{"id":"W3175306513","doi":"","title":"Digital LMS adaptation of analog filters without gradient information.","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446481,0.000545261,0.0003126742,0.000411221,0.000215128,0.0006038688,0.0004782109,0.0006844054,0.004253244],"category_scores_gemma":[0.001882443,0.0002177414,0.0002892844,0.0004922431,0.0004287163,0.0006963089,0.0004010664,0.0007171496,0.00303284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004484376,"about_ca_system_score_gemma":0.0004166156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00109189,"about_ca_topic_score_gemma":0.001876893,"domain_scores_codex":[0.999683,0.000051832,0.0000199435,0.00007117304,0.0001543946,0.00001964102],"domain_scores_gemma":[0.9997029,0.00008564425,0.00003079041,0.00006146279,0.0001060122,0.0000131759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001931783,0.00005800154,0.0004063571,0.0003262962,0.00009725026,0.0001718424,0.0001014777,0.04340186,0.1079898,0.03057528,0.01262167,0.8040571],"study_design_scores_gemma":[0.00008121782,0.0002148581,0.001081572,0.00009502163,0.00008254301,0.0008625868,0.0000381423,0.6909052,0.1281464,0.01636885,0.1620553,0.00006828453],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004632803,0.001224483,0.9820637,0.0002191444,0.0004932376,0.00007052925,0.00004450575,0.001471668,0.009780034],"genre_scores_gemma":[0.1953897,0.002829076,0.7437901,0.0006403691,0.000353117,0.0002158098,0.0003603124,0.0002300343,0.05619149],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004253244,"threshold_uncertainty_score":0.01422852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162180833600033,"score_gpt":0.2103394129634896,"score_spread":0.1941213296034863,"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."}}