{"id":"W2097892160","doi":"10.1109/mwscas.2005.1594108","title":"Computationally-efficient methods for blind adaptive equalization","year":2005,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Blind equalization; QAM; Computer science; Adaptive equalizer; Equalization (audio); Dither; Equalizer; Algorithm; Quadrature amplitude modulation; Constant (computer programming); Bit error rate; Decoding methods; Telecommunications; Bandwidth (computing)","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":[0.0009001305,0.001010798,0.0006763651,0.001046447,0.0004943713,0.0009360706,0.0009831126,0.0008422221,0.004153645],"category_scores_gemma":[0.003547749,0.0003826514,0.0006205761,0.000845237,0.0008066273,0.00155508,0.001193168,0.001817494,0.002214425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005170422,"about_ca_system_score_gemma":0.0008152514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007659215,"about_ca_topic_score_gemma":0.001224033,"domain_scores_codex":[0.9991251,0.0002082093,0.00004644103,0.00009128517,0.0004896613,0.00003939088],"domain_scores_gemma":[0.9990214,0.0005248914,0.00005901678,0.0001768381,0.0001985079,0.00001919945],"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":[0.0001350117,0.00007849815,0.0001922764,0.0003858424,0.00009127351,0.0000676881,0.0001052089,0.1303568,0.02128864,0.2990487,0.00732276,0.5409274],"study_design_scores_gemma":[0.0000430581,0.00004646327,0.0001759112,0.00006899227,0.0000270048,0.0002412147,0.00001826512,0.8535293,0.013012,0.09998363,0.03280386,0.00005028388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000317755,0.0003574613,0.9980202,0.00004386029,0.00004012815,0.00001261969,0.00001157509,0.0001060799,0.00109035],"genre_scores_gemma":[0.0266419,0.00153921,0.9660518,0.000102123,0.0002070399,0.0001542995,0.00008831133,0.0001369256,0.005078403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004153645,"threshold_uncertainty_score":0.01389533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06900201146282746,"score_gpt":0.4167659748833216,"score_spread":0.3477639634204941,"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."}}