• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.449-455
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• 2004

This paper presents a novel defect detection method for one chip RF front end with fault detection circuits using input matching measurement. We present a BIST circuit using 40.25{\mu}m$ CMOS technology. We monitor the input transient voltage of the RF front end to differentiate faulty and fault-free RF front end. Catastrophic as well as parametric variation fault models are used to simulate the faulty response of the RF front end. This technique has several advantages with respect to the standard approach based on current test stimulus and frequency domain measurement. Because DUT and fault detection circuits are implemented in the same chip, this test technique only requires use of digital voltmeter (RMS meter) and RF voltage source generator for simpleand inexpensive testing.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1179-1192
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• 2018

In order to detect the Antarctic Polar Front (PF) among the main fronts in the Southern Ocean, this study is based on the combinations of satellite-based sea surface temperature (SST) and height (SSH) observations. For accurate PF detection, we classified the signals as front or non-front grids based on the Bayesian decision theory from daily SST and SSH datasets, and then spatio-temporal synthesis has been performed to remove primary noises and to supplement geographical connectivity of the front grids. In addition, sea ice and coastal masking were employed in order to remove the noise that still remains even after performing the processes and morphology operations. Finally, we selected only the southernmost grids, which can be considered as fronts and determined as the monthly PF by a linear smoothing spline optimization method. The mean positions of PF in this study are very similar to those of the PFs reported by the previous studies, and it is likely to be well represents PF formation along the bottom topography known as one of the major influences of the PF maintenance. The seasonal variation in the positions of PF is high in the Ross Sea sector (${\sim}180^{\circ}W$), and Australia sector ($120^{\circ}E-140^{\circ}E$), and these variations are quite similar to the previous studies. Therefore, it is expected that the detection approach for the PF position applied in this study and the final composite have a value that can be used in related research to be carried out on the long term time-scale.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.776-779
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• 2004

A RF front-end module is designed for GIS(Gas Insulated Substation) which is employed for effective and efficient very high voltage transmission. Major advantage of the unit is improved PO detection sensitivity through minimizing the effect of surrounding interference signals, which is achieved by controlling the gain and the selection of the frequency band, for the precise detection of any UHF PO (Partial Discharge) disturbance occurred in the GIS due to some unwanted problems. For the development of the module, various switches for providing selected signal paths, wideband LNA, 3 BPF for selecting detection frequency band, and video detector are designed, fabricated and measured on 1 mm FR4 substrate with various RF components. The detection sensitivity of the unit was <-60 dBm that is sufficient to detect UHF PO signals as low as 1 pC. It is believed that the value is enough to detect the signal occurred in GIS.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.333-341
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• 2018

In this paper, we propose an algorithm to automatically detect gait's joints. The proposed method classifies gait's types into front gait and flank gait so as to automatically detect gait's joints. And then according to classified types, the proposed applies joint extracting algorithm to input images. Firstly, we split input images into foreground image using difference images of Hue and gray-scale image of input and background one and extract gait's object. The proposed method classifies gaits into front gait and flank gait using ratio of Face's width to torso's width. Then classified gait's type, joints are detected 10 at front gait and detected 7~8 at flank gait. The proposed method is applied to the camera's input and the result shows that the proposed method automatically extracts joints.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1135-1142
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• 2007

Front-projection type displays make it difficult to apply traditional skin color detection for human hand because the projected beam not only reaches to the screen but also to the user's hand. This paper solves this problem by modeling the distortion between original image and its final camera input. Our approach improves hand detection rate by modeling of interference effect among color channels and of intra-frame intensity and also by introducing adaptive threshold for color difference in skin region.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• Journal of the military operations research society of Korea
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• v.14 no.1
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• pp.53-62
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• 1988

A patroller is to protect a patrol area with a certain length front D by proceeding at constant speed on courses parallel to the patrol front, traveling back and forth between area boundaries and reversing course at each area boundary. Transitors enter the area uniformly distributed across the patrol front on a course perpendicular to the patrol front. Any transitor that closes the patroller to within his sweep radius R is detected. This paper use plane trigonometry to derive the theoretical probailities of detection and develop a Monte Carlo computer simulation Model.

• Journal of the military operations research society of Korea
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• v.13 no.2
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• pp.53-62
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• 1987

A patroller is to protect a patrol area with a certain length front D by proceeding at constant speed on courses parallel to the patrol front, traveling back and forth between area boundaries and reversing course at each area boundary. Transitors enter the area uniformly distributed across the patrol front on a course perpendicular to the patrol front. Any transitor that closes the patroller to within his sweep radius R is detected. This paper use plane trigonometry to derive the theoretical probailities of detection and develop a Monte Carlo computer simulation Model.

• Journal of electromagnetic engineering and science
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• v.12 no.4
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• pp.234-239
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• 2012

This paper presents a compact K-band Doppler radar sensor for human vital signal detection that uses a radar configuration with only single coupler. The proposed radar front-end configuration can reduce the chip size and the additional RF power loss. The radar front-end IC is composed of a Lange coupler, VCO, and single balanced mixer. The oscillation frequency of the VCO is from 27.3 to 27.8 GHz. The phase noise of the VCO is -91.2 dBc/Hz at a 1 MHz offset frequency, and the output power is -4.8 dBm. The conversion gain of the mixer is about 11 dB. The chip size is $0.89{\times}1.47mm^2$. The compact Ka-band Doppler radar system was developed in order to demonstrate remote human vital signal detection. The radar system consists of a Ka-band Doppler radar module with a $2{\times}2$ patch array antenna, baseband signal conditioning block, DAQ system, and signal processing program. The front-end module size is $2.5{\times}2.5cm^2$. The proposed radar sensor can properly capture a human heartbeat and respiration rate at the distance of 50 cm.