{"id":"W2160434634","doi":"10.1109/ccece.2004.1347658","title":"Blind decision feedback equalizer based on high order MCMA","year":2004,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Computer science; Mean squared error; Convergence (economics); Monte Carlo method; Equalizer; Metric (unit); Noise (video); Least mean squares filter; Gaussian; Adaptive filter; Mathematics; Artificial intelligence; Statistics; Telecommunications; Engineering","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":[0.0006418906,0.0005863503,0.0006896838,0.0004756688,0.0003639449,0.0005442099,0.0008270745,0.0007934557,0.001479658],"category_scores_gemma":[0.001893488,0.0002899109,0.0004411276,0.0004875798,0.0004823922,0.0008855214,0.0005228953,0.001116987,0.0005941624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005046165,"about_ca_system_score_gemma":0.0009124346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050583,"about_ca_topic_score_gemma":0.002822662,"domain_scores_codex":[0.9994465,0.000138706,0.00002819234,0.0001183998,0.0002104418,0.00005777647],"domain_scores_gemma":[0.9995054,0.0002440347,0.00005029846,0.00006713125,0.0001141244,0.00001898214],"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":[0.00061794,0.0001311107,0.001451689,0.0001792433,0.0001582644,0.0001368002,0.0001439177,0.2660716,0.07207625,0.04695183,0.002717374,0.609364],"study_design_scores_gemma":[0.00004067079,0.00007899878,0.000354686,0.00001001296,0.0000212959,0.0001208324,0.000006824843,0.9727634,0.01763486,0.005637642,0.003303544,0.00002721969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005478939,0.0002020402,0.9933883,0.0000588361,0.00005402951,0.00002007877,0.00001704437,0.0002833864,0.0004972986],"genre_scores_gemma":[0.2853142,0.0003049431,0.7094091,0.000170704,0.00009082777,0.0001216052,0.00009122292,0.00004587423,0.004451603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002050583,"threshold_uncertainty_score":0.004949927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121258406630506,"score_gpt":0.2907403233993088,"score_spread":0.2695277393330037,"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."}}