• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.351-355
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• 1999

The amount of data stored, processed and transmitted in the multi-media systems has been growing very fast, especially for the image data. For example, it takes 0.75Mbytes to store 512 12 pixels of 24-bit color image. A video signal with 30 frames per second will require 22.5Mbytes of storage space. To solve this problem, we need a good image compression technique. Recently, many researches on the image compression technique based on the wavelet transform are being pursued to overcome the problems of traditional JPEG. This paper describes the architecture and design of two-dimensional wavelet transform circuit. To keep the sire of the circuit small, we tried to minimize the internal storage space by using external SDRAM. This circuit was designed in Verilog-HDL, synthesized using Design Compiler and verified using Verilog-XL.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.135-137
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• 2006

To improve device yield and throughput, faults in plasma processing equipment should be quickly and accurately diagnosed. Despite many useful information of ex-situ sensor measurements, their applications to recognize plasma faultshave not been investigated. In this study, a new technique to identify fault causes by recognizing X-ray photoelectron spectroscopy (XPS) using neural network and continuous wavelet transformation (CWT). The presented technique was evaluated with the plasma etch data. A totalof 17 experiments were conducted for model construction. Model performance was investigated from the perspectives of training error, testing error, and recognition accuracy with respect to various thresholds. CWT-based BPNN models demonstrated a higher prediction accuracy of about 26%. Their advantages over pure XPS-based models were conspicuous in all three measures at small networks.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.132-134
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• 2006

To improve equipment throughput and device yield, a malfunction in plasma equipment should be accurately diagnosed. A recognition model for plasma diagnosis was constructed by applying neural network to scanning electron microscope (SEM) image of plasma-etched patterns. The experimental data were collected from a plasma etching of tungsten thin films. Faults in plasma were generated by simulating a variation in process parameters. Feature vectors were obtained by applying direct and wavelet techniques to SEM Images. The wavelet techniques generated three feature vectors composed of detailed components. The diagnosis models constructed were evaluated in terms of the recognition accuracy. The direct technique yielded much smaller recognition accuracy with respect to the wavelet technique. The improvement was about 82%. This demonstrates that the direct method is more effective in constructing a neural network model of SEM profile information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.914-916
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• 2016

The noise in a power signal degrades the detection rate of the power quality (PQ) event signals. We present a new wavelet de-noising technique for PQ event detection that employs the correlation-based thresholding instead of the wavelet-scale-based thresholding of existing schemes. The simulation results show that the proposed scheme is more robust to Gaussian and impulsive noisy conditions and has further improved detection ratio than existing schemes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.5-9
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• 2001

In this paper, we propose the new audio watermarking method and can be used on line processing. Instead of the wavelet transform, we use the integer wavelet transform for the reduction of the computational load. The watermark associated with the chi-square distribution is inserted into the signal on the integer wavelet domain. When extracting the watermark, the spread spectrum methods are used with the coefficients associated with the covariance sequence. We show that the chi-square distribution is a good tool for the spread spectrum method on the wavelet domain. This watermarking technique may be used for the control of the electrical product which can be controlled with the hidden signals and can be moved according to the audible signals simultaneously.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.552-558
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• 2009

We proposed a semi-blind digital image watermarking technique for copyright protection. The proposed algorithm embedded a binary sequence watermark into significant wavelet coefficients by using a quantization method. The main idea of the quantization method was to quantize a middle coefficient of the triplet of a significant wavelet coefficient according to the watermark's value. Unlike an existing algorithm, which used a random location table to find a coefficient in which the watermark bit will be embedded: the proposed algorithm used quad-tree decomposition to find a significant wavelet coefficient for embedding. For watermark detection, an original host image was not required. Thanks to the usage of significant wavelet coefficients, the proposed algorithm improved the correlation value, up to 0.43, in comparison with the existing algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.391-394
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• 1997

In this paper, we considered newly the use of wavelet transform in order to improve the troubles of the established methods for the analysis of ultrasound echo signals. We made the phantoms of 13.2g, 19.8g, 26.4g, 33.0g, 39.8g by ourselves, and extracted the only pulse-echo signals that reflected through the mediums using windowing technique. For determining the characterized value, the signals were wavelet transformed, absoluted, and integral calculated. As the result, we acquired characterized value of each signals, and acknowledged the differences among them except of some datas. But this will be improved by advanced work as sellecting a proper mother wavelet, a method of making phantoms, correcting the various errors, etc. We expect that wavelet transform is powerful for analysis of ultrasound signals.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• The Journal of Information Technology
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• v.2 no.2
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• pp.127-138
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• 1999

The wavelet transform is widely used in signal processing application. In this paper, a wavelet domain adaptive algorithm(WPTNLMS) is derived and its performances are evaluated in non-stationary environment. Where the input signals are decomposed by the wavelet packet transform for the multi-resolution adaptive processing. And the NLMS is used as an adaptive algorithm in wavelet domain. The proposed technique is applied to noise cancellation of the Doppler signal which is added with white Gaussian noise.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2307-2310
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• 2003

In this paper, a method for face recognition based on the wavelet packet decomposition is proposed. In the proposed method, the input image is decomposed by the 2-level wavelet packet transformation and then the face areas are defined by the Integral Projection technique applied to each of the 1-level subband images, HL and LH. After the defined face areas are divided into three areas, called top, bottom, and border, the mean and the variance of the three areas of the approximation image are computed, and the variance of the single predetermined face area for the rest of 15 detail images, from which the feature vectors of statistical measure are extracted. In this paper we use the wavelet packet decomposition, a generalization of the classical wavelet decomposition, to obtain its richer signal analysis features such as discontinuity in higher derivatives, self-similarity, etc. And we have shown that even with very simple statistical features such as mean values and variance we can make an excellent basis for face classification, if an appropriate probability distance is used.