• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.375-378
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• 2009

The Spectrum Sensing Technology is the core technology of the Cognitive Radio (CR) System that is one of the future wireless communication technologies. This is the technology that temporarily allocates the frequency bandwidth by scanning surrounding wireless environments to keep licensed terminals and search the unused frequency bandwidth. In this paper, we proposed the efficient Spectrum Sensing Method using the Short Time Fourier Transform (STFT). The Cosine and DVB-H signal with the 6MHz bandwidth is used as the Input Signal. And we confirm the Spectrum Sensing result using Modified Periodogram Method, Welch's Method for compared with Short Time Fourier Transform Algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.7
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• pp.656-665
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• 2015

In this paper, we improved the clarity and quality of the radar imaging by applying motion compensation for time-frequency transform in B-ISAR imaging. The proposed motion compensation algorithm using UWB radar is verified. B-ISAR algorithm procedure and time-frequency transform for improved motion compensation are provided for theoretical ground. The image was created by a UWB Radar B-ISAR imaging algorithm method. Also, creating a B-ISAR imaging algorithm for motion compensation of time-frequency transformation method was used. The B-ISAR Imaging algorithm is implemented using STFT(Short-Time Fourier Transform), GWT(Gabor Wavelet Transform), and WVD(Wigner-Ville Distribution) approaches. The performance of STFT is compared with the GWT and WVD algorithms. It is found that the WVD image shows more clarity and decreased spread phenomenon than other methods.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.10 s.113
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• pp.913-919
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• 2006

This paper presents a technique for motion compensation of ISAR(Inverse SAR) images for a moving target. If a simple fourier transform is employed to obtain ISAR image for a moving target, the image is usually blurred. These images blurring problem can be solved with the time-frequency transform. In this paper, motion compensation algorithms of ISAR image such as STFT(Short Time Fourier Transform), GWT(Gabor Wavelet Transform) are described. In order to show the performances of each algorithm, we use scattering wave of the ideal point scatterers and simulated MIG-25 to obtain motion compensated ISAR image, and display the resolution of STFT and GWT ISAR image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1199-1212
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• 2019

This paper proposes two wideband spectrum sensing approaches: (i) method A, the cumulative sum (CUSUM) algorithm with short-time Fourier transform, taking advantage of the time-frequency analysis for wideband spectrum. (ii)method B, the quickest spectrum sensing with short-time Fourier transform and compressed sensing, shortening the time of perception and improving the speed of spectrum access or exit. Moreover, method B can take advantage of the sparsity of wideband signals, sampling in the sub-Nyquist rate, and it is more suitable for wideband spectrum sensing. Simulation results show that method A significantly outperforms the single serial CUSUM detection for small SNRs, while method B is substantially better than the block detection based spectrum sensing in small probability of the false alarm.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2066-2072
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• 2008

The time reversal was investigated for direct root between PZT and PZT, but in case of a circular PZT, lamb wave moves not only along the direct root but also another roots. The center frequency of lamb wave is kept when the lamb waves are reflected from damage. This paper presents experimental and theoretical results for the new structural health monitoring method by above features of lamb wave, and we can increase accuracy of the new structural health monitoring method by using STFT(Short Time Fourier Transform).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.428-433
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• 2002

There have been many studies to visualize the vibration and noise of rotary compressor. Most of these studies assumed that the signal is stationary and the time-averaged signal is used for visualization. However, the noise and vibration signals generated during one cycle of crank shaft vary continuously. In this paper, the noise and vibration of rotary compressor which vary continuously are visualized by short time fourier transform method. The location of source and the transfer path of vibration and noise at arbitrary frequencies, which can not be visualized by averaged signal, can be visualized clearly.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.256-261
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• 2018

There are three types of noise generated inside the vehicle: BSR (Buzz, Squeak, Rattle). In this paper, we propose a classifier that automatically classifies automotive BSR noise by using features extracted from deep convolutional neural networks. In the preprocessing process, the features of above three noises are represented as noise-map using STFT (Short-time Fourier Transform) algorithm. In order to cope with the problem that the position of the actual noise is unknown in the part of the generated noise map, the noise map is divided using the sliding window method. In this paper, internal parameter of the deep convolutional neural networks is visualized using the t-SNE (t-Stochastic Neighbor Embedding) algorithm, and the misclassified data is analyzed in a qualitative way. In order to analyze the classified data, the similarity of the noise type was quantified by SSIM (Structural Similarity Index) value, and it was found that the retractor tremble sound is most similar to the normal travel sound. The classifier of the proposed method compared with other classifiers of machine learning method recorded the highest classification accuracy (99.15 %).

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.79-84
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• 2004

We use principal component analysis (PCA) to identify exons of a gene and further analyze their internal structures. The PCA is conducted on the short-time Fourier transform (STFT) based on the 64 codon sequences and the 4 nucleotide sequences. By comparing to independent component analysis (ICA), we can differentiate between the exon and intron regions, and how they are correlated in terms of the square magnitudes of STFTs. The experiment is done on the gene F56F11.4 in the chromosome III of C. elegans. For this data, the nucleotide based PCA identifies the exon and intron regions clearly. The codon based PCA reveals a weak internal structure in some exon regions, but not the others. The result of ICA shows that the nucleotides thymine (T) and guanine (G) have almost all the information of the exon and intron regions for this data. We hypothesize the existence of complex exon structures that deserve more detailed analysis.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.555-561
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• 2012

The Hilbert Huang Transform (HHT) technigue with Empirical Mode Decomposition (EMD) is one of the time-frequency domain analysis methods and it has several advantages such that analyzing non-stationary and nonlinear signal is possible. However, there are shortcomings in detecting near-range of frequencies and added noise signals. In this paper, to analyze characteristics of each method, HHT and Short-Time Fourier Transform (STFT) effective in dealing with stationary signals are compared. And with thermoacoustic instabilities signals from a Rijke tube test, HHT and the improved HHT with Ensemble Empirical Mode Decomposition (EEMD) are compared. The results show that the improved HHT is more appropriate than the original HHT due to the relative insensitivity to noise. Therefore it will result in more accurate analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.685-692
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• 2024

In this paper, we propose a new CNN model to improve the recognition performance of command voice signals. This method obtains a spectrogram image after performing a short-time Fourier transform (STFT) of the input signal and improves command recognition performance through supervised learning using a CNN model. After Fourier transforming the input signal for each short-time section, a spectrogram image is obtained and multi-classification learning is performed using a CNN deep learning model. This effectively classifies commands by converting the time domain voice signal to the frequency domain to express the characteristics well and performing deep learning training using the spectrogram image for the conversion parameters. To verify the performance of the speech recognition system proposed in this study, a simulation program using Tensorflow and Keras libraries was created and a simulation experiment was performed. As a result of the experiment, it was confirmed that an accuracy of 92.5% could be obtained using the proposed deep learning algorithm.