• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.385-391
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• 2015

In this paper, we propose a new method to infiltrate $Fe_3O_4$-nanoparticles into a porous silicon film and a monitoring technique to detect packing density of nanoparticles within the film. Recently, research to use porous silicon as a drug carrier or a new functional sensor material by infiltrating $Fe_3O_4$-nanoparticles has been extensively performed. However, it is still necessary to enhance the packing density and to develop a monitoring technique to detect the packing density in real time. In this light, we forcibly injected a nanoparticle solution into a rugate-structured free-standing porous silicon (FPS) film by applying a pressure difference between the two sides of the film. We found that the packing density by the pressure-infiltration method proposed in this paper is enhanced, relative to that by the previous diffusion method. Moreover, a continuous shift in wavelength of the rugate reflectance peak measured from the film surface was observed while the nanoparticle solution was being injected. By exploiting this phenomenon, we could qualitatively monitor the packing density of $Fe_3O_4$-nanoparticles within the FPS film with the injection volume of the nanoparticle solution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.329-334
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• 2014

This paper proposes the hardware architecture of high performance multi-mode 2D forward transform for HEVC which has same number of cycles for processing any type of four TUs and yield high throughput. In order to make the original image which has high pixel and high resolution into highly compressed image effectively, the transform technique of HEVC supports 4 kinds of pixel units, TUs and it finds the optimal mode after performs each transform computation. As the proposed transform engine uses the common computation operator which is produced by analyzing the relationship among transform matrix coefficients, it can process every 4 kinds of TU mode matrix operation with 35cycles equally. The proposed transform block was designed by Verilog HDL and synthesized by using TSMC 0.18um CMOS processing technology. From the results of logic synthesis, the maximum operating frequency was 400MHz and total gate count was 214k gates which has the throughput of 10-Gpels/cycle with the $4k(3840{\times}2160)@30fps$ image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1486-1491
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• 2017

In this paper, we propose a private multimedia caching scheme based on wireless local area network (WLAN) to improve the quality of service for high capacity and high quality multimedia streaming services which are recently increasing and to reduce the traffic load of core networks. The proposed caching scheme stores multimedia in the storage device mounted on WLAN APs and provides streaming services on its own without Internet connection in accordance with the request from clients. We have implemented a test network based on real commercial networks and measured the performance of the proposed caching scheme in terms of frames per second (FPS) and buffering time. According to the performance measurement results, the proposed caching scheme can reduce the average buffering time by 73.3% compared to the conventional streaming scheme. In addition, the proposed caching scheme can also improve the average FPS by 71.3% compared to the conventional streaming scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.169-177
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• 2015

This paper proposes a hardware architecture for an efficient SAO(Sample Adaptive Offset) with low area for HEVC(High Efficiency Video Coding) encoder. SAO is a newly adopted technique in HEVC as part of the in-loop filter. SAO reduces mean sample distortion by adding offsets to reconstructed samples. The existing SAO requires a great deal of computational and processing time for UHD(Ultra High Definition) video due to sample by sample processing. To reduce SAO processing time, the proposed SAO hardware architecture processes four samples simultaneously, and is implemented with a 2-step pipelined architecture. In addition, to reduce hardware area, it has a single architecture for both luma and chroma components and also uses optimized and common operators. The proposed SAO hardware architecture is designed using Verilog HDL(Hardware Description Language), and has a total of 190k gates in TSMC $0.13{\mu}m$ CMOS standard cell library. At 200MHz, it can support 4K UHD video encoding at 60fps in real time, but operates at a maximum of 250MHz.

• Toxicological Research
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• v.33 no.4
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• pp.305-313
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• 2017

Accumulating epidemiological evidence indicates that exposure to fine air pollution particles (APPs) is associated with a variety of adverse health effects. However, the exact physiochemical properties and biological toxicities of fine APPs are still not well characterized. We collected four types of fine particle (FP) (diesel exhaust particles [DEPs], natural organic combustion [NOC] ash, synthetic organic combustion [SOC] ash, and yellow sand dust [YSD]) and investigated their physicochemical properties and in vitro biological toxicity. DEPs were almost entirely composed of ultrafine particles (UFPs), while the NOC, SOC, and YSD particles were a mixture of UFPs and FPs. The main elements in the DEPs, NOC ash, SOC ash, and YSD were black carbon, silicon, black carbon, and silicon, respectively. DEPs exhibited dose-dependent mutagenicity even at a low dose in Salmonella typhimurium TA 98 and 100 strains in an Ames test for genotoxicity. However, NOC, SOC, and YSD particles did not show any mutagenicity at high doses. The neutral red uptake assay to test cell viability revealed that DEPs showed dose-dependent potent cytotoxicity even at a low concentration. The toxicity of DEPs was relatively higher than that of NOC, SOC, and YSD particles. Therefore, these results indicate that among the four FPs, DEPs showed the highest in vitro biological toxicity. Additional comprehensive research studies such as chemical analysis and in vivo acute and chronic inhalation toxicity tests are necessary to determine and clarify the effects of this air contaminant on human health.

• Journal of the Korea Computer Graphics Society
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• v.25 no.4
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• pp.17-24
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• 2019

In the game industry, traditional game development way was to develop a game engine in advance before developing game content. This has the disadvantage is the high cost of developing the game engine. Further, there is a problem that the game content are not developed unless the game engine is prepared in advance. In order to solve these problems, the game industry has recently selected a method of purchasing a license for a commercial engine, which is a method for improving the game content development productivity, rather than concentrating on game engine development. In this paper, we describe a game object and the structure in Unity and Unreal Engine 4. We compared the editor interface and the supported development languages in the two game engines. We also implemented a First-Person Shooter game to compare how the player's configuration and event handling differ from the two game engines.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.44-49
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• 2021

Recently, due to COVID-19, studies have been popularly worked to apply neural network to mask wearing automatic detection system. For applying neural networks, the 1-stage detection or 2-stage detection methods are used, and if data are not sufficiently collected, the pretrained neural network models are studied by applying fine-tuning techniques. In this paper, the system is consisted of 2-stage detection method that contain MTCNN model for face recognition and ResNet model for mask detection. The mask detector was experimented by applying five ResNet models to improve accuracy and fps in various environments. Training data used 17,217 images that collected using web crawler, and for inference, we used 1,913 images and two one-minute videos respectively. The experiment showed a high accuracy of 96.39% for images and 92.98% for video, and the speed of inference for video was 10.78fps.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.748-757
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• 2021

In this paper, we propose a gaze estimation network in which eye landmark position detection and gaze direction vector estimation are integrated into one deep learning network. The proposed network uses the Stacked Hourglass Network as a backbone structure and is largely composed of three parts: a landmark detector, a feature map extractor, and a gaze direction estimator. The landmark detector estimates the coordinates of 50 eye landmarks, and the feature map extractor generates a feature map of the eye image for estimating the gaze direction. And the gaze direction estimator estimates the final gaze direction vector by combining each output result. The proposed network was trained using virtual synthetic eye images and landmark coordinate data generated through the UnityEyes dataset, and the MPIIGaze dataset consisting of real human eye images was used for performance evaluation. Through the experiment, the gaze estimation error showed a performance of 3.9, and the estimation speed of the network was 42 FPS (Frames per second).

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.39-46
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• 2023

We present an efficient packet transmission strategy for massively multiplayer online first-person shooter (MMOFPS) games using movement-adaptive packet transmission interval. The player motion in FPS games shows a wide spectrum of movement variability both in speed and orientation, where there is room for reducing the number of packets to be transmitted to the server depending on the predictability of the character's movement. In this work, the degree of variability (nonlinearity) of the player movements is measured at every packet transmission to calculate the next transmission time, which implements the adaptive transmission frequency according to the amount of movement change. Server-side prediction with a few auxiliary heuristics is performed in concert with the incoming packets to ensure reliability for synchronizing the connected clients. The comparison of our method with the previous fixed-interval transmission scheme is presented by demonstrating them using a test game environment.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.411-418
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• 2022

Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.