{"id":"W3038116752","doi":"10.1109/tvt.2020.3004175","title":"Multi-Target Device-Free Wireless Sensing Based on Multiplexing Mechanisms","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Liaoning Revitalization Talents Program; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Multiplexing; Wireless; Computer science; Time-division multiplexing; Electronic engineering; Exploit; Frequency-division multiplexing; Orthogonal frequency-division multiplexing; Real-time computing; Engineering; Telecommunications; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000456723,0.000834424,0.0003906031,0.0005878813,0.0003285229,0.0005796253,0.001030806,0.0006151731,0.0008629378],"category_scores_gemma":[0.0007883884,0.0003340728,0.0004113565,0.0004717926,0.0005681609,0.001868146,0.001071537,0.000462158,0.0002949076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003201881,"about_ca_system_score_gemma":0.0002088528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002421666,"about_ca_topic_score_gemma":0.000332041,"domain_scores_codex":[0.9994276,0.00009768606,0.00003026006,0.0001370136,0.0002472965,0.00005999576],"domain_scores_gemma":[0.9995097,0.0001832704,0.0001123976,0.00008049964,0.00008238757,0.00003178536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002796564,0.0000845064,0.001353614,0.0003150426,0.00005766167,0.0003592685,0.0002379997,0.01305138,0.8311521,0.02265527,0.0006806443,0.1297728],"study_design_scores_gemma":[0.0000501824,0.0008070208,0.001519478,0.00004823748,0.00008509344,0.001719557,0.0001161744,0.3077123,0.6670214,0.008231944,0.01255796,0.0001305997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1243996,0.001819566,0.8647516,0.0002665187,0.0001927902,0.0001453541,0.00005594475,0.0006229899,0.00774553],"genre_scores_gemma":[0.8558471,0.001184938,0.1393693,0.0002168234,0.0001220098,0.0001095223,0.00004706653,0.00002723508,0.00307602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001030806,"threshold_uncertainty_score":0.002886772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593587723293568,"score_gpt":0.2125217660125636,"score_spread":0.1965858887796279,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}