{"id":"W4382541772","doi":"10.1109/prime58259.2023.10161935","title":"A Power-Efficient Successive Approximation Algorithm for Low-Activity Signals","year":2023,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Newfoundland and Labrador; Canada Foundation for Innovation","keywords":"Successive approximation ADC; Shaping; Algorithm; MATLAB; Computer science; Sample (material); SIGNAL (programming language); Power consumption; Effective number of bits; Analog-to-digital converter; Power (physics); Mathematics; Electronic engineering; Capacitor; Voltage; Electrical engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006454872,0.0008107894,0.0005578136,0.0008197462,0.0003437651,0.0007769505,0.0008888486,0.0005229643,0.002486954],"category_scores_gemma":[0.002411284,0.0002953252,0.00061941,0.001059703,0.000374043,0.0009972564,0.0004726068,0.00109585,0.00143442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003676676,"about_ca_system_score_gemma":0.0007991837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001819337,"about_ca_topic_score_gemma":0.002320742,"domain_scores_codex":[0.9993247,0.0000988187,0.00004546637,0.00009761137,0.000400968,0.00003247477],"domain_scores_gemma":[0.9994242,0.0002343426,0.00005529412,0.00008634073,0.0001807093,0.00001915016],"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":[0.0001716932,0.00006284941,0.0004770507,0.0001882691,0.00007508207,0.0001038616,0.0001655295,0.08989646,0.06874965,0.01566329,0.002340427,0.8221059],"study_design_scores_gemma":[0.00002358701,0.0001115949,0.0003084536,0.00001748891,0.00002439193,0.0002585147,0.00001861241,0.9605078,0.02427353,0.003737489,0.01070168,0.00001685559],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002135047,0.0001604363,0.9967733,0.00002155529,0.00002682676,0.00001563553,0.000008650371,0.0003246421,0.0005339065],"genre_scores_gemma":[0.06844135,0.000374536,0.9278157,0.00005059236,0.00006313265,0.0000700746,0.0001283835,0.0001241785,0.002932046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002486954,"threshold_uncertainty_score":0.008319676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501195100182527,"score_gpt":0.2380866717243414,"score_spread":0.2230747207225162,"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."}}