{"id":"W2091812614","doi":"10.1109/iccs.2006.301444","title":"MRC-Rake-LMS-Equalizer Performance for UWB","year":2006,"lang":"en","type":"article","venue":"","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Rake receiver; Adaptive equalizer; Equalizer; Least mean squares filter; Computer science; Rake; Path (computing); Transmission (telecommunications); Bit error rate; Algorithm; Sequence (biology); Electronic engineering; Adaptive filter; Fading; Telecommunications; Channel (broadcasting); Decoding methods; Engineering; Computer network","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":[0.00005249082,0.00008083653,0.00008692954,0.00006377963,0.00005618473,0.00001181602,0.0002525819,0.00008052005,0.0000858423],"category_scores_gemma":[0.000007965408,0.00007631248,0.00003345463,0.0001195444,0.00002618704,0.000074951,0.0000196796,0.00008035373,0.0001012523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002684749,"about_ca_system_score_gemma":0.00000502778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002165778,"about_ca_topic_score_gemma":0.00006346521,"domain_scores_codex":[0.9995537,0.000003653769,0.0001475649,0.00007608044,0.00004111659,0.0001778716],"domain_scores_gemma":[0.999457,0.00005267929,0.00001095751,0.0004416986,0.00002429774,0.00001337826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002774057,0.0002578319,0.01088803,0.0003859109,0.000154659,0.00000142756,0.0003457682,0.04505784,0.08035707,0.461634,0.2489386,0.1519512],"study_design_scores_gemma":[0.0006553241,0.00006046039,0.002884242,0.00001664706,0.00001882988,0.00001075137,0.00004977313,0.08885797,0.1204915,0.007570587,0.7789792,0.0004046214],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.667025,0.0008176086,0.09797936,0.0007385736,0.0002055876,0.0003825514,0.000009756587,0.002747706,0.2300939],"genre_scores_gemma":[0.9797227,0.00007226015,0.01697898,0.0000504353,0.00002939975,0.0001208987,0.00001774975,0.00002235516,0.002985267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5300407,"threshold_uncertainty_score":0.3111933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007509161655708096,"score_gpt":0.1987068389399599,"score_spread":0.1911976772842518,"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."}}