• Title/Summary/Keyword: Integer Operation

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An Efficient Error Compensation Method for Thumbnail Extraction in H.264/AVC Bitstreams (H.264/AVC 비트스트림으로부터 썸네일 추출 시 효율적인 오차 보상 방법)

  • Yoon, Myung-Keun;Lee, Yeo-Song;Sohn, Chae-Bong;Park, Ho-Chong;Ahn, Chang-Beom;Oh, Seoung-Jun
    • Journal of Broadcast Engineering
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    • v.13 no.5
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    • pp.622-635
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    • 2008
  • Recently, high definition media services like HDTV and IPTV are growing. A fast reduced-size image extracting method is required to meet what those services require. Conventional DC image extracting methods, however, can't be applied to H.264/AVC streams since a spatial domain prediction scheme is adopted in H.264/AVC intra mode. To solve this problem, a thumbnail extraction method in H.264/AVC was proposed. However, the method has mismatch problem which was caused by round-off operation in intra prediction and mismatch between integer and floating point calculation. In this paper, we propose an error compensation method for extracting thumbnail directly in H.264/AVC bitstreams. The compensation method introduces the mismatch problem in thumbnail extraction and presents compensation values. Through the implementation and performance evaluation, proposed method compensated round-off error efficiently in D1 and HD sequences while the additional extraction time is negligible.

Dual-Band VCO using Composite Right/Left-Handed Transmission Line and Tunable Negative Resistanc based on Pin Diode (Composite Right/Left-Handed 전송 선로와 Pin Diode를 이용한 조절 가능한 부성 저항을 이용한 이중 대역 전압 제어 발진기)

  • Choi, Jae-Won;Seo, Chul-Hun
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.44 no.12
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    • pp.16-21
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    • 2007
  • In this paper, the dual-band voltage-controled oscillator (VCO) using the composite right/left-handed (CRLH) transmission line (TL) and the tunable negative resistance based on the fin diode is presented. It is demonstrated that the CRLH TL can lead to metamaterial transmission line with the dual-band tuning capability. The dual-band operation of the CRLH TL is achieved by the frequency offset and the phase slope of the CRLH TL, and the frequency ratio of the two operating frequencies can be a non-integer. Each frequency band of VCO has to operate independently, so we have used the tunable negative resistance based on the pin diode. When the forward bias has been into the pin diode, the phase noise of VCO is $-108.34\sim-106.67$ dBc/Hz @ 100 kHz in the tuning range, $2.423\sim2.597$ GHz, whereas when the reverse bias has been fed into the pin diode, that of VCO is $-114.16\sim-113.33$ dBc/Hz @ 100 kHz in the tuning range, $5.137\sim5.354$ GHz.

Feasibility Study on Integration of SSR Correction into Network RTK to Provide More Robust Service

  • Lim, Cheol-Soon;Park, Byungwoon;Kim, Dong-Uk;Kee, Chang-Don;Park, Kwan-Dong;Seo, Seungwoo;So, Hyoungmin;Park, Junpyo
    • Journal of Positioning, Navigation, and Timing
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    • v.7 no.4
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    • pp.295-305
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    • 2018
  • Network RTK is a highly practical technology that can provide high positioning accuracy at levels between cm~dm regardless of user location in the network by extending the available range of RTK using reference station network. In particular, unlike other carrier-based positioning techniques such as PPP, users are able to acquire high-accuracy positions within a short initialization time of a few or tens of seconds, which increases its value as a future navigation system. However, corrections must be continuously received to maintain a high level of positioning accuracy, and when a time delay of more than 30 seconds occurs, the accuracy may be reduced to the code-based positioning level of meters. In case of SSR, which is currently in the process of standardization for PPP service, the corrections by each error source are transmitted in different transmission intervals, and the rate of change of each correction is transmitted together to compensate the time delay. Using these features of SSR correction is expected to reduce the performance degradation even if users do not receive the network RTK corrections for more than 30 seconds. In this paper, the simulation data were generated from 5 domestic reference stations in Gunwi, Yeongdoek, Daegu, Gimcheon, and Yecheon, and the network RTK and SSR corrections were generated for the corresponding data and applied to the simulation data from Cheongsong reference station, assumed as the user. As a result of the experiment assuming 30 seconds of missing data, the positioning performance compensating for time delay by SSR was analyzed to be horizontal RMS (about 5 cm) and vertical RMS (about 8 cm), and the 95% error was 8.7 cm horizontal and 1cm vertical. This is a significant amount when compared to the horizontal and vertical RMS of 0.3 cm and 0.6 cm, respectively, for Network RTK without time delay for the same data, but is considerably smaller compared to the 0.5 ~ 1 m accuracy level of DGPS or SBAS. Therefore, maintaining Network RTK mode using SSR rather than switching to code-based DGPS or SBAS mode due to failure to receive the network RTK corrections for 30 seconds is considered to be favorable in terms of maintaining position accuracy and recovering performance by quickly resolving the integer ambiguity when the communication channel is recovered.

YOLO Model FPS Enhancement Method for Determining Human Facial Expression based on NVIDIA Jetson TX1 (NVIDIA Jetson TX1 기반의 사람 표정 판별을 위한 YOLO 모델 FPS 향상 방법)

  • Bae, Seung-Ju;Choi, Hyeon-Jun;Jeong, Gu-Min
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.5
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    • pp.467-474
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    • 2019
  • In this paper, we propose a novel method to improve FPS while maintaining the accuracy of YOLO v2 model in NVIDIA Jetson TX1. In general, in order to reduce the amount of computation, a conversion to an integer operation or reducing the depth of a network have been used. However, the accuracy of recognition can be deteriorated. So, we use methods to reduce computation and memory consumption through adjustment of the filter size and integrated computation of the network The first method is to replace the $3{\times}3$ filter with a $1{\times}1$ filter, which reduces the number of parameters to one-ninth. The second method is to reduce the amount of computation through CBR (Convolution-Add Bias-Relu) among the inference acceleration functions of TensorRT, and the last method is to reduce memory consumption by integrating repeated layers using TensorRT. For the simulation results, although the accuracy is decreased by 1% compared to the existing YOLO v2 model, the FPS has been improved from the existing 3.9 FPS to 11 FPS.