• Journal of the Korean Institute of Telematics and Electronics D
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• v.36D no.3
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• pp.1-11
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• 1999

It is now common practice to utilize the quadrature RF coils to improve the signal-to-noise ratio (SNR) in the Magnetic Resonance Imaging (MRI) System. In addition, to make such an available SNR improvement, it is mandatory to use a well-designed quadrature coupler, which facilitates a perfect 3-dB coupling and quadrature-phase shift. However, the four ports matching condition has to be well considered during the RF excitation and the signal detection period. This work investigates the effects of such a mismatching condition (especially, due to patient) from the analysis, simulation, and real implementation and firstly proposes dynamic loading technique for a quadrature coupler and transmitting/receiving switch module to minimize a patient mismatching and enhance a system reliability. Also, we designed and implemented the quadrature coupler and transmitting/receiving switch module using microstrip. As a result, the SNR of our MRI system using the microstrip quadrature coupler and transmitting/receiving switch module with dynamic load increases 3 dB compared with the old one using USA quadrature switch. Also, the power capability of quadrature coupler and transmitting/receiving switch module is 5-kw peak power. Considering power loss and reduction of size, we used a RT/duroid 6010 substrate with high permittivity and for simulation we use Compact Software.

• Journal of Korea Game Society
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• v.12 no.5
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• pp.79-88
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• 2012

In this paper, we present a real-time hand pose recognition method to provide an intuitive user interface through hand poses or movements without a keyboard and a mouse. For this, the areas of right and left hands are segmented from the depth camera image, and noise removal is performed. Then, the rotation angle and the centroid point of each hand area are calculated. Subsequently, a circle is expanded at regular intervals from a centroid point of the hand to detect joint points and end points of the finger by obtaining the midway points of the hand boundary crossing. Lastly, the matching between the hand information calculated previously and the hand model of previous frame is performed, and the hand model is recognized to update the hand model for the next frame. This method enables users to predict the hidden fingers through the hand model information of the previous frame using temporal coherence in consecutive frames. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 95% and the performance indicated over 32 fps. The proposed method can be used as a contactless input interface in presentation, advertisement, education, and game applications.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.4
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• pp.82-92
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• 2006

In the field of image recognition, research on face recognition has recently attracted a lot of attention. The most important step in face recognition is automatic eye detection researched as a prerequisite stage. Existing eye detection methods for focusing on the frontal face can be mainly classified into two categories: active infrared(IR)-based approaches and image-based approaches. This paper proposes an eye region detection method in non-frontal faces. The proposed method is based on the edge--based method that shows the fastest computation time. To extract eye region in non-frontal faces, the method uses edge orientationhistogram of the global region of faces. The problem caused by some noise and unfavorable ambient light is solved by using proportion of width and height for local information and relationship between components for global information in approximately extracted region. In experimental results, the proposed method improved precision rates, as solving 3 problems caused by edge information and achieves a detection accuracy of 83.5% and a computational time of 0.5sec per face image using 300 face images provided by The Weizmann Institute of Science.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.363-373
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• 2012

This paper proposes a method to generate a depth image at an arbitrary intermediate view-point, which is targeting a video service for free-view, auto-stereoscopy, holography, etc. It assumes that the leftmost and the rightmost depth images are given and they both have been camera-calibrated and image-rectified. This method calculates and uses a disparity increment per depth value. In this paper, it is obtained by stereo matching for the given two depth image by considering more general cases. The disparity increment is used to find the location in the intermediate view-point depth image (IVPD) for each depth in the given images. Thus, this paper finds two IVPDs, from left image and from right image. Noises are removed and holes are filled in each IVPDs and the two results are combined to get the final IVPD. The proposed method was implemented and applied to several test sequences. The results revealed that the quality of the generated IVPD corresponds to 33.84dB of PSNR in average and it takes about 1 second to generate a HD IVPD. We evaluate that this image quality is quite good by considering the low correspondency among the left images, intermediate images, and the right images in the test sequences. If the execution speed is improved, the proposed method can be a very useful method to generate an IVPD at an arbitrary view-point, we believe.

• Progress in Medical Physics
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• v.6 no.2
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• pp.83-92
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• 1995

The T1 mapping of an human anatomy may give a characteristic contrast among the various tissues and the normal/abnormal tissues. Here, the methodology of constructing T1 map out of several images with different TRs, will be described using non-linear curve fitting. The general curve fitting algorithm requires the initial trial values T1t and Mot for the variables to be fitted. Three different methods of suppling the trial T1t and Mot are suggested and compared for the efficiency and the accuracy. The curve-fitting routine was written in ANSI C and excuted on a SUN workstation. Several distilled-water phantoms with various concentrations of Gd-DTPA were prepared to examine the accuracy of the curve-fitting program. An MR image was used as the true proton density image without any random noise, and several images with different TRs were generated with the theoretical T1 relaxation times 250, 500, and 1000msec. The random noise of 1, 5, and 10％ were embedded into the simulated images. These images were used to generate the T1 map, and the resultant T1 maps for each T1 were analyzed to study the effect of the random noise on the T1 map.

• Progress in Medical Physics
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• v.20 no.2
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• pp.62-71
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• 2009

Recently, optical coherence tomography (OCT) has demonstrated considerable promise for the noninvasive assessment of biological tissues. However, OCT images difficult to analyze due to speckle noise. In this paper, we tested various image processing techniques for speckle removal of human and rabbit cartilage OCT images. Also, we distinguished the images which get with methods of image segmentation for OCT images, and found the most suitable method for segmenting an image. And, we selected image segmentation suitable for OCT before image reconstruction. OCT was a weak point to system design and image processing. It was a limit owing to measure small a distance and depth size. So, good edge matching algorithms are important for image reconstruction. This paper presents such an algorithm, the chamfer matching algorithm. It is made of background for 3D image reconstruction. The purpose of this paper is to describe good image processing techniques for speckle removal, image segmentation, and the 3D reconstruction of cartilage OCT images.

• Investigative Magnetic Resonance Imaging
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• v.10 no.2
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• pp.89-97
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• 2006

Purpose : To evaluate MR image qualities we developed a new MRI phantom with the fixation structures necessary to position it into coil firmly. Materials and methods : We designed MRI phantom for eight evaluation items such as slice thickness accuracy, high contrast spatial resolution, low contrast object detectability, geometry accuracy, slice position accuracy, image intensity uniformity, percent signal ghosting and signal to noise ratio. For the positioning of phantom at coils, the fixation structures were set up on the surface of phantom. Six different MRI units were used for test the possibility for the clinical application and their image qualities were evaluated. Results : We acquired appropriate MR image qualities enough for the evaluation on all used MR units and confirmed that their evaluations were within reliable values compared to real ones for some items. The positioning of our phantom into head coils with fixation structures worked well for proper imaging. Conclusion : We found that our prototype of MRI phantom had the possibility of clinical application for MR image quality assessment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.8
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• pp.692-700
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• 2014

In this work, satellite FE (finite element) model updating for the prediction of the effect of micro-vibration is described. In the case of satellites launched in low earth orbit, high agility and more mission accomplishments are required by the customer in order to procure many images from satellites. To achieve the goal, many mechanisms, including high capacity wheels and antennas with multi-axis gimbals have been widely adopted, but they become a source of micro-vibration which could significantly deteriorate the quality of images. To investigate the effect due to the micro-vibration in orbit on the ground, a prediction is conducted through an integrated model coupling the measured jitter sources with FE (finite element) model. Before prediction, the FE model is updated to match simulation results with the modal survey test. Subsequently, the quality of FE model is evaluated in terms of frequency deviation error, the resemblance of mode shapes and FRFs (frequency response functions) between test and analysis.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.