{"id":"W2115279537","doi":"10.1109/tcsii.2003.815021","title":"Digital LMS adaptation of analog filters without gradient information","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Adaptive filter; Least mean squares filter; Computer science; Electronic engineering; Digital filter; Filter (signal processing); Analog signal; Analogue filter; Offset (computer science); Control theory (sociology); Digital signal processing; Algorithm; Engineering; Artificial intelligence; Computer hardware","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001630546,0.0002683117,0.0003517416,0.0002880681,0.0003164224,0.0004560516,0.00006499933,0.0001233017,0.000005438076],"category_scores_gemma":[0.000007080399,0.0002471459,0.00007933679,0.000286677,0.0001120718,0.001927148,9.440747e-7,0.0001770548,0.0000041512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004476687,"about_ca_system_score_gemma":0.00004749543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001734135,"about_ca_topic_score_gemma":0.000004394023,"domain_scores_codex":[0.9986633,0.00002525884,0.000534083,0.0002156455,0.0002777604,0.0002839529],"domain_scores_gemma":[0.9994274,0.00005747926,0.0001225254,0.00009685627,0.0001143151,0.0001813884],"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":[0.00002127014,0.0001136256,0.0004668333,0.0008073072,0.0001603619,0.000006220073,0.002205774,0.133721,0.000867345,0.002605655,0.00004110279,0.8589835],"study_design_scores_gemma":[0.006940149,0.003656167,0.0008922106,0.003431407,0.0007434204,0.00137749,0.02371883,0.9284142,0.006260569,0.005153609,0.01500463,0.004407359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05532465,0.0009303794,0.9335947,0.000001511675,0.0001408787,0.0002118548,0.0001073986,0.0001195014,0.009569104],"genre_scores_gemma":[0.9996749,0.00005069117,0.000005958583,0.00001460889,0.00002089762,0.00001939551,0.00002283309,0.000023156,0.0001675749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9443502,"threshold_uncertainty_score":0.9999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620103276935051,"score_gpt":0.1954330334746157,"score_spread":0.1792320007052652,"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."}}