{"id":"W2484071415","doi":"10.1109/cjece.2016.2570250","title":"Automatic Modulation Classification Based on Kernel Density Estimation","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Kernel density estimation; Pattern recognition (psychology); Artificial intelligence; Computer science; Kernel (algebra); Modulation (music); Estimation; Multivariate kernel density estimation; Variable kernel density estimation; Statistics; Support vector machine; Mathematics; Kernel method; Engineering; Physics; Acoustics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001518433,0.0005625439,0.001027308,0.001501548,0.0005514593,0.0009771925,0.001041166,0.0008297841,0.0009373436],"category_scores_gemma":[0.006449874,0.0003143385,0.0006592398,0.0009520412,0.0007065988,0.001672157,0.0008878265,0.001131442,0.0006620066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006818345,"about_ca_system_score_gemma":0.0007089163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001613406,"about_ca_topic_score_gemma":0.001063548,"domain_scores_codex":[0.9987391,0.0004079153,0.0000705819,0.0001996165,0.0004503324,0.0001325433],"domain_scores_gemma":[0.9968218,0.001582189,0.0003249456,0.0004488348,0.0007459269,0.00007628827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003822455,0.0002191276,0.003690552,0.0001427087,0.00009848834,0.0001414087,0.0002049239,0.2216003,0.02795333,0.03716798,0.003009654,0.7053893],"study_design_scores_gemma":[0.000005730535,0.00001959539,0.0004510316,0.00000418314,0.000006539323,0.00004377545,0.00000674091,0.9926748,0.003130502,0.003200331,0.0004440919,0.00001274908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01571124,0.0001728506,0.9829593,0.00009374605,0.00002877505,0.00002670107,0.0000192881,0.0003685276,0.0006195045],"genre_scores_gemma":[0.5734988,0.0003355539,0.4233324,0.0001473814,0.0001335089,0.000101868,0.0002583269,0.0000984893,0.002093686],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001613406,"threshold_uncertainty_score":0.008030295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0123838522273119,"score_gpt":0.1920564049912435,"score_spread":0.1796725527639316,"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."}}