{"id":"W1982530805","doi":"10.4236/ijcns.2010.36070","title":"Nonlinear Blind Equalizers: NCMA and NMCMA","year":2010,"lang":"en","type":"article","venue":"International Journal of Communications Network and System Sciences","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Nonlinear system; Quadrature amplitude modulation; Constant (computer programming); Mathematics; Algorithm; Mean squared error; Modulus; Square (algebra); Control theory (sociology); Mathematical optimization; Applied mathematics; Computer science; Bit error rate; Statistics; Geometry; Artificial intelligence; Physics","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":[0.000955604,0.0006961006,0.0004778561,0.0005026634,0.0004281043,0.0007990618,0.0007750892,0.001079281,0.001777445],"category_scores_gemma":[0.003932044,0.0002107942,0.0003214206,0.0004775763,0.001077946,0.001436899,0.0007566816,0.0009656731,0.0006823585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006003241,"about_ca_system_score_gemma":0.0008831258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001351455,"about_ca_topic_score_gemma":0.001771407,"domain_scores_codex":[0.9993057,0.0001478293,0.00004494654,0.0001528455,0.0003015527,0.00004722808],"domain_scores_gemma":[0.9988317,0.0005073402,0.000130245,0.0001834739,0.0003139525,0.00003328221],"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":[0.000868901,0.0001290114,0.00151166,0.0003785021,0.0001351781,0.0001422683,0.0002278072,0.1699239,0.1073342,0.1274828,0.003299483,0.5885663],"study_design_scores_gemma":[0.00007545937,0.0001753334,0.0006250914,0.00004521795,0.0000667939,0.0003098999,0.00003534046,0.9121361,0.0617529,0.01229816,0.01242419,0.00005545299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01103204,0.0007288032,0.9838573,0.0002025806,0.0001286093,0.00004432976,0.00001667754,0.0002352636,0.003754433],"genre_scores_gemma":[0.4148349,0.001073247,0.5718082,0.0003655062,0.0002808086,0.0002339129,0.00006447567,0.00006458348,0.01127443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001777445,"threshold_uncertainty_score":0.005946159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04025263552881538,"score_gpt":0.3457636567280029,"score_spread":0.3055110211991875,"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."}}