Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.
<p>Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.</p>
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2025
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| _version_ | 1852019086918156288 |
|---|---|
| author | Jiuxiao Cao (21594403) |
| author2 | Rui Zhu (305217) Zhen Wang (72451) Jun Wang (5906) Guohao Shi (21594406) Peng Chu (5565407) |
| author2_role | author author author author author |
| author_facet | Jiuxiao Cao (21594403) Rui Zhu (305217) Zhen Wang (72451) Jun Wang (5906) Guohao Shi (21594406) Peng Chu (5565407) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jiuxiao Cao (21594403) Rui Zhu (305217) Zhen Wang (72451) Jun Wang (5906) Guohao Shi (21594406) Peng Chu (5565407) |
| dc.date.none.fl_str_mv | 2025-06-24T17:41:02Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0326536.g018 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Five-fold_cross-validation_results_of_mulit-scale_CNN_algorithm_at_different_SNRs_/29393968 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Biological Sciences not elsewhere classified spatial feature diversity sequential learning capacity remote communications due hybrid architecture enhances evaluated using five ensure statistical reliability different receptive fields dataset includes 45 common shortwave protocols captures temporal dependencies 3gale &# 8212 000 labeled samples subcarrier modulations based scale convolutional gru robust signal classification subcarrier classification low signal world sources vital role time deployment study proposes standard gpu selective fading scalable solution results show results confirm range capabilities protocol identification noise ratios multipath propagation inference time gru provides fold cross disaster relief dimensional representations bidirectional gru based real 8 db 10 db |
| dc.title.none.fl_str_mv | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_320ddda075b677524dd83a4e06a1bf4b |
| identifier_str_mv | 10.1371/journal.pone.0326536.g018 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29393968 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.Jiuxiao Cao (21594403)Rui Zhu (305217)Zhen Wang (72451)Jun Wang (5906)Guohao Shi (21594406)Peng Chu (5565407)Space ScienceBiological Sciences not elsewhere classifiedspatial feature diversitysequential learning capacityremote communications duehybrid architecture enhancesevaluated using fiveensure statistical reliabilitydifferent receptive fieldsdataset includes 45common shortwave protocolscaptures temporal dependencies3gale &# 8212000 labeled samplessubcarrier modulations basedscale convolutional grurobust signal classificationsubcarrier classificationlow signalworld sourcesvital roletime deploymentstudy proposesstandard gpuselective fadingscalable solutionresults showresults confirmrange capabilitiesprotocol identificationnoise ratiosmultipath propagationinference timegru providesfold crossdisaster reliefdimensional representationsbidirectional grubased real8 db10 db<p>Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs.</p>2025-06-24T17:41:02ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0326536.g018https://figshare.com/articles/figure/Five-fold_cross-validation_results_of_mulit-scale_CNN_algorithm_at_different_SNRs_/29393968CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/293939682025-06-24T17:41:02Z |
| spellingShingle | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. Jiuxiao Cao (21594403) Space Science Biological Sciences not elsewhere classified spatial feature diversity sequential learning capacity remote communications due hybrid architecture enhances evaluated using five ensure statistical reliability different receptive fields dataset includes 45 common shortwave protocols captures temporal dependencies 3gale &# 8212 000 labeled samples subcarrier modulations based scale convolutional gru robust signal classification subcarrier classification low signal world sources vital role time deployment study proposes standard gpu selective fading scalable solution results show results confirm range capabilities protocol identification noise ratios multipath propagation inference time gru provides fold cross disaster relief dimensional representations bidirectional gru based real 8 db 10 db |
| status_str | publishedVersion |
| title | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| title_full | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| title_fullStr | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| title_full_unstemmed | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| title_short | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| title_sort | Five-fold cross-validation results of mulit-scale CNN algorithm at different SNRs. |
| topic | Space Science Biological Sciences not elsewhere classified spatial feature diversity sequential learning capacity remote communications due hybrid architecture enhances evaluated using five ensure statistical reliability different receptive fields dataset includes 45 common shortwave protocols captures temporal dependencies 3gale &# 8212 000 labeled samples subcarrier modulations based scale convolutional gru robust signal classification subcarrier classification low signal world sources vital role time deployment study proposes standard gpu selective fading scalable solution results show results confirm range capabilities protocol identification noise ratios multipath propagation inference time gru provides fold cross disaster relief dimensional representations bidirectional gru based real 8 db 10 db |