• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.29-35
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• 2010

This paper discusses a method that can enhance the exactness of depth estimation of an image by PCA(Principle Component Analysis) based on feature reduction through learning algorithm. In estimation of the depth of an image, hyphen such as energy of pixels and gradient of them are found, those selves and their relationship are used for depth estimation. In such a case, many features are obtained by various filter operations. If all of the obtained features are equally used without considering their contribution for depth estimation, The efficiency of depth estimation goes down. This paper proposes a method that can enhance the exactness of depth estimation of an image and its processing speed is considered as the contribution factor through PCA. The experiment shows that the proposed method(30% of an feature vector) is more exact(average 0.4%, maximum 2.5%) than using all of an image data in depth estimation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.1
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• pp.45-50
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• 2015

In this paper, we confirm the effective adaptive algorithm for tha active noise contorl (ANC) though the performance comparison between adaptive algorithms. Generally, the normalized least mean square (NLMS) algorithm has been widely used for an adaptive algorithm thanks to its simplicity and having a fast convergence speed. However, the convergence performance of the NLMS algorithms is often deteriorated by colored input signals. To overcome this problem, the affine pojection (AP) algorithm that updates the weight vector based on a number of recent input vectors can be used for allowing a higher convergence speed than the NLMS algorithm, but it is computationally complex. Thus, the proper algorithm were determined by the comparison between NLMS and AP algorithms regarding as the convergence performance and complexity. Simulation results confirmed that the noise reduction performance of NLMS algorithm was comparable to AP algorithm with low complexity. Therefore the NLMS algorithm is more effective for ANC system.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.279-289
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• 1994

A Geographic Database System is a database system which supports map-formed output and allows users to store, retrieve, manage and analyze spatial and aspatial data. Because of large data amount, takes too much time to input spatial data into a Geographic Database System and too much storage. Therefore, an efficient spatial data collecting system is highly required for a Geographic Database System to reduce the input processing time and to use the storage efficiently. In this paper, we analyze conventional vectorizing methods and suggest a different approach. Our approach vectorizes specific geographic data when the users input its aspatial data, instead of vectorizing all the map data. And also, we propose optimized vector data format using tag bit to use the storage that collected data efficiently.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9C
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• pp.871-879
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• 2009

The performance variations of a spatio temporal smart antenna which is equipped at the basestation of CDMA cellular communication network due to the parametric change of multipath fading environment are studied in this paper. The smart antenna of interest employs space diversity based adaptive array structure in conjunction with rake receiver that has fingers the number of which is the same as that of multipath links. The beamforming is achieved via LMS(Least Mean Square) algorithm in which a reference signal is generated using decision directed formula. It has been shown by computer simulation that the performance of our smart antenna of interest depends significantly upon not only the degree of desired signal's DOA(Direction of Arrival)spread but the number of fingers of the rake receiver. The relative insensitivity of the smart antenna's performance on desired signal's delay spread has also been observed. Computer simulation has shown that the increase of the number of fingers brings in a nonlinear enhancement of the performance of our smart antenna. The renewal of weight vector in the beamforming procedure is taken place at post PN despread stage.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• Journal of the Korea Society of Computer and Information
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• v.13 no.2
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• pp.185-191
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• 2008

Most threats of WLAN are easily caused by attackers who access to the radio link between STA and AP, which involves some Problems to intercept network communications or inject additional messages into them. In comparison with wired LAN, severity of wireless LAN against threats is bigger than the other networks. To make up for the vulnerability of wireless LAN, it needs to use the Intrusion Detection System using a powerful intrusion detection method as SVM. However, due to classification based on calculating values after having expressed input data in vector space by SVM, continuous data type can not be used as any input data. In this paper, therefore, we design the IDS system for WLAN by tuning with SVM and data-mining mechanism to defend the vulnerability on certain WLAN and then we demonstrate the superiority of our method.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.51-56
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• 2007

Feature functions were used for the classification of music. The spectral roll-off, variance, average peak level, and class were chosen to make up a feature function vector. Among these, it is the spectral roll-off function that has a low-frequency to high-frequency ratio. To find the optimal roll-off point, the roll-off points from 0.05 to 0.95 were swept. The classification success rate was monitored as the roll-off point was being changed. The data that were used for the experiments were taken from the sounds made by a modern violin and a baroque one. Their shapes and sounds are similar, but they differ slightly in sound texture. As such, the data obtained from the sounds of these two kinds of violin can be useful in finding an adequate roll-off point. The optimal roll-off point, as determined through the experiment, was 0.85. At this point, the classification success rate was 85%, which was the highest.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.57-65
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• 2007

In the fast block matching algorithm. search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image qualify. In this paper, we propose a new fast block matching algorithm using the flat-hexagon search pattern that ate solved disadvantages of the diamond pattern search algorithm(DS) and the hexagon-based search algorithm(HEXBS). Our proposed algorithm finds mainly the motion vectors that not close to the center of search window using the flat-hexagon search pattern. Through experiments, compared with the DS and HEXBS, the proposed f)at-hexagon search algorithm(FHS) improves about $0.4{\sim}21.3%$ in terms of average number of search point per motion vector estimation and improves about $0.009{\sim}0.531dB$ in terms of PSNR(Peak Signal to Noise Ratio).

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.35-42
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• 2000

3D texture mapping generates highly natural visual effects in which objects appear carved from lumps of materials rather than laminated with thin sheets as in 2D texture mapping. Storing 3D texture images in a table for fast mapping computations, instead of evaluating procedures on the fly, however, has been considered impractical due to the extremely high memory requirement. Recently, a practical real-time 3D texture mapping technique was proposed in [11], where they attempt to resolve the potential texture memory problem by compressing 3D textures using a wavelet-based encoding method. In this paper, we consider two other encoding schemes that could also be applied to the compression-based 3D texture mapping. In particular, we extend the vector quantization and FXT1 for 3D texture compression, and compare their performance with the wavelet-based encoding scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.423-428
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• 2017

These days, various and tons of education information are rapidly increasing and spreading due to Internet and smart devices usage. Recently, as e-Learning usage increasing, many instructors and students (learners) need to set a goal to maximize learners' result of education and education system efficiency based on big data analytics via online recorded education historical data. In this paper, the author applied Word2Vec algorithm (neural network algorithm) to find similarity among education words and classification by clustering algorithm in order to objectively recognize and analyze online recorded education historical data. When the author applied the Word2Vec algorithm to education words, related-meaning words can be found, classified and get a similar vector values via learning repetition. In addition, through experimental results, the author proved the part of speech (noun, verb, adjective and adverb) have same shortest distance from the centroid by using clustering algorithm.