{"id":"W2334242385","doi":"10.1364/sppcom.2015.spt3d.5","title":"Low-complexity Fractionally Spaced Equalizer with Non-integer Sub-symbol Sampling for Coherent Optical Receivers","year":2015,"lang":"en","type":"article","venue":"","topic":"Optical Network Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Symbol rate; Integer (computer science); Equalizer; Computer science; Sampling (signal processing); Adaptive equalizer; Symbol (formal); Computational complexity theory; SIGNAL (programming language); Algorithm; Electronic engineering; Signal processing; Bit error rate; Digital signal processing; Equalization (audio); Telecommunications; Decoding methods; Engineering","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":[0.0002883773,0.0004529462,0.0003154901,0.0003160962,0.0003003373,0.0005680256,0.0007141638,0.0004744139,0.001564451],"category_scores_gemma":[0.0005612241,0.0002595133,0.0002510051,0.0003428974,0.0002922927,0.0009289127,0.0003834497,0.0007627338,0.0004470963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003939207,"about_ca_system_score_gemma":0.0004193346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004164522,"about_ca_topic_score_gemma":0.001074372,"domain_scores_codex":[0.999648,0.00006176698,0.00001975928,0.00005352091,0.0001804014,0.00003648007],"domain_scores_gemma":[0.9997891,0.00007604383,0.0000371857,0.00003748043,0.00004594695,0.00001422205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005578254,0.0001749543,0.0009976695,0.0001584577,0.00006740325,0.0001390054,0.00008487958,0.02101332,0.71012,0.02551751,0.001141195,0.2400277],"study_design_scores_gemma":[0.00008883026,0.000521539,0.0009024742,0.00002191681,0.00008106852,0.0003865664,0.00001978402,0.6112043,0.374067,0.003137978,0.009513706,0.0000548021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05076873,0.0004973578,0.9455695,0.0001467006,0.00007952337,0.00004524564,0.0000256496,0.0003844749,0.002482839],"genre_scores_gemma":[0.4659289,0.0003365345,0.5303316,0.000182373,0.0001500466,0.00006805293,0.00007404546,0.00004026239,0.00288825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001564451,"threshold_uncertainty_score":0.005233586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06559375952911053,"score_gpt":0.274169419715961,"score_spread":0.2085756601868505,"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."}}