{"id":"W2207470643","doi":"10.1109/jsen.2015.2397874","title":"CMOS Image Sensor With Area-Efficient Block-Based Compressive Sensing","year":2015,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; CMC Microsystems","keywords":"Linearity; Integrator; Pixel; CMOS; Image sensor; Block (permutation group theory); Electronic engineering; Scalability; CMOS sensor; Computer science; Compressed sensing; Transistor; Encoding (memory); Materials science; Computer hardware; Electrical engineering; Engineering; Artificial intelligence; Voltage; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002277171,0.0003777769,0.0003905756,0.0002563079,0.0001812334,0.000224325,0.0001847628,0.0001209436,0.00001597264],"category_scores_gemma":[0.00004178113,0.0003102881,0.0001223227,0.0002473804,0.0001379092,0.0001052117,0.00001804434,0.0006812742,0.00004328251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797483,"about_ca_system_score_gemma":0.00009647988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001512946,"about_ca_topic_score_gemma":0.000004387857,"domain_scores_codex":[0.9981284,0.0001110772,0.0003738002,0.0002525023,0.0005690393,0.0005651607],"domain_scores_gemma":[0.9983945,0.00009167571,0.0001521445,0.000391256,0.0005390887,0.0004312875],"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":[0.00009433126,0.00004230992,0.00005444507,0.000009133822,0.00009023682,0.0017006,0.0003551782,0.9010824,0.07234863,0.000002290364,0.02360919,0.0006112655],"study_design_scores_gemma":[0.001258171,0.0001567447,0.00006812205,0.0003412912,0.00007754965,0.003819206,0.0003595101,0.6727766,0.31718,0.00005927897,0.003353341,0.0005502407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.925064,0.0001515109,0.06866244,0.0001548758,0.001066516,0.0002019587,0.000009130851,0.000868387,0.003821133],"genre_scores_gemma":[0.96678,0.000009981009,0.03245325,0.0001289417,0.0004447544,0.000001137893,0.000002207677,0.00009898053,0.00008077318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2448313,"threshold_uncertainty_score":0.9999349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02675725627970461,"score_gpt":0.2325344835298932,"score_spread":0.2057772272501886,"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."}}