{"id":"W3017995847","doi":"10.1109/twc.2020.2987990","title":"A Downscaled Faster-RCNN Framework for Signal Detection and Time-Frequency Localization in Wideband RF Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Radio frequency; Wideband; Interference (communication); SIGNAL (programming language); Bluetooth; Wireless; Artificial intelligence; Radio spectrum; Feature extraction; Signal-to-noise ratio (imaging); Noise (video); Detection theory; Time–frequency analysis; Speech recognition; Pattern recognition (psychology); Electronic engineering; Telecommunications; Detector; Engineering; Radar","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004289412,0.001060815,0.0006580593,0.0005579612,0.0002261625,0.0005214479,0.001560421,0.0006927553,0.002418581],"category_scores_gemma":[0.001032484,0.0004168392,0.0006509868,0.000492284,0.0003452616,0.0009125998,0.0007125316,0.001038928,0.0009981542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006105373,"about_ca_system_score_gemma":0.0007300928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01371569,"about_ca_topic_score_gemma":0.01535036,"domain_scores_codex":[0.9997527,0.00002669868,0.00001073818,0.0000976977,0.00007157758,0.00004060511],"domain_scores_gemma":[0.9997144,0.00006953558,0.00002952136,0.00005710919,0.0001162582,0.00001301832],"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":[0.0001161111,0.00008334887,0.0008435642,0.0001149992,0.00007065919,0.0001498937,0.00006510883,0.5932537,0.03374317,0.003240836,0.003725374,0.3645933],"study_design_scores_gemma":[0.000004214349,0.00002687292,0.0002375151,0.000006262903,0.00001179169,0.00003207736,0.000006168875,0.9939176,0.003641963,0.0009103753,0.001196842,0.000008259419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02206706,0.000528003,0.9718899,0.0001330872,0.00008699667,0.00003803007,0.0001716527,0.003216214,0.001869051],"genre_scores_gemma":[0.5517524,0.0005758429,0.4382775,0.0003798613,0.00009633887,0.0001594468,0.001039823,0.0004415471,0.00727733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01371569,"threshold_uncertainty_score":0.02727169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019277183968542,"score_gpt":0.2341598939590274,"score_spread":0.2148827099904854,"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."}}