• Title/Summary/Keyword: Harmonic coding

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Implementation of a RF transceiver for WRAN System Using Cognitive Radio Technology in TV Whitespace Band (Cognitive Radio 기술 기반의 TV Whitespace대역 WRAN 시스템의 RF 송.수신기 구현)

  • Min, Jun-Ki;Hwang, Sung-Ho;Kim, Ki-Hong;Park, Yong-Woon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.5A
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    • pp.496-503
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    • 2010
  • The implementation of a RF transceiver for WRAN(Wireless Regional Area Network) system based on IEEE 802.22 standard using Cognitive Radio technology is presented in this paper. A CMOS RF transceiver IC for WRAN system operates in VHF/UHF(54~862MHz) broadband, and employs dual-path direct-conversion configuration and the in-band harmonic distortions are effectively suppressed by exploiting the dual-path direct conversion architecture. For 64QAM(3/4 coding rate) OFDM signal, an EVM of <-31.4dB(2.7%) has been achieved at 10dBm off-chip PA output power and the total chip area with pads is 12.95 mm2. The experimental results show that the proposed CMOS RF transceiver IC has perfect performance for WRAN system based on TDD(Time Division Duplex) mode.

Evaluation of Video Codec AI-based Multiple tasks (인공지능 기반 멀티태스크를 위한 비디오 코덱의 성능평가 방법)

  • Kim, Shin;Lee, Yegi;Yoon, Kyoungro;Choo, Hyon-Gon;Lim, Hanshin;Seo, Jeongil
    • Journal of Broadcast Engineering
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    • v.27 no.3
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    • pp.273-282
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    • 2022
  • MPEG-VCM(Video Coding for Machine) aims to standardize video codec for machines. VCM provides data sets and anchors, which provide reference data for comparison, for several machine vision tasks including object detection, object segmentation, and object tracking. The evaluation template can be used to compare compression and machine vision task performance between anchor data and various proposed video codecs. However, performance comparison is carried out separately for each machine vision task, and information related to performance evaluation of multiple machine vision tasks on a single bitstream is not provided currently. In this paper, we propose a performance evaluation method of a video codec for AI-based multi-tasks. Based on bits per pixel (BPP), which is the measure of a single bitstream size, and mean average precision(mAP), which is the accuracy measure of each task, we define three criteria for multi-task performance evaluation such as arithmetic average, weighted average, and harmonic average, and to calculate the multi-tasks performance results based on the mAP values. In addition, as the dynamic range of mAP may very different from task to task, performance results for multi-tasks are calculated and evaluated based on the normalized mAP in order to prevent a problem that would be happened because of the dynamic range.

Variable Rate IMBE-LP Coding Algorithm Using Band Information (주파수대역 정보를 이용한 가변률 IMBE-LP 음성부호화 알고리즘)

  • Park, Man-Ho;Bae, Geon-Seong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.38 no.5
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    • pp.576-582
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    • 2001
  • The Multi-Band Excitation(MBE) speech coder uses a different approach for the representation of the excitation signal. It replaces the frame-based single voiced/unvoiced classification of a classical speech coder with a set of such decision over harmonic intervals in the frequency domain. This enables each speech segment to be a mixture of voiced and unvoiced, and improves the synthetic speech quality by reducing decision errors that might occur on the frame-based single voiced and unvoiced decision process when input speech is degraded with noise. The IMBE-LP, improved version of MBE with linear prediction, represents the spectral information of MBE model with linear prediction coefficients to obtain low bit rate of 2.4 kbps. In this Paper, we proposed a variable rate IMBE-LP vocoder that has lower bit rate than IMBE-LP without degrading the synthetic speech quality. To determine the LP order, it uses the spectral band information of the MBE model that has something to do with he input speech's characteristics. Experimental results are riven with our findings and discussions.

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