<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2666</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>91</pageFirst>
    <pageLast>93</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Method of Toothbrush Position Measurement using AR Markers</title>
    <abstract language="eng">Appropriate daily brushing of the teeth is important for preventing oral diseases. Therefore, a personal assistant for assessing and coaching appropriate toothbrushing is needed. Herein, a three-dimensional toothbrush position measurement method using augmented reality (AR) markers is proposed. The AR markers are detected via a brushing video captured using a smartphone camera. The AR markers are installed on each surface of a dodecahedron attached at the rear end of the toothbrush. This report describes the proposed method, the resulting toothbrush position measurement accuracy, and the optimal number of markers needed for an accurate measurement of the position.</abstract>
    <parentTitle language="eng">2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech): 10-12 March 2020, Kyoto, Japan</parentTitle>
    <identifier type="doi">10.1109/LifeTech48969.2020.1570619103</identifier>
    <enrichment key="opus.import.date">2022-02-04T08:58:18+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Shuhei Hayakawa</author>
    <author>Ghassan Al-Falouji</author>
    <author>Gerald Schickhuber</author>
    <author>Roland Mandl</author>
    <author>Takahiro Yoshida</author>
    <author>Seiichiro Hangai</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AR marker</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>position measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>toothbrush</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Labor Smart Embedded Systems</collection>
  </doc>
  <doc>
    <id>2488</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Text/Language-Independent Unknown Speaker Rejection Method Using LSP Codes</title>
    <abstract language="eng">Scam phone calls pretending relatives have become a major social problem. However, current countermeasure against such scam phone calls stays only enlightenment activities although there is a limited number of technical measures in telephones. In this study, we have proposed a text-independent unknown speaker rejection method which recognizes whether a known speaker or an unknown speaker on receiving a call. In this method, we used a line spectral pair (LSP) spectrogram as the feature and a convolutional neural network (CNN) as the identification engine. From the evaluated results, the proposed method recognized known/unknown speakers with the false acceptance rate (FAR) of 13.1% in Japanese speech and 25.2% in German speech, respectively.</abstract>
    <parentTitle language="eng">IEEE 9th Global Conference on Consumer Electronics (GCCE 2020): 13-16 Oct. 2020, Kobe, Japan</parentTitle>
    <identifier type="doi">10.1109/GCCE50665.2020.9291835</identifier>
    <enrichment key="opus.import.date">2022-01-21T12:31:59+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Toru Hiyama</author>
    <author>Armin Sehr</author>
    <author>Takahiro Yoshida</author>
    <author>Seiichiro Hangai</author>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16317">Sensorik</collection>
    <collection role="institutes" number="">Labor Elektroakustik</collection>
  </doc>
</export-example>
