{"created":"2024-06-24T04:54:30.418647+00:00","id":2000835,"links":{},"metadata":{"_buckets":{"deposit":"ef156b01-96f7-4fba-a6d9-6f797645231b"},"_deposit":{"created_by":14,"id":"2000835","owner":"14","owners":[14],"pid":{"revision_id":0,"type":"depid","value":"2000835"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:02000835","sets":["8:9"]},"author_link":["35482"],"control_number":"2000835","item_21_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"11","bibliographicPageStart":"1","bibliographicVolumeNumber":"44","bibliographic_titles":[{"bibliographic_title":"HODIC Circular","bibliographic_titleLang":"en"}]}]},"item_21_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"自己参照型ホログラフィックメモリ（SR-HDS）は，ホログラフィックメモリ（HDS）の実装形態の一つであり，参照光を用いないことを特長とする．参照光を用いないため光学系が安定ではあるが，設計の自由度が低いという側面もある．このことは，SR-HDSの記録再生品質を向上させるという目的において，光学設計によるアプローチには限界があることを示唆している．そこで本研究では，近年注目されている深層学習を用いるアプローチに着目する．具体的には，記録再生光の位相分布の設計，再生データページのデノイズおよびブロック符号の復号などに深層学習を適用することができると期待される．さらに我々は，深層学習に必要な一部の演算をSR-HDSに記録されているホログラムと少しの電子処理に担わせる実装方法，つまり，深層学習の機能を内在するSR-HDSの確立を目指している．本稿では，SR-HDSとSR-HDSの光学系を用いた深層ニューラルネットワーク（DNN）ハードウェアの実装方法である自己参照型ホログラフィック深層ニューラルネットワーク（SR-HDNN）を紹介する．最後にそれらの融合に対する期待と展望を述べる．","subitem_description_language":"ja","subitem_description_type":"Abstract"},{"subitem_description":"Self-referential holographic data storage (SR-HDS) is one of the implementations of holographic data storage (HDS), and does not require a reference light which is required in conventional HDS systems including two-beam and collinear geometries. By this feature, the optical system of SR-HDS is enabled to be stable and compact, however the degree of freedom of optical design is limited. This means that the significant improvement of the recording qualities by optical design approach is not expected. Therefore, we have focused on approaches using deep learning which have been attracting attention in recent years. For example, we expect to use deep learning-based approaches in design of the phase pattern used in recording and reading processes, denoising of the reconstructed datapages, and decoding block codes. In this paper, we introduce SR-HDS and how to implement deep neural network (DNN) hardware by the same optical system as SR-HDS, which is named self-referential holographic deep neural network (SR-HDNN). Finally, we discuss how to establish SR-HDS internalizing deep learning capabilities.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_21_description_5":{"attribute_name":"備考","attribute_value_mlt":[{"subitem_description":"HODIC2024年第1回研究会，2024年3月25日，千葉県千葉市（ハイブリッド）","subitem_description_language":"ja","subitem_description_type":"Other"}]},"item_21_publisher_7":{"attribute_name":"出版社","attribute_value_mlt":[{"subitem_publisher":"日本光学会","subitem_publisher_language":"ja"}]},"item_21_relation_38":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://hodic.org/circular/index.html","subitem_relation_type_select":"URI"}}]},"item_21_select_59":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_21_version_type_58":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_ab4af688f83e57aa","subitem_version_type":"AM"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高林, 正典","creatorNameLang":"ja"},{"creatorName":"Takabayashi, Masanori","creatorNameLang":"en"}],"familyNames":[{},{}],"givenNames":[{},{}],"nameIdentifiers":[{"nameIdentifier":"35482","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"70636000","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/nrid/1000070636000"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2024-06-24"}],"filename":"neuro_69.pdf","filesize":[{"value":"1.1 MB"}],"format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://kyutech.repo.nii.ac.jp/record/2000835/files/neuro_69.pdf"},"version_id":"567c80a4-50c3-419b-be2a-adc0ba8d2a8a"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"自己参照型ホログラフィックメモリ","subitem_subject_scheme":"Other"},{"subitem_subject":"自己参照型ホログラフィック深層ニューラルネットワーク","subitem_subject_scheme":"Other"},{"subitem_subject":"深層学習","subitem_subject_scheme":"Other"},{"subitem_subject":"Self-referential holographic data storage","subitem_subject_scheme":"Other"},{"subitem_subject":"Self-referential holographic deep neural network","subitem_subject_scheme":"Other"},{"subitem_subject":"Deep learning","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"自己参照型ホログラフィックメモリにおける深層学習の利用と実装","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"自己参照型ホログラフィックメモリにおける深層学習の利用と実装","subitem_title_language":"ja"},{"subitem_title":"Use and implementation of deep learning in self-referential holographic data storage","subitem_title_language":"en"}]},"item_type_id":"21","owner":"14","path":["9"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-06-24"},"publish_date":"2024-06-24","publish_status":"0","recid":"2000835","relation_version_is_last":true,"title":["自己参照型ホログラフィックメモリにおける深層学習の利用と実装"],"weko_creator_id":"14","weko_shared_id":-1},"updated":"2024-06-24T05:18:05.803728+00:00"}