{"id":"W1495652757","doi":"10.1109/icosp.2004.1452567","title":"Genetic algorithms for the design of digital filters using canonic signed digit coefficients","year":2005,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Coding (social sciences); Numerical digit; Computer science; Algorithm; Genetic algorithm; Throughput; Digital filter; Algorithm design; Design methods; Arithmetic; Computer hardware; Mathematics; Filter (signal processing); Engineering; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000613836,0.0007321349,0.0003951497,0.0008769492,0.0003926275,0.0005941993,0.0006496595,0.0007278985,0.001328052],"category_scores_gemma":[0.00203281,0.0002962468,0.0004459633,0.0007654166,0.0006094101,0.0004241321,0.0003201881,0.0008228155,0.0003191853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005645851,"about_ca_system_score_gemma":0.0006716179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001967126,"about_ca_topic_score_gemma":0.003164506,"domain_scores_codex":[0.9997274,0.00007892889,0.00001624434,0.00003357185,0.0001276103,0.00001620093],"domain_scores_gemma":[0.9996376,0.000231899,0.0000344202,0.00001989457,0.00006700242,0.000009141813],"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":[0.00007303022,0.00006207969,0.0005535738,0.0002785962,0.0001274181,0.0001364382,0.0001906319,0.5609054,0.01558628,0.07926304,0.002008161,0.3408153],"study_design_scores_gemma":[0.00007112992,0.0001243751,0.0002638398,0.00007402425,0.00006382433,0.0001649143,0.00003557224,0.9515151,0.007776975,0.02496982,0.01490871,0.00003156582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00424134,0.0004977937,0.9930362,0.0000796695,0.00004252879,0.00003945589,0.00001059321,0.0001330325,0.001919383],"genre_scores_gemma":[0.08866544,0.001193134,0.906783,0.0001022877,0.00003648288,0.0002639756,0.00005821947,0.00004502363,0.002852446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001967126,"threshold_uncertainty_score":0.004442751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04326202874631511,"score_gpt":0.2726552891241682,"score_spread":0.2293932603778531,"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."}}