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        <identifier>oai:ipsj.ixsq.nii.ac.jp:00214026</identifier>
        <datestamp>2025-01-19T16:55:52Z</datestamp>
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          <dc:title>Digital Microfluidic Biochipにおけるエラー同士の相殺を利用したエラー訂正手法</dc:title>
          <dc:title>Fault-Tolerant Sample Preparation on DMFBs by Canceling the Eﬀect of Errors by Other Errors</dc:title>
          <dc:creator>和田, 有史</dc:creator>
          <dc:creator>山下, 茂</dc:creator>
          <dc:creator>Yuji, Wada</dc:creator>
          <dc:creator>Shigeru, Yamashita</dc:creator>
          <dc:subject>Stochastic Computingおよびバイオチップ</dc:subject>
          <dc:description>近年研究されているバイオチップの中に，DMFB (Digital MicroFluidic Biochip) という種類のバイオチップがある．DMFB は試薬と緩衝液を１：１の割合で希釈操作を行い目標の濃度の液滴を生成する装置である．DMFB では，液滴分割時に，均等に分割されない可能性がある．対策として，すべてのノードでエラーを検査する手法が存在する．しかし，この手法では，センサが多く必要で，エラー発生率が高い場合，再希釈回数が増加する．本論文では，エラー同士の相殺を用いて検査数を減らすことによって，より効率的なエラー訂正を行う手法を提案する． 結果として，既存の手法と比較して再希釈回数と最終液滴の濃度の誤差の両方の値を改善することを確認できた．</dc:description>
          <dc:description>On a digital microfluidic biochip (DMFB), we can perform a sequence of (1:1) mix-split operations to generate a desired concentration factor of a sample, which is called sample preparation. A major error source in this process is due to unbalanced splitting. There has been proposed a method to check each splitting and redo it if there is an unacceptable error. However, this error recovery technique may require many split operations if the error probability is relatively high. To address this problem, this paper proposes an error recovery method that does not check all split operations. To do so, our method utilizes a fact that one error may “cancel” another error.  Our preliminary experimental results show that our method can decrease the number of re-split operations by more than 20% than the previous method.</dc:description>
          <dc:description>technical report</dc:description>
          <dc:publisher>情報処理学会</dc:publisher>
          <dc:date>2021-11-24</dc:date>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>研究報告システムとLSIの設計技術（SLDM）</dc:identifier>
          <dc:identifier>17</dc:identifier>
          <dc:identifier>2021-SLDM-196</dc:identifier>
          <dc:identifier>1</dc:identifier>
          <dc:identifier>6</dc:identifier>
          <dc:identifier>2188-8639</dc:identifier>
          <dc:identifier>AA11451459</dc:identifier>
          <dc:identifier>https://ipsj.ixsq.nii.ac.jp/record/214026/files/IPSJ-SLDM21196017.pdf</dc:identifier>
          <dc:language>jpn</dc:language>
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