{"id":"W2101334499","doi":"10.1109/test.1994.528010","title":"An analog multi-tone signal generator for built-in-self-test applications","year":2002,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Signal generator; Delta-sigma modulation; Computer science; SIGNAL (programming language); Tone (literature); Electronic engineering; Analog signal; Filter (signal processing); Field-programmable gate array; Generator (circuit theory); Digital-to-analog converter; Encoding (memory); Low-pass filter; Analog transmission; Digital filter; Digital signal processing; Computer hardware; Electrical engineering; Chip; Engineering; Physics; Telecommunications; Artificial intelligence; Power (physics); Bandwidth (computing)","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":[0.0002247524,0.000264221,0.0001762258,0.0002573976,0.000158531,0.0003901736,0.0006182398,0.0004066096,0.004075658],"category_scores_gemma":[0.000519641,0.0001152393,0.0001288668,0.0001427416,0.0001491266,0.0003804287,0.0002512172,0.0003531283,0.0009505289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001183247,"about_ca_system_score_gemma":0.0001397782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005899736,"about_ca_topic_score_gemma":0.0001555712,"domain_scores_codex":[0.9997925,0.00004626086,0.00001221888,0.00003083578,0.0001027561,0.00001543813],"domain_scores_gemma":[0.9997128,0.00008682464,0.00003563144,0.00004531137,0.00009173161,0.00002771092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003537743,0.00009820952,0.001419477,0.0004570352,0.00003827338,0.0004337569,0.0001495847,0.003533756,0.597065,0.01024331,0.004254568,0.3819532],"study_design_scores_gemma":[0.0001660975,0.00319459,0.006054159,0.0001033022,0.0001510791,0.006382972,0.00005505536,0.1275943,0.7028251,0.003560266,0.1498479,0.00006540018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06696559,0.0009518895,0.9158779,0.0003514462,0.0004178214,0.0002551932,0.000174981,0.003077736,0.01192746],"genre_scores_gemma":[0.7366482,0.0003900136,0.2456733,0.0004544581,0.0002069187,0.000147388,0.0002129506,0.0001786999,0.01608813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004075658,"threshold_uncertainty_score":0.01363438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02613351025984315,"score_gpt":0.2537851437871703,"score_spread":0.2276516335273271,"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."}}