• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.53-63
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• 2001

An efficient mobility management for mobile stations plays an important role in mobile communication network. This paper studies the mobility management scheme that combines a distance-based registration(DBR) and a selective paging (SP). We introduce an analytical model based on 2-dimensional random walk mobility model and evaluate the performance of the proposed mobility management scheme using the model to determine the optimal size of location area that results in the minimum signaling traffic on radio channels. Numerical results are provided to demonstrate the performance of the proposed mobility management scheme under various circumstances. These results can be used effectively in design and evaluation of registration methods considering the system circumstances.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.366-370
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• 2001

한정된 무선 채널로 보다 많은 가입자에게 이동통신 서비스를 제공하기 위해서는 무선 채널의 효율을 높여야 하며 이를 위해 효율적인 위치등록 방법이 필수적이다. 본 연구에서는 이동기준 위치등록(movement-based registration, MBR)과 거리기준 위치등록(distance-based registration, DBR)의 성능을 평가하고 두 위치등록 방법의 성능을 비교한다. 본 연구에서는 육각형 셀 환경하에서 2차원 랜덤워크 모형 (2-dimensional random walk model)에 기반을 둔 이동성 모형을 이용하여 MBR과 DBR의 위치등록 부하를 구한다. 특히 기존의 수식과는 다른 형태의 DBR의 위치등록 부하에 대한 식을 제시하고 이를 이용하여 DBR이 MBR에 비하여 항상 우수한 성능을 나타냄을 보인다. 또한 다양한 경우에 대한 수리적 결과를 통하여 무선 채널에서의 신호 트래픽을 최소로 해 주는 최적 제어변수의 값을 제시한다. 본 연구의 결과는 시스템의 운용환경에 따라 적절한 위치등록 방법을 평가하는 데에 효과적으로 이용될 수 있다.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.391-400
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• 2004

Within multimodal medical image registration techniques, which correlate different images and Provide integrated information, surface registration methods generally minimize the surface distance between two modalities. However, the features of two modalities acquired from one subject are similar. So, it can improve the accuracy of registration result to match two images based on optimization of both surface distance and shape feature. This research proposes a registration method which optimizes surface distance and surface curvature of two brain modalities. The registration process has two steps. First, surface information is extracted from the reference images and the test images. Next, the optimization process is performed. In the former step, the surface boundaries of regions of interest are extracted from the two modalities. And for the boundary of reference volume image, distance map and curvature map are generated. In the optimization step, a transformation minimizing both surface distance and surface curvature difference is determined by a cost function referring to the distance map and curvature map. The applying of the result transformation makes test volume be registered to reference volume. The suggested cost function makes possible a more robust and accurate registration result than that of the cost function using the surface distance only. Also, this research provides an efficient means for image analysis through volume visualization of the registration result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2402-2414
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• 1996

Registration is the process by which a base station gets the information of location and status about mobile stations. Registration will become an increasingly important issue in mobile cellular communications, especially in microcellular system with the increase of location updating traffic. Four typesof registration schemes, which are specified in US standard IS-95 and Korean standard TTA-KO-0062, power up registration scheme, power down registration scheme, distance based registration scheme and zone based registration scheme, will be mainly used in the future. In this paper, we derive the combined cost of signaling traffic of paging and registration for various parameters of mobile stations behavior(unencumbered call duration, power up and down rate, velocity, etc.) then we present numerical results for the perfroamnce of four registration schemes. The size of location area is provided for optimal operation.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.3_4
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• pp.224-238
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• 2004

Multi-modality image registration is a widely used image processing technique to obtain composite information from two different kinds of image sources. This study proposes an image registration method based on moment information and surface distance, which improves the previous surface-based registration method. The proposed method ensures stable registration results with low registration error without being subject to the initial position and direction of the object. In the preprocessing step, the surface points of the object are extracted, and then moment information is computed based on the surface points. Moment information is matched prior to fine registration based on the surface distance, in order to ensure stable registration results even when the initial positions and directions of the objects are very different. Moreover, surface comer sampling algorithm has been used in extracting representative surface points of the image to overcome the limits of the existed random sampling or systematic sampling methods. The proposed method has been applied to brain MRI(Magnetic Resonance Imaging) and PET(Positron Emission Tomography), and its accuracy and stability were verified through registration error ratio and visual inspection of the 2D/3D registration result images.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.612-624
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• 2005

In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.360-365
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• 2008

We propose an efficient robot name registration and recognition method in order to enable a Call-and-Come service for home robots. In the proposed method for the name registration, the search space is first restricted by using monophone-based acoustic models. Second, the registration of robot names is completed by using triphone-based acoustic models in the restricted search space. Next, the parameter for the utterance verification is calculated to reduce the acceptance rate of false calls. In addition, acoustic models are adapted by using a distance speech database to improve the performance of distance speech recognition, Moreover, the location of a user is estimated by using a microphone array. The experimental result on the registration and recognition of robot names shows that the word accuracy of speech recognition is 98.3%.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.265-272
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• 2005

In this paper, we propose a technique which is speedily searching for correspondent points of two images using Voronoi-Distance, as an image registration method for feature based image mosaics. It extracts feature points in two images by the SUSAN corner detector, and then create not only the Voronoi Surface which has distance information among the feature points in the base image using a priority based Voronoi distance algorithm but also select the model area which has the maximum variance value of coordinates of the feature points in the model image. We propose a method for searching for the correspondent points in the Voronoi surface of the base image overlapped with the model area by use of the partitive search algorithm using queues. The feature of the method is that we can rapidly search for the correspondent points between adjacent images using the new Voronoi distance algorithm which has $O(width{\times}height{\times}logN)$ time complexity and the the partitive search algerian using queues which reduces the search range by a fourth at a time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.3
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• pp.353-359
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• 2010

In this paper, we propose the automatic image-to-image registration of high resolution satellite images using local properties of tie points to improve the registration accuracy. A spatial distance between interest points of reference and sensed images extracted by Scale Invariant Feature Transform(SIFT) is additionally used to extract tie points. Coefficients of affine transform between images are extracted by invariant descriptor based matching, and interest points of sensed image are transformed to the reference coordinate system using these coefficients. The spatial distance between interest points of sensed image which have been transformed to the reference coordinates and interest points of reference image is calculated for secondary matching. The piecewise linear function is applied to the matched tie points for automatic registration of high resolution images. The proposed method can extract spatially well-distributed tie points compared with SIFT based method.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.68-74
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• 2016

In this study, we investigated the rotational characteristics which were comprised of directionality and linearity of target registration error (TRE) as a study in advance to enhance the accuracy of contour-based registration in neuronavigation. For the experiment, two rigid head phantoms that have different faces with specially designed target frame fixed inside of the phantoms were used. Three-dimensional coordinates of facial surface point cloud and target point of the phantoms were acquired using computed tomography (CT) and 3D scanner. Iterative closest point (ICP) method was used for registration of two different point cloud and the directionality and linearity of TRE in overall head were calculated by using 3D position of targets after registration. As a result, it was represented that TRE had consistent direction in overall head region and was increased in linear fashion as distance from facial surface, but did not show high linearity. These results indicated that it is possible for decrease TRE by controlling orientation of facial surface point cloud acquired from scanner, and the prediction of TRE from surface registration error can decrease the registration accuracy in lesion. In the further studies, we have to develop the contour-based registration method for improvement of accuracy by considering rotational characteristics of TRE.