• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.560-563
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• 2001

We made a HTTP server using 8 bit microprocessor. It was TMP84C015 which applied a 180 core and RTL8019AS was installed for an ethernet physical layer. Assembly language was used to optimize a performance of the MPU, to overcome an restriction of memory size and to maximize the throughput of packet using TCP, UDP, IP, ICMP and ARP protocol. We used LabVIEW to verified the each protocol on the client side.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10b
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• pp.953-956
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• 2000

최근들어 정보가전. 휴대 통신 기기. 셋탑박스 등의 Embedded 시스템이 개발됨에 따라 이를 운영할 실시간 운영체제의 필요성이 절실히 요구되고 있으며, 여기에 사용될 파일 시스템이 필요하게 되었다. 그러나, 이런 Embedded 시스템의 특성상 전원이 꺼진 상태에서도 데이터를 보관하기 위하여 플래시 메모리를 이용한 파일 시스템이 필요하며, 본 논문에서는 이런 실시간 운영체제를 위한 플래시 파일시스템을 설계하고 구현한다. 또한 효율적인 플래시 메모리 접근을 위해 플래시 메모리 관리자를 구현하고, 오류 복구를 위한 효율적인 복구 알고리즘을 제안한다. 마지막으로 순차적 파일 쓰기와 무작위 파일 쓰기 실험을 통해 본 논문의 플래시 파일 시스템의 성능을 평가한다.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2672-2674
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• 2003

In this paper, web based monitoring systems are implemented using embedded systems. A parallel port connecting parallel I/O is controlled via HTTP protocol and web browser program. HTTP protocol is ported into Linux. A micro web server program and port control program installed on-board memory using CGI to be accessed by web browser. Experimental results of proposed web based monitoring systems can be used in automation systems and remote distributed control systems via internet using web browser.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.792-794
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• 2018

본 연구에서는 일반적으로 R,G,B 색 평면의 높은 상관관계를 이용하여 컬러 복원을 시도하던 기존의 방법의 문제점을 정의하고, DCGAN을 활용한 디모자이킹에 관한 연구를 소개한다. 약 2000장의 $256{\times}256$ 이미지를 학습데이터를 이용하였다. 보다 나은 결과를 위하여 R,G,B 색상 채널에 따라 각각의 네트워크를 구성하고 학습하였다. 제안 방법은 Intel Core i7-7770 CPU(3.60GHz), 16GB Memory,NVIDIA GeForce GTX1080Ti 구성의 Laptop에서 진행하였고, 평균 PSNR 22.5dB 정도의 성능을 보인다.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.27-31
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• 2005

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.179-186
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• 2021

For stereo matching based on deep learning, the design of network structure is crucial to the calculation of matching cost, and the time-consuming problem of convolutional neural network in image processing also needs to be solved urgently. In this paper, a method of stereo matching using sparse loss volume in parallax dimension is proposed. A sparse 3D loss volume is constructed by using a wide step length translation of the right view feature map, which reduces the video memory and computing resources required by the 3D convolution module by several times. In order to improve the accuracy of the algorithm, the nonlinear up-sampling of the matching loss in the parallax dimension is carried out by using the method of multi-category output, and the training model is combined with two kinds of loss functions. Compared with the benchmark algorithm, the proposed algorithm not only improves the accuracy but also shortens the running time by about 30%.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.63-69
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• 2019

Recently, ultra-low latency flash storage devices such as Z-SSD and Optane SSD were introduced with the significant technological improvement in the storage devices which provide much faster response time than today's other NVMe SSDs. With such ultra-low latency, $10{\mu}s$, storage devices the cost of context switch could be an overhead during interrupt-driven I/O completion process. As an interrupt-driven I/O completion process could bring an interrupt handling overhead, polling or hybrid-polling for the I/O completion is known to perform better. In this paper, we analyze tail latency problem in a polling process caused by process scheduling in data center environment where multiple applications run simultaneously under one system and we introduce our adaptive polling selection technique which dynamically selects efficient processing method between two techniques according to the system's conditions.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.1
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• pp.11-18
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• 2019

In this paper, we consider interferences from other automobile pulse radars using same frequency spectrum. In order to eliminate the interference, we propose the PN code modulation method. This method uses the cross-correlation between PN codes with different seed. The ROC performance is used for comparing the proposed detector to conventional method. And the proposed detector can decide the present or absent of targets and measure the range of the targets by using memory buffer of range gate. Especially, we use false alarm probability for all range gates. That is the false alarm if in any one range gate the false alarm occurs. From the simulation result, we can see that the proposed detector with using PN code is not influenced by interferences.