• Title/Summary/Keyword: Robust adaptive algorithm

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Internet Traffic Control Using Dynamic Neural Networks

  • Cho, Hyun-Cheol;Fadali, M. Sami;Lee, Kwon-Soon
    • Journal of Electrical Engineering and Technology
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    • v.3 no.2
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    • pp.285-291
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    • 2008
  • Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

Vibration-Robust Attitude and Heading Reference System Using Windowed Measurement Error Covariance

  • Kim, Jong-Myeong;Mok, Sung-Hoon;Leeghim, Henzeh;Lee, Chang-Yull
    • International Journal of Aeronautical and Space Sciences
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    • v.18 no.3
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    • pp.555-564
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    • 2017
  • In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

New De-interlacing Algorithm Combining Edge Dependent Interpolation and Global Motion Compensation Based on Horizontal and Vertical Patterns (수평, 수직 패턴에 기반 한 경계 방향 보간과 전역 움직임 보상을 고려한 새로운 순차주사화 알고리즘)

  • 박민규;이태윤;강문기
    • Journal of Broadcast Engineering
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    • v.9 no.1
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    • pp.43-53
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    • 2004
  • In this paper, we propose a robust deinterlacing algorithm which combines edge dependent interpolation (EDI) and global motion compensation (GMC). Generally, EDI algorithm shows a visually better performance than any other deinterlacing algorithm using one field. However, due to the restriction of information in one field, a high duality progressive image from Interlaced sources cannot be acquired by intrafield methods. On the contrary, since algorithms based on motion compensation make use of not only spatial information but also temporal information, they yield better results than those of using one field. However, performance of algorithms based on motion compensation depends on the performance of motion estimation. Hence, the proposed algorithm makes use of mixing process of EDI and GMC. In order to obtain the best result, an adaptive thresholding algorithm for detecting the failure of GMC is proposed. Experimental results indicate that the proposed algorithm outperforms the conventional approaches with respect to both objective and subjective criteria.

Robust Extraction of Facial Features under Illumination Variations (조명 변화에 견고한 얼굴 특징 추출)

  • Jung Sung-Tae
    • Journal of the Korea Society of Computer and Information
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    • v.10 no.6 s.38
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    • pp.1-8
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    • 2005
  • Facial analysis is used in many applications like face recognition systems, human-computer interface through head movements or facial expressions, model based coding, or virtual reality. In all these applications a very precise extraction of facial feature points are necessary. In this paper we presents a method for automatic extraction of the facial features Points such as mouth corners, eye corners, eyebrow corners. First, face region is detected by AdaBoost-based object detection algorithm. Then a combination of three kinds of feature energy for facial features are computed; valley energy, intensity energy and edge energy. After feature area are detected by searching horizontal rectangles which has high feature energy. Finally, a corner detection algorithm is applied on the end region of each feature area. Because we integrate three feature energy and the suggested estimation method for valley energy and intensity energy are adaptive to the illumination change, the proposed feature extraction method is robust under various conditions.

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Visual Voice Activity Detection and Adaptive Threshold Estimation for Speech Recognition (음성인식기 성능 향상을 위한 영상기반 음성구간 검출 및 적응적 문턱값 추정)

  • Song, Taeyup;Lee, Kyungsun;Kim, Sung Soo;Lee, Jae-Won;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.34 no.4
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    • pp.321-327
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    • 2015
  • In this paper, we propose an algorithm for achieving robust Visual Voice Activity Detection (VVAD) for enhanced speech recognition. In conventional VVAD algorithms, the motion of lip region is found by applying an optical flow or Chaos inspired measures for detecting visual speech frames. The optical flow-based VVAD is difficult to be adopted to driving scenarios due to its computational complexity. While invariant to illumination changes, Chaos theory based VVAD method is sensitive to motion translations caused by driver's head movements. The proposed Local Variance Histogram (LVH) is robust to the pixel intensity changes from both illumination change and translation change. Hence, for improved performance in environmental changes, we adopt the novel threshold estimation using total variance change. In the experimental results, the proposed VVAD algorithm achieves robustness in various driving situations.

Robust Road Detection using Adaptive Seed based Watershed Segmentation (적응적 Seed를 기초로한 분수계 분할을 이용한 차도영역 검출)

  • Park, Han-dong;Oh, Jeong-su
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2015.10a
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    • pp.687-690
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    • 2015
  • Forward collision warning systems(FCWS) and lane change assist systems(LCAS) need regions of interest for detecting lanes and objects as road regions. Watershed segmentation is effective algorithm that classify the road. That algorithm is split results appear differently depending on Watershed line with local minimum in the early part of the seed. If not road regions or vehicles combined the road's seed, It segment road with the others. For compensate the that defect, It has to adaptive change by road environment. The method is that image segmentate the several of regions of interest. Then It is set in a straight line that is detected in regions of interest. If It was detected cars on seed, seed is adjusted the location. And If It wasn't include the line, seed is adjusted the length for final decision the seed. We can detect the road region using the final seed that selected according to the road environment.

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Real-Time Object Tracking Algorithm based on Adaptive Color Model in Surveillance Networks (서베일런스 네트워크에서 적응적 색상 모델을 기초로 한 실시간 객체 추적 알고리즘)

  • Kang, Sung-Kwan;Lee, Jung-Hyun
    • Journal of Digital Convergence
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    • v.13 no.9
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    • pp.183-189
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    • 2015
  • In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.

Implementation of An Unmanned Visual Surveillance System with Embedded Control (임베디드 제어에 의한 무인 영상 감시시스템 구현)

  • Kim, Dong-Jin;Jung, Yong-Bae;Park, Young-Seak;Kim, Tae-Hyo
    • Journal of the Institute of Convergence Signal Processing
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    • v.12 no.1
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    • pp.13-19
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    • 2011
  • In this paper, a visual surveillance system using SOPC based NIOS II embedded processor and C2H compiler was implemented. In this system, the IP is constructed by C2H compiler for the output of the camera images, image processing, serial communication and network communication, then, it is implemented to effectively control each IP based on the SOPC and the NIOS II embedded processor. And, an algorithm which updates the background images for high speed and robust detection of the moving objects is proposed using the Adaptive Gaussian Mixture Model(AGMM). In results, it can detecte the moving objects(pedestrians and vehicles) under day-time and night-time. It is confirmed that the proposed AGMM algorithm has better performance than the Adaptive Threshold Method(ATM) and the Gaussian Mixture Model(GMM) from our experiments.

Robust k-means Clustering-based High-speed Barcode Decoding Method to Blur and Illumination Variation (블러와 조명 변화에 강인한 k-means 클러스터링 기반 고속 바코드 정보 추출 방법)

  • Kim, Geun-Jun;Cho, Hosang;Kang, Bongsoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.1
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    • pp.58-64
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    • 2016
  • In this paper presents Robust k-means clustering-based high-speed bar code decoding method to blur and lighting. for fast operation speed and robust decoding to blur, proposed method uses adaptive local threshold binarization methods that calculate threshold value by dividing blur region and a non-blurred region. Also, in order to prevent decoding fail from the noise, decoder based on k-means clustering algorithm is implemented using area data summed pixel width line of the same number of element. Results of simulation using samples taken at various worst case environment, the average success rate of proposed method is 98.47%. it showed the highest decoding success rate among the three comparison programs.

MRI Image Retrieval Using Wavelet with Mahalanobis Distance Measurement

  • Rajakumar, K.;Muttan, S.
    • Journal of Electrical Engineering and Technology
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    • v.8 no.5
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    • pp.1188-1193
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    • 2013
  • In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and H elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.