• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.9
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• pp.863-875
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• 2010

The vibration of a cylindrical shell is generated due to elastic waves propagating on the shell. Those elastic waves include propagating waves such as flexural, longitudinal and shear waves. Those also include non-propagating decaying waves, i.e. evanescent waves. In order to separate contributions of each type of waves to the data for the vibration of the cylindrical shell, spatial signal processing techniques for wavenumber analysis are investigated in this paper. Those techniques include Fast Fourier transform(FFT) algorithm, Extended Prony method and Overdetermined Modified Extended Prony method(OMEP). Those techniques have been applied to identify the waves from simulated vibration signals with various signal-to-noise ratios. Futhermore, the experimental data for in-plane vibration of the cylindrical shell has been processed with those techniques to identify propagating waves(longitudinal, shear and flexural waves) and evanescent waves.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.754-764
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• 2016

The exhaustive list of sparsification methods for a digital image suffers from achieving an adequate number of zero and near-zero coefficients. The method proposed in this paper, which is known as the Discrete Rajan Transform Sparsification, overcomes this inadequacy. An attempt has been made to compare the simulation results for benchmark images by various popular, existing techniques and analyzing from different aspects. With the help of Discrete Rajan Transform algorithm, both lossless and lossy sparse representations are obtained. We divided an image into $8{\times}8-sized$ blocks and applied the Discrete Rajan Transform algorithm to it to get a more sparsified spectrum. The image was reconstructed from the transformed output of the Discrete Rajan Transform algorithm with an acceptable peak signal-to-noise ratio. The performance of the Discrete Rajan Transform in providing sparsity was compared with the results provided by the Discrete Fourier Transform, Discrete Cosine Transform, and the Discrete Wavelet Transform by means of the Degree of Sparsity. The simulation results proved that the Discrete Rajan Transform provides better sparsification when compared to other methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.231-238
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• 2004

This paper presents the high speed noise reduction processing system using the modified discrete fourier transform(MDFT) on the frequency domain. The proposed filter uses the linear prediction coefficients of the adaptive line enhance(ALE) method based on the Sign algorithm The signals with a random noise tracking performance are examined through computer simulations. It is confirmed that the fast adaptive digital filter is realized by the high speed adaptive noise reduction(HANR) algorithm with rapid convergence on the frequency domain(FD).

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1878-1888
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• 1990

In this paper, the input-output interconnection method of the multi-valued signal processing circuit using perfect Shuffle technique and Kronecker product is discussed. Using this method, the design method of circuit of the multi-valued Reed-Muller expansions(MRME) to be used the multi-valued signal processing on finite field GF(p**m) is presented. The proposed input-output interconnection method is shown that the matrix transform is efficient and that the module structure is easy. The circuit design of MRME on FG(p**m) is realized following as` 1) contructing the baisc gates on GF(3) by CMOS T gate, 2) designing the basic cells to be implemented the transform and inverse transform matrix of MRME using these basic gates, 3) interconnecting these cells by the input-output interconnecting method of the multivalued signal processing circuits. Also, the circuit design of the multi-valued signal processing function on GF(3\ulcorner similar to Winograd algorithm of 3x3 array of DFT (discrete fourier transform) is realized by interconnection of Perfect Shuffle technique and Kronecker product. The presented multi-valued signal processing circuits that are simple and regular for wire routing and posses the properties of concurrency and modularity are suitable for VLSI.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.498-503
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• 2009

As for Hadamard Transform, because the calculation time of this transform is slower than Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT), the effectiveness and the practicality are insufficient. Then, the computational complexity can be decreased by using the butterfly operation as well as FFT. We composed calculation time of FFT with that of Fast Complex Hadamard Transform by constructing the algorithm of Fast Complex Hadamard Transform. They are indirect conversions using program of complex number calculation, and immediate calculations. We compared calculation time of them with that of FFT. As a result, the reducing the calculation time of the Complex Hadamard Transform is achieved. As for the computational complexity and calculation time, the result that quadrinomial Fast Complex Hadamard Transform that don't use program of complex number calculation decrease more than FFT was obtained.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.10
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• pp.152-159
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• 1996

We have designed and implemented an optoelectronic hardware of binary phase extraction joint transform correlator (BPEJTC) which provides higher peak-to-sidelobe ratio than many other versions of JTC that has been published so far and does not produce correlation peaks due to intra-class association, to construct a multi-target tracking system. The digital processing unit controlling the entire system plays the part of modifying and binarizing the joint transform power spectrum (JTPS) and the optical processing unit is mainly used to take fourier transform operations. Some experimental results conducted by designed system along with its architecture showed the processing rate of 6 frames per second, thereby the potential applicability of the proposed system to real-time multitarget tracking system is given.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.39-44
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• 2013

In this paper digital phase-shifter for multi-arm spiral antennas was designed by using Hilbert transform. All frequency components in input signal are phase-shifted for 90 degree by Hilbert transform, and the transform is implemented by FIT and IFIT. Digital phase-shifter generates two signals with phase difference of 90 degree by using Hilbert transform from input signals sampled by analog-digital converter(ADC), and then the input signal is phase-shifted for a given phase by using two signals. Hilbert transform based on digital phase-shifter is designed by Xilinx System generator, and the effects of input noise, FIT point, sampling period, initial phase of input signal, and shifted phase are simulated and its results are compared with Matlab results.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.43-52
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• 2017

Pattern Analysis is One of the Most Important Techniques in the Signal and Image Processing and Text Mining Fields. Discrete Fourier Transform (DFT) is Generally Used to Analyzing the Pattern of Signals and Images. We thought DFT could also be used on the Analysis of Text Patterns. In this Paper, DFT is Firstly Adapted in the World to the Sentence Plagiarism Detection Which Detects if Text Patterns of a Document Exist in Other Documents. We Signalize the Texts Converting Texts to ASCII Codes and Apply the Cross-Correlation Method to Detect the Simple Text Plagiarisms such as Cut-and-paste, term Relocations and etc. WordNet is using to find Similarities to Detect the Plagiarism that uses Synonyms, Translations, Summarizations and etc. The Data set, 2013 Corpus, Provided by PAN Which is the One of Well-known Workshops for Text Plagiarism is used in our Experiments. Our Method are Fourth Ranked Among the Eleven most Outstanding Plagiarism Detection Methods.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.178-184
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• 2007

The EEG signal in general can be categorized as the Alpha wave, the Beta wave, the Theta wave, and the Delta wave. The alpha wave, showed in stable state, is the dominant wave for a human EEG and the beta wave displays the excited state. The subject of this paper was to recognize the stable state of EEG quantitatively using wavelet transform and power spectrum analysis. We decomposed EEG signal into the alpha wave and the beta wave in the process of wavelet transform, and calculated each power spectrum of EEG signal, using Fast Fourier Transform. And then we calculated the stable state quantitatively by stable state ratio, defined as the power spectrum of the alpha wave over that of the beta wave. The study showed that it took more than 10 minutes to reach the stable state from the normal activity in 69 % of the subjects, 5 -10 minutes in 9%, and less than 5 minutes in 16 %.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.391-399
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• 2018

In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.