result of facial expression recognition is compared with dimensional values of internal stated derived from semantic ratings of words related to emotion. The dimensional model can recognize not only six facial expressions related to Ekman's basic emotions, but also expressions of various internal states.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.459-462
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• 2000

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device operating. so it is well hon that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. In this paper, we prove that the nature of the high impedance faults is indeed a deterministic chaos, not a random motion Algorithms for estimating Lyapunov spectrum and the largest Lyapunov exponent are applied to various fault currents detections in order to evaluate the orbital instability peculiar to deterministic chaos dynamically, and fractal dimensions of fault currents which represent geometrical self-similarity are calculated. Wavelet transform analysis is applied the time-scale information to fault signal. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.856-858
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• 1999

The analysis of distribution line faults is essential to the proper protection of power system. A high impedance fault(HIF) dose not make enough current to cause conventional protective device. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional Protection system. This paper describes an algorithm using neural network for pattern recognition and detection of high impedance faults. Wavelet transform analysis gives the time-scale information. Time-scale representation of high impedance faults can detect easily and localize correctly the fault waveform.