{"id":"W2153434444","doi":"10.1109/iscas.2007.378855","title":"A Diversity Controlled Genetic Algorithm for Optimization of FRM Digital Filters over DBNS Multiplier Coefficient Space","year":2007,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Finite impulse response; Digital filter; Lookup table; Multiplier (economics); Algorithm; Computer science; Filter design; Crossover; Mathematics; Filter (signal processing); Artificial intelligence; Computer vision","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":[0.0001788875,0.0001131754,0.0001745834,0.0001217902,0.0001431933,0.0001315118,0.0003065525,0.00003252829,0.00002078735],"category_scores_gemma":[0.00003162615,0.0001005789,0.0001097429,0.0002006765,0.00003081666,0.0005877204,0.0003584704,0.00002322898,0.000003472771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005295169,"about_ca_system_score_gemma":0.00002300333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002277917,"about_ca_topic_score_gemma":0.000003572119,"domain_scores_codex":[0.9989534,0.000009447889,0.0002747579,0.0002493781,0.000272555,0.0002404877],"domain_scores_gemma":[0.999276,0.0001716529,0.0001275591,0.0002010589,0.000140062,0.00008363304],"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":[0.0009424825,0.002114598,0.01106386,0.00009028531,0.0005151949,0.00002836596,0.009925974,0.1406213,0.002241645,0.02397309,0.006621912,0.8018612],"study_design_scores_gemma":[0.005063249,0.0002508792,0.002325509,0.000005927026,0.0000126977,0.000001966304,0.00008411778,0.9890738,0.002619293,0.0001917314,0.000217465,0.0001534203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0092526,0.000007771085,0.9878018,0.0000464579,0.0001816744,0.0007134504,0.00003306875,0.00005998895,0.001903213],"genre_scores_gemma":[0.6165913,0.000001249921,0.3826694,0.0001366528,0.00001986566,0.000007514247,0.00001990458,0.000006044803,0.000548038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8484524,"threshold_uncertainty_score":0.4101489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01639833383906605,"score_gpt":0.2512298646019818,"score_spread":0.2348315307629158,"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."}}