{"id":"W1561419443","doi":"10.1109/iscas.1994.409497","title":"Recursive allpass filter design using least-squares techniques","year":2002,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"All-pass filter; Minimax; Mathematics; Weighting; Least-squares function approximation; Filter (signal processing); Algorithm; Recursive least squares filter; Control theory (sociology); Computer science; Low-pass filter; Mathematical optimization; Adaptive filter; High-pass filter; Statistics; Artificial intelligence","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":[0.0006858877,0.001091368,0.0008629485,0.0005350898,0.0003888434,0.001147271,0.001000374,0.001245536,0.004900245],"category_scores_gemma":[0.001913441,0.0006045339,0.0008260203,0.0005213639,0.0003655769,0.0008728178,0.0005223787,0.001188989,0.003440833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005514283,"about_ca_system_score_gemma":0.0006445757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001875513,"about_ca_topic_score_gemma":0.003301234,"domain_scores_codex":[0.9993242,0.0001502806,0.00004416317,0.0001207587,0.0003208085,0.00003974642],"domain_scores_gemma":[0.999404,0.0001886032,0.00005901472,0.00008455227,0.0002461469,0.0000178149],"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":[0.0001881381,0.00008517988,0.0004897768,0.0004852069,0.0001978013,0.0001632972,0.00035162,0.1772181,0.07447037,0.04778644,0.006463135,0.692101],"study_design_scores_gemma":[0.00006787972,0.0001727116,0.0003704005,0.00005807799,0.00007726165,0.0002185687,0.00002949107,0.9065753,0.0444641,0.01076646,0.03713895,0.00006087311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000507916,0.00008454858,0.998445,0.00001780928,0.00001463862,0.00001484492,0.00001200482,0.0003719183,0.0005313667],"genre_scores_gemma":[0.03603841,0.0003665785,0.9582878,0.00007035013,0.00004245657,0.0001640413,0.0001231506,0.000163864,0.004743329],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004900245,"threshold_uncertainty_score":0.01639295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1475246335144996,"score_gpt":0.289910668770794,"score_spread":0.1423860352562944,"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."}}