• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2693-2695
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• 2001

In this paper, we present an adaptive image filter using genetic algorithm. The filter is robust to the characteristic variance of image and noise, by evolving the parameter and combination of image preprocessors properly. And we have adopted adaptive mutation strategy, which use different mutation rate for specific region of chromosome. The filter is implemented on FPGA board and controlled by host PC.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.111-114
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• 1996

Mucociliary transport is one of the essential defensive functions of the airway mucosa. In this paper, the objective and quantitative method of measuring CBF(Ciliary Beat Frequency) was developed based on the image processing method. Microscopic ciliary images are acquired through image processing board inside PC, and data necessary for the FFT(Fast Fourier Transform) analysis are extracted. By means of FFT analysis, maximum peak frequencies are found in each divided block of a whole acquired image. Finally using these frequencies, we compose a frequency map showing the spatial distribution of CBF's.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1156-1158
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• 2003

The FGI is an integral component of the MSC test equipment and is situated in a slot along the ISA bus of the test equipment PC. The main function of the FGI is an interface between the MSC image data via hotlink interface and Frame Grabber. The FGI has two independent receiving channels that allow the board to receive image data arriving. The FGI also includes two transmission channels with hotlink transmitters. Additionally, the FGI is capable of generating digital video test patterns to test the NUC.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1355-1360
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• 2005

In this paper, we approach the problem of image filter design automation using a kind of intrinsic evolvable hardware architecture. For the purpose of implementing the intrinsic evolution process in a common FPGA chip and evolving a complicated digital circuit system-image filter, the design automation system employs the reconfigurable circuit architecture as the reconfigurable component of the EHW. The reconfigurable circuit architecture is inspired by the Cartesian Genetic Programming and the functional level evolution. To increase the speed of the hardware evolution, the whole evolvable hardware system which consists of evolution algorithm unit, fitness value calculation unit and reconfigurable unit are implemented by a commercial FPGA chip. The Celoxica RC1000 card which is fitted with a Xilinx Virtex xcv2000E FPGA chip is employed as the experiment platform. As the result, we conclude the terms of the synthesis report of the image filter design automation system and hardware evolution speed in the Celoxica RC1000 card. The evolved image filter is also compared with the conventional image filter form the point of filtered image quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.43-49
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• 2021

A small infrared image homing system is a tracking system that has an infrared image sensor that identifies a target through the day and night infrared image processing of the target on the ground and searches for and detects the target with respect to the main target. This paper describes the development of a board equipped with a high-speed CPU and FPGA (Field Programmable Gate Array) to identify target through real-time image processing by acquiring target information through infrared image. We propose a CPU-FPGA combining architecture for CPU and FPGA selection and video signal processing, and also describe a controller design using FPGA to control infrared sensor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.577-580
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• 2005

Surface quality of a micro-processed sample with laser has been investigated by using of scanning confocal microscope(SCM) and atomic force microscope(AFM). Samples are bump electrodes and ITO glass of LCD module used in a mobile phone and a wafer surface scribed by UV laser. A image of $140\times120{\mu}m^2$ is obtained within 1 second by SCM because scan speed of a x-axis and y-axis are 1kHz and 1Hz, respectively. AFM is able to measure correctly hight and width of ITO and scribing depth and width of a wafer with a resolution less than 300 . However, the scan speed is slow and it is difficult to distinguish a surface composed of different nm kinds of materials. Results show that SCM is preferable to obtain a image of a sample composed of different kinds of material than AFM because the intensity of a reflected light from surface is different from each material.

• Journal of the Korean Society of Visualization
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• v.14 no.2
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• pp.52-60
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• 2016

The Passive Auxiliary Feedwater System(PAFS) is one of the advanced safety concepts adopted in the Advanced Power Reactor Plus(APR+). To validate the operational performance of the PAFS, detailed understanding of a boiling heat transfer on horizontal tube outside is of great importance. Especially, in the mechanistic boiling heat transfer model, it is important to visualize the phenomena but there are some limitations with conventional experimental approaches. In the present study, we devised a heater based on the Flexible Printed Circuit Board (FPCB) for a more comprehensive visualization and subsequently, a digital image processing technique for the bubble motion measurement was established. Using the measurement technique, important parameters of the nucleate boiling are analyzed.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.4 s.181
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• pp.52-59
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• 2006

Surface qualities of a micro-processed sample with a pulse laser have been investigated by making use of scanning confocal microscope(SCM) and atomic force microscope(AFM). Samples are bump electrodes and ITO glass of LCD module used in a mobile phone and a wafer surface scribed by UV laser. A image of $140{\times}120{\mu}m^2$ is obtained within 1 second by SCM because scan speed of a x-axis and y-axis are 1kHz and 1Hz, respectively. AFM is able to correctly measure the hight and width of ITO, and scribing depth and width of a wafer with a resolution less than 300nm. However, the scan speed is slow and it is difficult to distinguish a surface composed of different kinds of materials. Results show that SCM is preferable to obtain a image of a sample composed of different kinds of material than AFM because the intensity of a reflected light from the surface is different for each material.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.166-169
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• 2017

Currently, studies using Deep Learning are actively carried out showing good results in many fields. A template matching method is mainly used to recognize parts mounted on PCB(Printed Circuit Board). However, template matching should have multiple templates depending on the shape, orientation and brightness. And it takes long time to perform matching because it searches for the entire image. And there is also a disadvantage that the recognition rate is considerably low. In this paper, we use the Faster-RCNN method for recognizing PCB components as machine learning for classifying several objects in one image. This method performs better than the template matching method, execution time and recognition.