• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.943-949
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• 2009

In this paper, the 5.9GHz WAVE(Wireless Access in Vehicular Environments) channel modeling is used by the Jakes channel model for the suitability of the fast wireless channel fluctuation. The performance analysed the fading signal constellation and the spectrum in the IEEE 802.11p spectrum mask, the Doppler effect, the modulation scheme. In addition, the vehicular speed, exactly the performance analysis the WAVE communication systems follow the Doppler effect.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.459-466
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• 2014

This paper deals with the method to classify the aircraft target by analyzing its JEM signal. We propose the method to classify the engine model by analyzing JEM spectrum using the harmonic frequency mask generated from the blade information of jet engine. The proposed method does not need the complicated logic algorithm to find the chopping frequency in each rotor stage and the pre-simulated engine spectrum DB used in the previous methods. In addition, we propose the method to estimate the precise spool rate and it reduces the error in estimating the number of blades or in calculating the harmonic frequency of frequency mask.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.339-346
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• 2000

Medical image is analyzed to get an anatomical information for diagnostics. Segmentation must be preceded to recognize and determine the lesion more accurately. In this paper, we propose automatic segmentation algorithm for MR brain images using T1-weighted, T2-weighted and PD images complementarily. The proposed segmentation algorithm is first, extracts cerebrum images from 3 input images using cerebrum mask which is made from PD image. And next, find 3D clusters corresponded to cerebrum tissues using scale filtering and 3D clustering in 3D space which is consisted of T1, T2, and PD axis. Cerebrum images are segmented using FCM algorithm with its initial centroid as the 3D cluster's centroid. The proposed algorithm improved segmentation results using accurate cluster centroid as initial value of FCM algorithm and also can get better segmentation results using multi spectral analysis than single spectral analysis.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.468-474
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• 2010

The major factor that disturbs practical use of speech recognition is distortion by the ambient and channel noises. Generally, the ambient noise drops the performance and restricts places to use. DSR (Distributed Speech Recognition) based speech recognition also has this problem. Various noise cancelling algorithms are applied to solve this problem, but loss of spectrum and remaining noise by incorrect noise estimation at low SNR environments cause drop of recognition rate. This paper proposes methods for speech enhancement. This method uses MMSE-STSA for noise cancelling and ideal binary mask to compensate damaged spectrum. According to experiments at noisy environment (SNR 15 dB ~ 0 dB), the proposed methods showed better spectral results and recognition performance.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.132-135
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• 2003

핸드폰에 장착된 반투과형 LCD (Liquid Crystal Display) 패널에서 반사판을 제거하면 투과형 LCD 마스크로 이용할 수 있다. LCD 패널의 광 흡수 실험에서 얻은 스펙트럼을 참고하여 다양한 파장대의 광원으로 리소그래피 하였다. 컴퓨터 이미지 프로그램으로 편집한 그림을 핸드폰 전용 통신 케이블을 통하여 LCD 패널로 전송하여, 다양한 모양의 패턴을 기판위에 전사하는데 성공하였다. 픽셀간의 경계가 현상되어 끊어지는 패턴이 형성되는 LCD 마스크의 단점을 극복하여 연속적인 패턴결과를 얻는데도 성공하였다. 이로부터 프로젝션 리소그래피의 응용에 쉽게 접근할 수 있는 발판이 마련된 것으로 생각된다.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.141-147
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• 2010

In this paper, we propose a new speech separation algorithm to extract and enhance the target speech signals from mixed speech signals by utilizing both magnitude and phase information. Since the previous statistical modeling algorithms assume that the log power spectrum values of the mixed speech signals are independent in the temporal and frequency domain, discontinuities occur in the resultant separated speech signals. To reduce the discontinuities, we apply a smoothing filter in the time-frequency domain. To further improve speech separation performance, we propose a statistical model based on both magnitude and phase information of speech signals. Experimental results show that the proposed algorithm improve signal-to-interference ratio (SIR) by 1.5 dB compared with the previous magnitude-only algorithms.

• The Journal of the Acoustical Society of Korea
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• v.41 no.1
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• pp.38-44
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• 2022

Speech enhancement is performed to improve intelligibility and quality of the noise-corrupted speech. In this paper, speech enhancement performance was compared using different loss functions in time and frequency domains. This study proposes a combination of loss functions to utilize advantage of each domain by considering both the details of spectrum and the speech waveform. In our study, Scale Invariant-Source to Noise Ratio (SI-SNR) is used for the time domain loss function, and Mean Squared Error (MSE) is used for the frequency domain, which is calculated over the complex-valued spectrum and magnitude spectrum. The phase loss is obtained using the sin function. Speech enhancement result is evaluated using Source-to-Distortion Ratio (SDR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI). In order to confirm the result of speech enhancement, resulting spectrograms are also compared. The experimental results over the TIMIT database show the highest performance when using combination of SI-SNR and magnitude loss functions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.72-82
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• 1999

The error diffusion algorithm is excellent for reproducing continuous gray-scale images to bianry images and also has good edge characteristics. However, it is well known that artifacts with objectionable patterns can occur in the halftoned images. On the other hand, a halftone algorithm using blue noise mask has been proposed. where the halftoning is achieved by a pixelwise comparison of gray-scale image with an array, the blue noise mask. It doesn't have pattern artifacts, but the halftoned image looks unclear because the quantization errors are not feedbacked compared to the error diffusion. In this paper, edge enhanced error diffusion which dithers the threshold with the blue noise mask is proposed. We show that the proposed algorithm can produce unstructured and edge enhanced halftone images. This algorithm is analyzed by the concept of an equivalent input image. The performace of the proposed algorithm is compared with that of the conventional halftoning by measuring the radially averaged power spectrum and edge correlation.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.235-243
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• 2002

Theoretical analysis have been performed to design the high power semiconductor laser for an optical sensor at 1.55${\mu}{\textrm}{m}$ wavelength range which is the lowest loss wavelength in optical fiber. The materials of active region and SCH were $Ln_{1-x}Ga_xAs_yP_{1-y}$. In order to use the light source of optical sensors, it has to satisfy wide spectral width and short coherence length. Therefore, in order to suppress lasing oscillation, we proposed laterally tilted PBH type with a window region. Also, tapered stripe structure was applied for high coupling efficiency into a single mode fiber. From these analyses, the devices of laterally tilted angled and bending structure were fabricated and their characteristics were measured. In the results of the measurement, the fabricated devices have sufficient output power and wide FWHM to apply to the light source of optical fiber sensors.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.