{"id":"W2804103001","doi":"10.23919/ropaces.2018.8364214","title":"Nonlinear neural network equalizer for metro optical fiber communication systems","year":2018,"lang":"en","type":"article","venue":"2018 International Applied Computational Electromagnetics Society Symposium (ACES)","topic":"Optical Network Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Maximum likelihood sequence estimation; Intersymbol interference; Bit error rate; Computer science; Nonlinear system; Adaptive equalizer; Equalization (audio); Artificial neural network; Estimator; Optical communication; Electronic engineering; Algorithm; Mathematics; Estimation theory; Decoding methods; Artificial intelligence; Engineering; Physics; Statistics","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":[0.0002066054,0.0002979343,0.0002273063,0.0001752456,0.0002569937,0.0004119272,0.0003949113,0.0004567916,0.002025965],"category_scores_gemma":[0.000482769,0.0001135858,0.000148318,0.0001856915,0.0002230977,0.0005842318,0.0003429629,0.0005202874,0.0003527247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000425024,"about_ca_system_score_gemma":0.0002774439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001478776,"about_ca_topic_score_gemma":0.002699479,"domain_scores_codex":[0.9998553,0.00002715663,0.000007318218,0.00003096928,0.00005856258,0.00002072871],"domain_scores_gemma":[0.9999113,0.0000315452,0.00001296515,0.000007386701,0.00003297423,0.000003764577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004920342,0.000193373,0.001077159,0.0002156064,0.0001248343,0.0001905855,0.00006972848,0.5077527,0.1015862,0.03056896,0.003285681,0.3544432],"study_design_scores_gemma":[0.00001113231,0.00004783988,0.0002041414,0.000008286112,0.00001561447,0.00004054604,0.000004854024,0.9764439,0.0199711,0.001354771,0.001889548,0.000008231106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04429445,0.001099165,0.9469357,0.0003264358,0.0001641008,0.00003835149,0.00004633959,0.0004212899,0.006674073],"genre_scores_gemma":[0.8250449,0.000731497,0.1576271,0.0002163157,0.0001417191,0.00007708208,0.00009479414,0.00003889983,0.01602773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002025965,"threshold_uncertainty_score":0.006777525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009124952873197601,"score_gpt":0.2345901928174736,"score_spread":0.225465239944276,"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."}}