{"id":"W2917686006","doi":"10.1364/ofc.2019.m1b.2","title":"Noise Mitigation of Random Data Signals Through Linear Temporal Sampling Based on the Talbot Effect","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Photonic Communication Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Lossless compression; Sampling (signal processing); Noise (video); Waveform; Computer science; Random noise; Algorithm; Artificial intelligence; Data compression; Telecommunications; Computer vision; Filter (signal processing); Radar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003952531,0.000407445,0.0002865501,0.000358967,0.0001838405,0.0005689047,0.0007421757,0.0003102213,0.0009657477],"category_scores_gemma":[0.001017625,0.0001619798,0.0001521109,0.0003560009,0.0006748587,0.0007100919,0.0008998665,0.0003498958,0.0004923656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003095675,"about_ca_system_score_gemma":0.0003358059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002687289,"about_ca_topic_score_gemma":0.0003689595,"domain_scores_codex":[0.9997119,0.00003456292,0.00001077487,0.00006536728,0.0001234563,0.00005392351],"domain_scores_gemma":[0.9995845,0.0001486827,0.0001043982,0.00006239801,0.00006688401,0.00003314903],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001943136,0.00004720964,0.0002924,0.0001043764,0.00001242655,0.0001113434,0.0001081151,0.002210872,0.9662914,0.005395299,0.000272118,0.02496023],"study_design_scores_gemma":[0.00001614404,0.0001757354,0.0002517795,0.000009701954,0.000008411401,0.0001289122,0.00002041715,0.03316738,0.9639817,0.0009027645,0.001321704,0.00001549058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5372431,0.00113332,0.4527467,0.0003056834,0.00008410127,0.00008281872,0.0001483305,0.001463791,0.006792149],"genre_scores_gemma":[0.9471695,0.0002985007,0.050053,0.0001114328,0.00002865439,0.00003549521,0.00009100208,0.00007505141,0.00213725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009657477,"threshold_uncertainty_score":0.003230751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419633372062866,"score_gpt":0.2996393562605978,"score_spread":0.2554430225399691,"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."}}