• Journal of the Optical Society of Korea
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• v.14 no.2
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• pp.104-108
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• 2010

We proposed an axis transformation technique which reveals a distance parameter directly from optical scanning holography (OSH). After synthesis of a real-only spectrum hologram and power fringe adjusted filtering, we transform an original frequency axis to a new frequency axis using interpolation. In the new frequency axis, the filtered hologram has a single frequency which is linearly proportional to the distance parameter. Thus, the inverse Fourier transformation of the filtered hologram gives a delta function pair in the new spatial axis. Finally, we extract the distance parameter by detecting the location of the delta function pair.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1993.11a
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• pp.29-34
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• 1993

In this paper, we used one-dimensional process simulator, SUPREM-II, and two-dimensional device simulator, MINIMOS 4.0 to extract optimal process parameter that can minimize degradation of device characteristics caused by process parameter variation in the case of short channel nMOSFET and pMOSFET device. From this simulation, we have derieved the relationship between process parameter and device characteristics. Here we have presented a method to extract process parameters from design trend curve(DTC) obtained by process and device simulations. We parameters to verify the validity of the DTC method. The experimental result of 0.8 $\mu\textrm{m}$ channel length devices that have been fabricated with optimal that reduces short channel effects, that is, good drain current-voltage characteristics, low body effects and threshold voltage of 1.0 V, high punchthrough and breakdown voltage of 12 V, low subthreshold swing(S.S) values of 105 mV/decade.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.9
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• pp.24-29
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• 2010

This paper presents the RF modeling for silicon nanowire MOSFET with 30 nm channel length and 5 nm channel radius. Equations for analytical parameter extraction are derived by analysis of Y-parameter. Accuracies of the new model and extracted parameters have been verified by 3-dimensional device simulation data up to 100 GHz. The model verifications are performed under conditions of saturation region ($V_{gs}$ = $_{ds}$ = 1 V) and linear region ($V_{gs}$ = 1 V, $V_{ds}$ = 0.5 V). The RMS modeling error of Y-parameters was calculated to be 1 %.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.10
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• pp.569-574
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• 2003

Computer vision system is broadly adapted like as autonomous vehicle system, product line inspection, etc., because it has merits which can deal with environment flexibly. However, for applying it for that industry, it has to clear the problem that recognize position parameter of itself. So that computer vision system stands in need of camera calibration to solve that. Camera calibration consists of the intrinsic parameter which describe electrical and optical characteristics and the extrinsic parameter which express the pose and the position of camera. And these parameters have to be reorganized as the environment changes. In traditional methods, however, camera calibration was achieved at off-line condition so that estimation of parameters is in need again. In this paper, we propose a method to the calibration of camera using line correspondence in image sequence varied environment. This method complements the corresponding errors of the point corresponding method statistically by the extraction of line. The line corresponding method is strong by varying environment. Experimental results show that the error of parameter estimated is within 1% and those is effective.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.85-95
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• 2005

This study proposes a method of feature point extraction using scale-space filtering and a feature point tracking algorithm based on a texturedness similarity comparison, With well-defined operators one can select a scale parameter for feature point extraction; this affects the selection and localization of the feature points and also the performance of the tracking algorithm. This study suggests a feature extraction method using scale-space filtering, With a change in the camera's point of view or movement of an object in sequential images, the window of a feature point will have an affine transform. Traditionally, it is difficult to measure the similarity between correspondence points, and tracking errors often occur. This study also suggests a tracking algorithm that expands Shi-Tomasi-Kanade's tracking algorithm with texturedness similarity.

• JSTS:Journal of Semiconductor Technology and Science
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• v.12 no.1
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• pp.46-52
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• 2012

Separate extraction of source ($R_S$) and drain ($R_D$) resistances caused by process, layout variations and long term degradation is very important in modeling and characterization of MOSFETs. In this work, we propose "Avalanche Hot-Source Method (AHSM)" for simple separated extraction of $R_S$ and $R_D$ in a single device. In AHSM, the high field region near the drain works as a new source for abundant carriers governing the current-voltage relationship in the MOSFET at high drain bias. We applied AHSM to n-channel MOSFETs as single-finger type with different channel width/length (W/L) combinations and verified its usefulness in the extraction of $R_S$ and $R_D$. We also confirmed that there is a negligible drift in the threshold voltage ($V_T$) and the subthreshold slope (SSW) even after application of the method to devices under practical conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5078-5094
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• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• Journal of the Korean Society of Radiology
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• v.6 no.3
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• pp.167-171
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• 2012

In this paper, we present an algorithm to extract features about disease region in digital stomach images. For feature extraction, DCT coefficients of gastrointestinal imaging matrix was obtained. DCT coefficent matrix is concentrated energy in low frequency region, we were extracted 128 feature parameters in low frequency region. Extracted feature parameters can using for differential compression of PACS and, can using for input parameter in CAD.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.68-72
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• 1999

The choice of parameters from various lip information and the robustness of extracting lip parameters play important roles in the performance of bimodal speech recognition system. In this paper, lip parameters are extracted by using an automatic extraction algorithm and inner lip parameters effect on the recognition rate more than outer lip parameters. Compared with a manual extraction algorithm, the automatic extraction method is evaluated about its robustness.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.321-339
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• 2017

In this work, we are interested in the extraction of areas of interest from satellite images by introducing a MO-TRIBES/OC-SVM approach. The One-Class Support Vector Machine (OC-SVM) is based on the estimation of a support that includes training data. It identifies areas of interest without including other classes from the scene. We propose generating optimal training data using the Multi-Objective TRIBES (MO-TRIBES) to improve the performances of the OC-SVM. The MO-TRIBES is a parameter-free optimization technique that manages the search space in tribes composed of agents. It makes different behavioral and structural adaptations to minimize the false positive and false negative rates of the OC-SVM. We have applied our proposed approach for the extraction of earthquakes and urban areas. The experimental results and comparisons with different state-of-the-art classifiers confirm the efficiency and the robustness of the proposed approach.