• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.361-373
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• 2002

In this paper, we propose a channel normalization method to improve the performance of CMS (cepstral mean subtraction) which is widely adopted to normalize a channel variation for speech and speaker recognition. CMS which estimates the channel effects by averaging long-term cepstrum has a weak point that the estimated channel is biased by the formants of voiced speech which include a useful speech information. The proposed Formant-broadened Cepstral Mean Subtraction (FBCMS) is based on the facts that the formants can be found easily in log spectrum which is transformed from the cepstrum by fourier transform and the formants correspond to the dominant poles of all-pole model which is usually modeled vocal tract. The FBCMS evaluates only poles to be broadened from the log spectrum without polynomial factorization and makes a formant-broadened cepstrum by broadening the bandwidths of formant poles. We can estimate the channel cepstrum effectively by averaging formant-broadened cepstral coefficients. We performed the experiments to compare FBCMS with CMS, PFCMS using 4 simulated telephone channels. In the experiment of channel estimation, we evaluated the distance cepstrum of real channel from the cepstrum of estimated channel and found that we were able to get the mean cepstrum closer to the channel cepstrum due to an softening the bias of mean cepstrum to speech. In the experiment of text-independent speaker identification, we showed the result that the proposed method was superior than the conventional CMS and comparable to the pole-filtered CMS. Consequently, we showed the proposed method was efficiently able to normalize the channel variation based on the conventional CMS.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3213-3215
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• 2000

뇌자도 신호의 측정은 뇌에서 발생하는 자장 성분을 정밀하게 측정할 수 있으나, 신호의 크기가 매우 작기 때문에 노이즈에 매우 민감하게 동작하며 이러한 노이즈 성분의 발생원인은 외부 환경에 의하여 발생하거나 시스템 내부에서 발생하는 두가지로 나눌 수 있다. 따라서 뇌자도 신호를 측정하는데 있어서 가장 중요한 작업은 신호에 존재하는 노이즈 성분을 제거하는 것이다. 특히 뇌자도 측정 시스템에서는 외부 노이즈 성분을 제거하기 위하여 레퍼런스 채널이 존재한다. 따라서 본 논문에서는 청각 자극 신호에 의한 뇌자도 신호를 측정하고 측정한 데이터를 사용하여 레퍼런스 채널과 입력신호에 대하여 LMS 알고리즘을 이용한 적응 필터를 모델링 하였다. 그리고, 구현한 적응 필터를 이용하여 뇌자도 신호의 평균값, 표준편차의 통계적 결과를 비교하여 모델링한 적응 필터 방법의 유용성을 확인하였다.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3718-3726
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• 1999

Color image is more effective than gray one in human visual perception. Therefore, color image processing becomes important area. Color images are often corrupted by noises due to the input sensor, channel transmission errors and so on. Some filtering techniques such as vector median, mean filter, and vector $\alpha-trimmed$ mean filter have been used for color noise removal. Among them, vector $\alpha-trimmed$ mean filter gave the best performance in the mixed color noise. But, there are edge shift and blurring effect because vector $\alpha-trimmed$ mean filter is uniformly processed across the image. So, we proposed a conditional fuzzy cluster filter to improve this problems. Simulation results showed that the proposed scheme improves the NCD measure and visual quality over the conventional vector $\alpha-trimmed$ mean filter in the mixed color noise.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.34-41
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• 2017

Vehicle to Infrastructure(V2I) communication means the technology between the vehicle and the roadside unit to provide the Intelligent Transportation Systems(ITS) and Telematic services. The vehicle collects information about the probe data through the evolved Node B(eNodeB) and after that eNodeB provides road conditions or traffic information to the vehicle. To provide these V2I communication services, we need a link adaptation technology that enables reliable and higher transmission rate. The receiver transmits the estimated Channel State Information(CSI) to transmitter, which uses this information to enable the link adaptation. However, due to the rapid channel variation caused by vehicle speed and the processing delay between the layers, the estimated CSI quickly becomes outdated. For this reason, channel interpolation and prediction scheme are needed to achieve link adaptation in V2I communication system. We propose the Advanced Discrete Prolate Spheroidal Sequence(ADPSS) channel interpolation and prediction scheme. The proposed scheme creates an orthonomal basis, and uses a correlation matrix to interpolate and predict channel. Also, smoothing is applied to frequency domain for noise removal. Simulation results show that the proposed scheme outperforms conventional schemes with the high speed and low speed vehicle in the freeway and urban environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2473-2483
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• 2000

In this paper, The turbo code is used to effectively remove noise which is generated on the image communication channel. Turbo code had excellent decoding performance. However, it had limitations for real time communication because of the system complexity and time delay in decoding procedure. To overcome this problem, this paper proposed a new SRI(Semi Random Interleaved algorithm, which decrease the time delay, when the image data, which reduced the interleaver size of turbo code encoder and decoder, transmitted. The SRI algorithm was composed of 0.5 interleaver size from input frame sequence. When the data inputs in interleaver, the data recorded by row such as block interleaver. But, When the data read in interleaver, the data was read by randomly and the next data located by the just address simultaneously. Therefore, the SRI reduced half-complexity when it was compared with pre-existing method such as block, helical, random interleaver. The image data could be the real time processing when the SRI applied to turbo code.