• Journal of KIEE
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• v.11 no.1
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• pp.69-77
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• 2001

This paper describes an image processing algorithm capable of recognizing road lanes by using a CDF(cumulative distribution function). The CDF is designed for the model function of road lanes. Based on the assumptions that there are no abrupt changes in the direction and location of road lanes and that the intensity of lane boundaries differs from that of the background, we formulated the CDF, which accumulates the edge magnitude for edge directions. The CDF has distinctive peak points at the vicinity of lane directions due to the directional and the positional continuities of a lane. To obtain lane-related information a scatter diagram was constructed by collecting edge pixels, of which the direction corresponds to the peak point of the CDF, then the principal axis-based line fitting was performed for the scatter diagram. Noises can cause many similar features to appear and to disappear in an image. Therefore, to reduce the noise effect a recursive estimator of the CDF was introduced, and also to prevent false alarms or miss detection a scene understanding index (DUI) was formulated by the statistical parameters of the CDF. The proposed algorithm has been implemented in real time on video data obtained from a test vehicle driven on a typical highway.

• Annual Conference of KIPS
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• 2006.05a
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• pp.653-656
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• 2006

본 논문은 Cumulative Distribution Function(CDF) 부합에 의한 영상 개선 방법에 대해서 제안하였다. 제안한 방법은 원본 영상의 히스토그램 분포도를 조사하여 히스토그램 그래프상의 특정 색도값들을 선정, 이 점들을 보간법을 이용하여 히스토그램을 재 작성한다. 이를 이용하여 원본 CDF 그래프를 크게 벋어나지 않고, 즉 밝기 정보가 크게 훼손 되지 않은 상태로 색도 값을 재 배치 함으로써 히스토그램 평활화와 스트레칭 효과를 모두 만족하는 영상 향상의 결과를 얻을 수 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.468-476
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• 2002

The confidence of information from image processing depends on the original image quality. Enhancing the confidence by an algorithm has an essential limitation. Especially, road images are exposed to lots of noisy sources, which makes image processing difficult. We, in this paper, propose a FNN (fuzzy-neural network) capable oi deciding the quality of a road image prior to extracting lane-related information. According to the decision by the FNN, road images are classified into good or bad to extract lane-related information. A CDF (cumulative distribution function), a function of edge histogram, is utilized to construct input parameters of the FNN, it is based on the fact that the shape of the CDF and the image quality has large correlation. Input pattern vector to the FNN consists of ten parameters in which nine parameters are from the CDF and the other one is from intensity distribution of raw image. Correlation analysis shows that each parameter represents the image quality well. According to the experimental results, the proposed FNN system was quite successful. We carried out simulations with real images taken by various lighting and weather conditions and achieved about 99% successful decision-making.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2814-2818
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• 2000

This paper describes an image processing algorithm capable of recognizing the road lane using a CDF (Cumulative Distribution Function). which is designed for the model function of the road lane. The CDF has distinctive peak points at the vicinity of the lane direction because of the directional and positional continuities of the lane. We construct a scatter diagram by collecting the edge pixels with the direction corresponding to the peak point of the CDF and carry out the principal axis-based line fitting for the scatter diagram to obtain the lane information. As noises play the role of making a lot of similar features to the lane appear and disappear in the image we introduce a recursive estimator of the function to reduce the noise effect and a scene understanding index (SUI) formulated by statistical parameters of the CDF to prevent a false alarm or miss detection. The proposed algorithm has been implemented in a real time on the video data obtained from a test vehicle driven in a typical highway.

• Journal of Korea Multimedia Society
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• v.8 no.9
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• pp.1187-1194
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• 2005

In this paper, new contrast enhancement algorithms that use temporal decimation method and approximated CDF(Cumulative Distribution Function) are proposed. They reduce the amount of computation which is required for image contrast enhancement. Simulation results show that the algorithms can achieve significant reduction in the computational cost and the hardware complexity. Visual test and standard deviation of their histogram have been introduced to evaluate the resultant output images of the proposed method and the original ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1240-1243
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• 2016

In this paper, we analyze the queueing performance of cumulative distribution function (CDF)-based opportunistic scheduling over Nakagami-m Markov fading channels. We derive the formula for the average queueing delay and the queue length distribution by constructing a two-dimensional Markov chain. Using our formula, we investigate the queueing performance for various fading parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.924-927
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• 2016

In this paper, we consider a cumulative distribution function (CDF)-based opportunistic scheduling for downlink transmission in a cellular network consisting of a base station and multiple mobile stations. We present a closed-form formula for the average starvation period of each mobile station (i.e., the length of the time interval between two successive scheduling points of a mobile station) over Markov time-varying channels. Based on our formula, we investigate the starvation period of the CDF-based scheduling for various system parameters.

• International Journal of Automotive Technology
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• v.6 no.1
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• pp.53-63
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• 2005

We propose a fuzzy neural network (FNN) theory capable of deciding the quality of a road image prior to extracting lane-related information. The accuracy of lane-related information obtained by image processing depends on the quality of the raw images, which can be classified as good or bad according to how visible the lane marks on the images are. Enhancing the accuracy of the information by an image-processing algorithm is limited due to noise corruption which makes image processing difficult. The FNN, on the other hand, decides whether road images are good or bad with respect to the degree of noise corruption. A cumulative distribution function (CDF), a function of edge histogram, is utilized to extract input parameters from the FNN according to the fact that the shape of the CDF is deeply correlated to the road image quality. A suitability analysis shows that this deep correlation exists between the parameters and the image quality. The input pattern vector of the FNN consists of nine parameters in which eight parameters are from the CDF and one is from the intensity distribution of raw images. Experimental results showed that the proposed FNN system was quite successful. We carried out simulations with real images taken in various lighting and weather conditions, and obtained successful decision-making about $99\%$ of the time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.37-42
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• 2021

NOMA (Non-orthogonal multiple Access) system has been focused on the next generation cellular system for higher spectral efficiency. However, this requires user scheduling as the NOMA system is a multi-user system which accesses simultaneously. There are two representative scheduling schemes, proportionate scheduling (FP) and cumulative distribution function (CFD) scheduling. The PF scheduling is applied, the cell edge user is hard to obtain a transmit opportunity. Recently, CDF scheduling is obviously noted that it offers the same possibility of transmission for a user regardless of the location in a cell. We consider an uplink NOMA system with CDF scheduling, and obtain the channel access probabilities, the outage probabilities of the system with different number of users and different kinds of weights through simulation. The results indicate that the likelihood of each user accessing the channel is the same and the probability of failure decreases as the number of users increases. We found that the effect of the probability of failure is negligible as the weight of the cell edge user increases.

• Wind and Structures
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• v.31 no.6
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• pp.549-560
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• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.