{"id":"W2354835911","doi":"","title":"Theory and Applicaton of Compressive Sensing","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Compressed sensing; Orthonormal basis; Computer science; Nyquist–Shannon sampling theorem; SIGNAL (programming language); Nyquist rate; Basis (linear algebra); Algorithm; Sampling (signal processing); Matrix (chemical analysis); Sample (material); Signal reconstruction; Measure (data warehouse); Process (computing); Mathematical optimization; Signal processing; Computer vision; Mathematics; Telecommunications; Data mining; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006359541,0.0001011414,0.0001254455,0.00006252811,0.00005024253,0.00001910261,0.0001206652,0.00006378709,0.0000047857],"category_scores_gemma":[2.854991e-7,0.0001041424,0.0000281826,0.000091965,0.00009358567,0.00003222677,0.00006028196,0.0001604791,0.000006884144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000396081,"about_ca_system_score_gemma":0.000004211879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004255937,"about_ca_topic_score_gemma":0.000002423788,"domain_scores_codex":[0.9995545,0.00001076894,0.000142023,0.0001427078,0.00004231049,0.000107693],"domain_scores_gemma":[0.9995444,0.00008186761,0.00003260124,0.0002559789,0.00004428987,0.00004088818],"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":[0.000001573076,0.00001141437,0.0000350244,0.000014099,0.00001672359,3.38754e-7,0.00009419519,0.00007716697,0.7863053,0.01422746,0.0007076858,0.198509],"study_design_scores_gemma":[0.0001402028,0.000007472411,0.0008740161,0.00002193779,0.000021588,0.00005390997,0.00001860845,0.009127813,0.7964781,0.01984664,0.1731982,0.0002115291],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1688174,0.000152976,0.8284286,0.0000185734,0.00001523782,0.0003026732,0.000006690192,0.0004049239,0.001852896],"genre_scores_gemma":[0.8128194,0.00001678149,0.1869859,0.00004480954,0.00007359945,0.00002564935,0.000006120536,0.00001921676,0.000008571301],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.644002,"threshold_uncertainty_score":0.4246803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00368048763385717,"score_gpt":0.2081636360753914,"score_spread":0.2044831484415342,"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."}}