• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.271-275
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• 2011

In this paper, we present a hybrid encryption for a digital hologram which is the most valuable image content. The encryption algorithm is based on a hybrid technique implementation a four-dimensional transform combining the discrete wavelet transform(DWT) and the discrete cosine transform (DCT). The encryption scheme is composed on the basis of the energy distribution. The experimental results showed that encrypting only 0.0244% of the entire data was enough to hide the constants of the hologram. The encryption algorithm expected to be used effectively on the researches on encryption and others for digital holographic display.

• Proceedings of the KIEE Conference
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• summer
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• pp.202-204
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• 2004

This paper presents a neural network technology for the detection and classification of the various types of power quality disturbances. Power quality phenomena are short-time problems and of many varieties. Particularly, the transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting ·md classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. We test two neural network and compare the results of Backpropagation Neural (BPN) network with Radial basis function network (RBFN). RBFN is more useful to detect and classify than BPN. The configuration of the hardware of PQ-DAS and some case studies are described.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.993-1000
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• 2012

In this research, we proposed an efficient VLSI architecture of the lifting-based 2D DWT (Discrete Wavelet Transform) filter with 100% hardware utilization. The (9,7) filter structure has been applied and extendable to the filter length. We proposed a new block-based scheduling that computes the DWT for the lower levels on an "as-early-as-possible" basis, which means that the calculation for the lower level will start as soon as the data is ready. Since the proposed 2D DWT computes the outputs of all levels by one row-based scan, the intermediate results for other resolution levels should be kept in storage such as the Data Format Converter (DFC) and the Delay Control Unit (DCU) until they are used. When the size of input image is $N{\times}N$ and m is the filter length, the required storage for the proposed architecture is about 2mN. Since the proposed architecture processes the 2D DWT in horizontal and vertical directions at the same time with 4 input data, the total period for 2D DWT is $N^2(1-2^{-2J})/3$.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.10
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• pp.965-975
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• 2005

In the case of the fault in stator windings of a high voltage motor. it facilitates certain destructive characteristics in insulations. This will result in a decreased reliability in power supplies and will prevent the generation of electricity, which will result in huge economic losses. This study simulates motor windings using normal windings and four faulty windings for an actual fault in stator winding of a high voltage motor. The partial discharge signals produced in each faulty winding were measured using an 80 PF epoxy/mica coupler sensor. In order to quantified signal waves its a way of feature extraction for each faulty signal, the signal wave of winding was quantified to measure the degree of skewness shape and kurtosis, which are both types of statistical parameters, using a discrete wavelet transformation method for each faulty type. Wave types present different types lot each faulty type, and the skewness and kurtosis also present different quantified values. The result of feature extraction was used as a preprocessing stage to identify a certain fault in stater windings. It is evident that the type of faulty signals can be classified from the test results using faulty signals that were randomly selected from the signal, which was not applied in the training after the training and learning period, by applying it to a back-propagation algorithm due to the supervising and learning method in a neural network in order to classify the faulty type. This becomes an important basis for studying diagnosis methods using the classification of faulty signals with a feature extraction algorithm, which can diagnose the fault of stator windings in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.820-828
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• 2017

Service of digital hologram that has been recognized as a visual system for next generation requires various signal processing technologies. A transform is the most frequently used tool among signal processing techniques for 2-dimensional(D) and 3-D natural picture. A digital hologram has totally different property with a natural picture, so it is rarely efficient to apply transform tools used in 2-D image processing to a digital hologram. To overcome this a Fresnelet transform for a digital hologram has been proposed. We derive a Fresnelet transform by using the Daubechie's filter after applying an unitary Fresnel transform to a wavelet basis function. We also implement the transform as types of device and kernel code to improve operational performance. In consideration of the average time that is required for a pixel we can have observed the performance is improved up to 242 and 30 times for using the (9,7) and (5,3) filters in case of using device code.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.612-621
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• 2008

Due to the various constraints such as feasible size of data payload and low transmission power, no technical specifications on the voice communication are included in the Zigbee standard. In this paper, a voice coding technique for application to the IEEE 802.15.4 standard, which is the basis of Zigbee communication, is presented. Here, both high compression and good waveform recovery are essential. To meet those requirements, a multi-stage discrete wavelet transform (DWT) block and a binary coding block consisting of two different pulse-code modulations are exploited. Theoretical analysis and simulation results in an indoor wireless channel show that the voice coder with 2-stage DWT is most appropriate from the viewpoint of compression and waveform recovery. When the line-of-sight component is dominant, the voice coding scheme has good recovery capability even in the moderate signal-to-noise power ratios. Hence, it is considered that the presented scheme will be a technical reference for the future recommendation of voice communication exploiting Zigbee.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.16-24
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• 2002

Classification of the land cover characteristics is a major application of remote sensing. The goal of this study is to propose an optimal classification process for electro-optical camera(EOC) of Korea Multi-Purpose Satellite(KOMPSAT). The study was carried out on Landsat TM, high spectral resolution image and KOMPSAT EOC, high spatial resolution image of Miho river basin, Korea. The study was conducted in two stages: one was image fusion of TM and EOC to gain high spectral and spatial resolution image, the other was land cover classification on fused image. Four fusion techniques were applied and compared for its topographic interpretation such as IHS, HPF, CN and wavelet transform. The fused images were classified by radial basis function neural network(RBF-NN) and artificial neural network(ANN) classification model. The proposed RBF-NN was validated for the study area and the optimal model structure and parameter were respectively identified for different input band combinations. The results of the study propose an optimal classification process of KOMPSAT EOC to improve the thematic mapping and extraction of environmental information.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.111-118
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• 2004

Techniques based on thresholding of wavelet coefficients are gaining popularity for denoising data because of the reasonable performance at the low complexity. The VisuShrink which removes the noise with the universal threshold is one of the techniques. The universal threshold is proportional to the noise deviation and the number of data samples. In general, because the noise deviation is not known, one needs to estimate the deviation for determining the value of the universal threshold. But, only for the finest scale wavelet coefficients, it has been known the way of estimating the noise deviation, so the noise in coarse scales cannot be removed with the VisuShrink. We propose here a new denoising method which removes the noise in each scale except the coarsest scale by Visushrink method. The noise deviation at each band is estimated by the monotonic transform and weighted deviation, the product of estimated noise deviation by the weight, is applied to the universal threshold. By making use of the universal threshold and the Soft-Threshold technique, the noise in each band is removed. The denoising characteristics of the proposed method is compared with that of the traditional VisuShrink and SureShrink method. The result showed that the proposed method is effective in denoising on Gaussian noise and quantization noise.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.636-639
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• 2004

In present, there are many methods such as DCT, Wavelet Transform, or Quantization -to the image compression field, but the basic image compression method have based on DCT. The representative thing of the efficient techniques for information compression is DCT method. It is more superior than other information conversion method. It is widely applied in digital signal processing field and MPEG and JPEG which are selected as basis algorithm for an image compression by the international standardization group. It is general that DCT is consisted of using multiplier with main arithmetic blocks having many arithmetic amounts. But, the use of multiplier requires many areas when hardware is embodied, and there is fault that the processing speed is low. In this paper, we designed the hardware module that could run high-speed operation using row-column separation calculation method and Chen algorithm by distributed arithmetic method using ROM table instead of multiplier for design DCT module of high speed.