Reliable extraction of semantic information and rate of innovation estimation for graph signals

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buir.contributor.authorKalfa, Mert
buir.contributor.authorYetim, Sadık Yağız
buir.contributor.authorAtalik, Arda
buir.contributor.authorGök, Mehmetcan
buir.contributor.authorDuman, Tolga Mete
buir.contributor.authorArıkan, Orhan
buir.contributor.orcidKalfa, Mert|0000-0002-6462-1776
buir.contributor.orcidYetim, Sadık Yağız|0009-0001-1740-9502
buir.contributor.orcidAtalik, Arda|0000-0003-3439-7838
buir.contributor.orcidGök, Mehmetcan|0000-0001-9085-0110
buir.contributor.orcidDuman, Tolga Mete|0000-0002-5187-8660
buir.contributor.orcidArıkan, Orhan|0000-0002-3698-8888
dc.citation.epage140en_US
dc.citation.issueNumber1
dc.citation.spage119
dc.citation.volumeNumber41
dc.contributor.authorKalfa, Mert
dc.contributor.authorYetim, Sadık Yağız
dc.contributor.authorAtalik, Arda
dc.contributor.authorGök, Mehmetcan
dc.contributor.authorGe, Y.
dc.contributor.authorLi, R.
dc.contributor.authorTong, W.
dc.contributor.authorDuman, Tolga Mete
dc.contributor.authorArıkan, Orhan
dc.date.accessioned2024-03-18T06:58:00Z
dc.date.available2024-03-18T06:58:00Z
dc.date.issued2022-12-19
dc.departmentDepartment of Electrical and Electronics Engineering
dc.description.abstractSemantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.
dc.description.provenanceMade available in DSpace on 2024-03-18T06:58:00Z (GMT). No. of bitstreams: 1 Reliable_extraction_of_semantic_information_and_rate_of_innovation_estimation_for_graph_signals.pdf: 4419619 bytes, checksum: cc5115a732098bcee6d5857a233681ae (MD5) Previous issue date: 2023-01-01en
dc.identifier.doi10.1109/JSAC.2022.3221950
dc.identifier.eissn1558-0008
dc.identifier.issn0733-8716
dc.identifier.urihttps://hdl.handle.net/11693/114856
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://dx.doi.org/10.1109/JSAC.2022.3221950
dc.source.titleIEEE Journal on Selected Areas in Communications
dc.subjectSemantic signal processing
dc.subjectSemantic communications
dc.subjectSemantic graph signals
dc.subjectGoal-oriented signal processing
dc.subjectGoal-oriented communications
dc.titleReliable extraction of semantic information and rate of innovation estimation for graph signals
dc.typeArticle

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