{"id":"W2038216172","doi":"10.1109/tvlsi.2011.2156822","title":"Hardware Implementation of Nakagami and Weibull Variate Generators","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fading; Field-programmable gate array; Weibull distribution; Computer science; Nakagami distribution; Algorithm; Rician fading; Random variate; Autocorrelation; Weibull fading; Throughput; Parallel computing; Rayleigh fading; Mathematics; Statistics; Computer hardware; Telecommunications; Wireless; Random variable","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":[0.0002498764,0.0006227674,0.0003311087,0.0005760712,0.0003183505,0.0006296083,0.00118222,0.0003016562,0.01000054],"category_scores_gemma":[0.0007736351,0.0002454664,0.0002856489,0.0003700148,0.0001576363,0.0005151026,0.0003453834,0.0004153964,0.003232485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005592702,"about_ca_system_score_gemma":0.0008090544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001415082,"about_ca_topic_score_gemma":0.001556973,"domain_scores_codex":[0.9997539,0.00004133075,0.00001957952,0.00004222482,0.0000974819,0.00004554567],"domain_scores_gemma":[0.9997234,0.00008618546,0.00003345286,0.00005949398,0.00007636061,0.00002110411],"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":[0.0009106757,0.0001760813,0.004407893,0.0004130895,0.0001161244,0.0008139325,0.0003339248,0.2221246,0.1300207,0.04116741,0.017785,0.5817305],"study_design_scores_gemma":[0.0001825033,0.000699472,0.001955861,0.00006069447,0.00006474845,0.001049016,0.00006398166,0.799578,0.1417215,0.006247977,0.04828693,0.00008924496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04001279,0.0002988868,0.9343991,0.0001521051,0.0001034287,0.0002105658,0.0002853518,0.01328071,0.01125713],"genre_scores_gemma":[0.5821635,0.000293241,0.4062151,0.0001238861,0.00005128835,0.0002606919,0.0006738162,0.0003744455,0.009844049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01000054,"threshold_uncertainty_score":0.03345519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878391223736055,"score_gpt":0.2416809035885888,"score_spread":0.2228969913512283,"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."}}