{"id":"W3048806530","doi":"10.1109/taes.2020.3015322","title":"Efficient Sensing for Compressive Estimation of Frequency of a Real Sinusoid","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Compressed sensing; Estimator; Nyquist rate; Algorithm; Nyquist–Shannon sampling theorem; Signal reconstruction; Mathematics; SIGNAL (programming language); Computer science; Signal processing; Statistics; Sampling (signal processing); Telecommunications; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.00005406099,0.0001259514,0.0002640715,0.00005635538,0.0000478373,0.00001141185,0.00004718808,0.00007101226,0.000001038698],"category_scores_gemma":[0.000002760427,0.0001271785,0.00006497689,0.0001293878,0.00003520912,0.00002101266,5.01766e-7,0.0001131472,5.967576e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004568054,"about_ca_system_score_gemma":0.00002683416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000144114,"about_ca_topic_score_gemma":0.00001561194,"domain_scores_codex":[0.9993123,0.00002173986,0.0002310033,0.0001385019,0.0001061241,0.000190363],"domain_scores_gemma":[0.9996207,0.00007282607,0.0000750003,0.0001219528,0.00006452326,0.00004497386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002912111,0.00001587002,0.000001457439,0.0001220358,0.00005937741,4.422194e-7,0.000425199,0.7745888,0.2225352,0.0002227472,0.00005886229,0.001940822],"study_design_scores_gemma":[0.0002588587,0.0002167028,0.000005351176,0.0001679132,0.0000419463,0.000006614346,0.0001089996,0.7533178,0.245738,0.00003394957,0.0000128417,0.00009110059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.321324,0.0004041672,0.677617,0.00003312927,0.00009510266,0.0002924018,0.0000160686,0.0001229103,0.00009528462],"genre_scores_gemma":[0.9986326,0.0001146075,0.001179662,0.000007954845,0.0000222506,0.00001233516,0.00000154501,0.00002326785,0.000005841651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6773086,"threshold_uncertainty_score":0.5186187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343098525040044,"score_gpt":0.2262790512663768,"score_spread":0.2128480660159764,"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."}}