FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2752417686> ?p ?o ?g. }

• W2752417686 abstract "Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this study, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm---built upon belief-propagation principles---that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing." @default.
• W2752417686 created "2017-09-15" @default.
• W2752417686 creator A5009017662 @default.
• W2752417686 creator A5043511500 @default.
• W2752417686 creator A5044634366 @default.
• W2752417686 creator A5054469104 @default.
• W2752417686 date "2017-12-01" @default.
• W2752417686 modified "2023-10-16" @default.
• W2752417686 title "Heterogeneous Networked Data Recovery From Compressive Measurements Using a Copula Prior" @default.
• W2752417686 cites W132374903 @default.
• W2752417686 cites W1513531234 @default.
• W2752417686 cites W1514040736 @default.
• W2752417686 cites W1538797276 @default.
• W2752417686 cites W1542184596 @default.
• W2752417686 cites W1583589866 @default.
• W2752417686 cites W1712805121 @default.
• W2752417686 cites W1767194506 @default.
• W2752417686 cites W1985409483 @default.
• W2752417686 cites W1996718482 @default.
• W2752417686 cites W2011452458 @default.
• W2752417686 cites W2016480770 @default.
• W2752417686 cites W2020716141 @default.
• W2752417686 cites W2024105794 @default.
• W2752417686 cites W2049567982 @default.
• W2752417686 cites W2051679410 @default.
• W2752417686 cites W2071284784 @default.
• W2752417686 cites W2082029531 @default.
• W2752417686 cites W2088604742 @default.
• W2752417686 cites W2090209634 @default.
• W2752417686 cites W2095939291 @default.
• W2752417686 cites W2099641086 @default.
• W2752417686 cites W2100993827 @default.
• W2752417686 cites W2102041916 @default.
• W2752417686 cites W2104068236 @default.
• W2752417686 cites W2106407813 @default.
• W2752417686 cites W2106460483 @default.
• W2752417686 cites W2110575505 @default.
• W2752417686 cites W2119436275 @default.
• W2752417686 cites W2119667497 @default.
• W2752417686 cites W2120121938 @default.
• W2752417686 cites W2123820440 @default.
• W2752417686 cites W2127271355 @default.
• W2752417686 cites W2127295759 @default.
• W2752417686 cites W2128633079 @default.
• W2752417686 cites W2128765501 @default.
• W2752417686 cites W2129131372 @default.
• W2752417686 cites W2134074206 @default.
• W2752417686 cites W2134238238 @default.
• W2752417686 cites W2135643635 @default.
• W2752417686 cites W2135859872 @default.
• W2752417686 cites W2141588466 @default.
• W2752417686 cites W2143116731 @default.
• W2752417686 cites W2154304330 @default.
• W2752417686 cites W2162404506 @default.
• W2752417686 cites W2163402412 @default.
• W2752417686 cites W2166481425 @default.
• W2752417686 cites W2296616510 @default.
• W2752417686 cites W2399057589 @default.
• W2752417686 cites W2468117332 @default.
• W2752417686 cites W2494606451 @default.
• W2752417686 cites W2507039649 @default.
• W2752417686 cites W2565295977 @default.
• W2752417686 cites W2762625016 @default.
• W2752417686 cites W2798180542 @default.
• W2752417686 cites W2907233437 @default.
• W2752417686 cites W2963295317 @default.
• W2752417686 cites W2963322354 @default.
• W2752417686 cites W3100486754 @default.
• W2752417686 cites W3140968660 @default.
• W2752417686 cites W3144088531 @default.
• W2752417686 doi "https://doi.org/10.1109/tcomm.2017.2746099" @default.
• W2752417686 hasPublicationYear "2017" @default.
• W2752417686 type Work @default.
• W2752417686 sameAs 2752417686 @default.
• W2752417686 citedByCount "14" @default.
• W2752417686 countsByYear W27524176862017 @default.
• W2752417686 countsByYear W27524176862018 @default.
• W2752417686 countsByYear W27524176862019 @default.
• W2752417686 countsByYear W27524176862020 @default.
• W2752417686 countsByYear W27524176862021 @default.
• W2752417686 countsByYear W27524176862022 @default.
• W2752417686 crossrefType "journal-article" @default.
• W2752417686 hasAuthorship W2752417686A5009017662 @default.
• W2752417686 hasAuthorship W2752417686A5043511500 @default.
• W2752417686 hasAuthorship W2752417686A5044634366 @default.
• W2752417686 hasAuthorship W2752417686A5054469104 @default.
• W2752417686 hasBestOaLocation W27524176862 @default.
• W2752417686 hasConcept C105795698 @default.
• W2752417686 hasConcept C11413529 @default.
• W2752417686 hasConcept C124101348 @default.
• W2752417686 hasConcept C124851039 @default.
• W2752417686 hasConcept C133462117 @default.
• W2752417686 hasConcept C153083717 @default.
• W2752417686 hasConcept C154945302 @default.
• W2752417686 hasConcept C165696696 @default.
• W2752417686 hasConcept C24590314 @default.
• W2752417686 hasConcept C31258907 @default.
• W2752417686 hasConcept C33923547 @default.
• W2752417686 hasConcept C38652104 @default.
• W2752417686 hasConcept C41008148 @default.

• next