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dc.contributor.authorLiu, Fangzheng
dc.contributor.authorDementyev, Artem
dc.contributor.authorWicaksono, Irmandy
dc.contributor.authorParadiso, Joseph
dc.date.accessioned2025-12-09T22:58:55Z
dc.date.available2025-12-09T22:58:55Z
dc.date.issued2025-09-27
dc.identifier.isbn979-8-4007-2036-9
dc.identifier.urihttps://hdl.handle.net/1721.1/164254
dc.descriptionUIST Adjunct ’25, Busan, Republic of Koreaen_US
dc.description.abstractThis paper introduces EmbedNet, a method for integrating dense sensor networks into casting objects. With EmbedNet, sensor nodes are seamlessly incorporated into casting objects during fabrication. The process involves extruding base materials like silicone rubber or liquid plastic and a custom-designed sensor strip using a hand-held extruder into a mold tailored to specific applications. The base material mixes with the sensor strip in the mold, and upon curing, the result is an object with a defined shape housing a sensor network. EmbedNet employs a small Host node to access sensor data from all nodes on the strip. Each sensor node is self-contained and provides status indications through an onboard RGB LED. The Host connects with all sensor nodes using just three wires: power, ground, and data. This one-wire communication is facilitated through a custom-designed software serial port for each sensor node. The paper showcases various applications of EmbedNet, including wearables, home sensing, and entertainment devices.en_US
dc.publisherACM|The 38th Annual ACM Symposium on User Interface Software and Technologyen_US
dc.relation.isversionofhttps://doi.org/10.1145/3746058.3758986en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleExperiencing EmbedNet: Embedding self-sensing to 3D casting objectsen_US
dc.typeArticleen_US
dc.identifier.citationFangzheng Liu, Artem Dementyev, Irmandy Wicaksono, and Joseph A. Paradiso. 2025. Experiencing EmbedNet: Embedding self-sensing to 3D casting objects. In Adjunct Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct '25). Association for Computing Machinery, New York, NY, USA, Article 35, 1–4.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Responsive Environments Groupen_US
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2025-10-01T07:50:18Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2025-10-01T07:50:19Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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