• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.758-760
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• 1995

The purpose of noise cancellation is to estimating signals corrupted by additive noise or interference. In this paper, an adaptive noise canceller is built from a Walsh filter with a new adaptive algorithm. The Walsh filter consists of a Walsh function. Since the Walsh functions are either even or odd functions, the covariance matrix in the tap gain adjustment algorithm can be reduced to a simple form. In this paper, minimization of the mean squre error is accomplished by a proposed adaptive algorithm. The conventional adaptation techniques use a fixed time constant convergence factor by trial and error methods. In this paper, a convergence factor is obtained that is tailored for each adaptive filter coefficient and is updated at each block iteration.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• ETRI Journal
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• v.36 no.6
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• pp.968-978
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• 2014

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a $300m{\times}500m$ space.

• Journal of Institute of Convergence Technology
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• v.1 no.1
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• pp.41-47
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• 2011

With the world-wide demand on the emission minimization, the needs on the diesel aftertreatment devices with high efficiency are also increasing. In order to effectively develop or design a high-performance diesel particulate filter, a clear understanding on the deposition and regeneration mechanism is required. In the present study, a theory on the lumped parameter model for wall-flow type diesel particulate filters is described focusing on the deposition efficiency, pressure drop inside the filter. The fourth order explicit Runge-Kutta method is utilized for the mass flow rate computation. Engine operation modes with controlled and uncontrolled regeneration options are selected. The computational lumped parameter model is validated by comparing the computed results with the measured data.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1203-1211
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• 2022

Melanoma is a skin cancer that starts in pigment-producing cells (melanocytes). The death rates of skin cancer like melanoma can be reduced by early detection and diagnosis of diseases. It is common for doctors to spend a lot of time trying to distinguish between skin lesions and healthy cells because of their striking similarities. The detection of melanoma lesions can be made easier for doctors with the help of an automated classification system that uses deep learning. This study presents a new approach for melanoma classification based on an ensemble of deep convolution neural networks and a Log-Gabor filter. First, we create the Log-Gabor representation of the original image. Then, we input the Log-Gabor representation into a new ensemble of deep convolution neural networks. We evaluated the proposed method on the melanoma dataset collected at Yonsei University and Dongsan Clinic. Based on our numerical results, the proposed framework achieves more accuracy than other approaches.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.165-168
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• 2000

In this paper, we propose a method, an optimized architecture of a device with an image signal process of a field unit to minimize the flick phenomenon that happens in direction of a color temperature at a color tone change. The proposed IIR loop filter has an optimized architecture and reduced hardware compared with previous filters. In order to achieve the optimization for the hardware complexity. It is designed by time-multiplexing architecture. The proposed IIR loop filter is synthesized by using the STD90 0.35um cell library.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.2
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• pp.18-24
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• 1985

Above certain filter order, the frequency domain -adaptive filter is superior to the time domain-adaptive filter in computational complexity. In this paper a new type algorithm, $\mu$-FLMs algorithm, is proposed for the frequency domain- adaptive filter and the characteristics of the proposed algorithm is compared with that of the time domain- adaptive filter algorithm($\mu$-FLMS algorithm). The simulation results showed that under the same convergence rate , the frequency domain-adaptive filter is efficient in compu tational burden.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.103-109
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• 1995

In this paper, we propose a postprocessing method that neatly removes blocking effect but retains visually important image details and edges. The iterative thresholded lowpass filter is basically a low pass filter whose ouput depends on three variable elements. I.e. iteration number, threshold value and passband width. The threshold value restricts the difference between the output of the proposed filter and the original input independent of the iteration number. With this property, the iterative thresholded lowpass filter can retain most of the image details while smoothing the block boundaries. The other two variable elements, i.e. iteration number and passband width, can determine the convergence speed of the proposed filter. In this paper, we also propose several adaptive filtering techniques based on the iterative thresholded lowpass filter with their simulation results.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.4
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• pp.1-9
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• 1983

In designing the adaptive recursive filter using the MRAS hyperstable output-error identifier, the most crucial point is the design of the error-smoothing filter In this paper, it is shown that by modifyins the HARF algorithm properly, we can obtain an algorithm which requires no error-smoothing filter and has relatively high convergence rate.

• Speech Sciences
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• v.11 no.4
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• pp.89-97
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• 2004

In this paper, an improved network echo canceller is proposed. The proposed echo canceller is based on the LTJ(lattice transversal joint) adaptive filter which uses informations from the speech decoder. In the proposed implementation method of the network echo canceller, the filer coefficients of the transversal filter part in the LTJ adaptive filter is updated every other sample instead of every sample. So its complexity can be lower than that of the transversal filter. And the echo cancellation rate can be improved by residual echo cancellation using the lattice predictor whose order is less than 10. Computational complexity of the proposed echo canceller is lower than that of the transversal filter but the convergence speed is faster than that of the transversal filter. The performance improvement of the proposed echo canceller was verified by the experiments using the real speech signal and speech coder.