{"id":"W1966128506","doi":"10.1007/s00521-011-0796-y","title":"QAM equalization and symbol detection in OFDM systems using extreme learning machine","year":2012,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Computational Science and Engineering; Equalization (audio); Orthogonal frequency-division multiplexing; Extreme learning machine; Symbol (formal); QAM; Quadrature amplitude modulation; Artificial intelligence; Machine learning; Pattern recognition (psychology); Algorithm; Telecommunications; Artificial neural network; Channel (broadcasting); Bit error rate","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003611747,0.00009793686,0.0001115171,0.0001076265,0.0003721557,0.0001369776,0.0001020307,0.00004285495,4.335738e-7],"category_scores_gemma":[0.00002472361,0.00009658578,0.0000132657,0.0003255484,0.00001949771,0.0002052274,0.0001180615,0.0001737704,0.000002239359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001997268,"about_ca_system_score_gemma":0.000005124952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002593174,"about_ca_topic_score_gemma":0.00000571591,"domain_scores_codex":[0.9991418,0.0001345402,0.0001860638,0.0002284184,0.00009474401,0.0002144468],"domain_scores_gemma":[0.9995661,0.0001016151,0.0000980431,0.0001347278,0.00002386423,0.0000755855],"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":[0.000004407312,0.0001044674,0.1119639,0.0001483227,0.00001019563,9.241309e-7,0.002409413,0.1047508,0.01246647,0.02438545,0.000005891705,0.7437497],"study_design_scores_gemma":[0.0001356502,0.00001656796,0.01438112,0.00002025655,0.000004266756,0.00004947971,0.00006242402,0.9840381,0.00008003753,0.00004920357,0.001056955,0.0001059854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4736496,0.0006120323,0.525266,0.00006016585,0.0000639136,0.0001187779,2.093144e-7,0.000121542,0.0001077475],"genre_scores_gemma":[0.998069,0.00001143427,0.001686157,0.00003298432,0.0001465969,0.00001011686,0.000003056496,0.000008138871,0.00003252839],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8792872,"threshold_uncertainty_score":0.3938653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03371695335298815,"score_gpt":0.2872922914169985,"score_spread":0.2535753380640103,"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."}}