{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007907068,0.0003746361,0.0003633954,0.0006035454,0.0002419845,0.0000326254,0.0004845768,0.0006667956,0.00004256035],"category_scores_gemma":[0.00003861225,0.0003966414,0.0001556219,0.001046007,0.000124337,0.0000830882,0.000005198656,0.0008570792,0.0001265723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001223932,"about_ca_system_score_gemma":0.00002408813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006982153,"about_ca_topic_score_gemma":0.00002235077,"domain_scores_codex":[0.9984502,0.00003240732,0.000343099,0.000454958,0.000247993,0.0004713302],"domain_scores_gemma":[0.9990421,0.00005971194,0.00004841874,0.0006813295,0.0000759928,0.00009243799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002558083,0.00006707804,0.000007773782,0.00006840396,0.00006346478,0.00007273337,0.00009049282,0.8848881,0.0967105,0.0003671758,0.00006100458,0.01757776],"study_design_scores_gemma":[0.0006815105,0.0000965361,0.000001908403,0.00003685442,0.00001698912,0.000005805565,0.0001835815,0.5376743,0.4604591,0.0002121552,0.0004017194,0.0002295591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01943884,0.00004384283,0.9711069,0.001939252,0.000398947,0.0003491818,0.00005012241,0.006583419,0.00008948483],"genre_scores_gemma":[0.9282409,0.00001706745,0.07085215,0.000715423,0.00001934606,0.00003899443,0.000007337133,0.00009834269,0.00001041266],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9088021,"threshold_uncertainty_score":0.9998485,"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."}}