{"id":"W2050037170","doi":"10.1109/mwscas.2011.6026510","title":"An adaptive bistable system based detector and its application in watermark extraction","year":2011,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Detector; Watermark; Gaussian noise; Noise (video); Bistability; Stochastic resonance; Computer science; SIGNAL (programming language); Gaussian; Amplitude; Digital watermarking; Additive white Gaussian noise; Signal-to-noise ratio (imaging); Pulse-amplitude modulation; Algorithm; Electronic engineering; Control theory (sociology); Physics; Pulse (music); Optics; Telecommunications; Artificial intelligence; Engineering; Optoelectronics; White noise; Image (mathematics)","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":[0.0002512824,0.0002651307,0.0002996186,0.0003769862,0.0001228846,0.0002760681,0.0003551138,0.0007013271,0.0007980152],"category_scores_gemma":[0.0009870238,0.0001474914,0.0001873925,0.0003913808,0.000370598,0.0006143331,0.0002897807,0.00035349,0.0002953863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001779189,"about_ca_system_score_gemma":0.0001390003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001817793,"about_ca_topic_score_gemma":0.000192072,"domain_scores_codex":[0.9998065,0.00003882858,0.000009353013,0.00004654535,0.00008669253,0.00001229101],"domain_scores_gemma":[0.9996921,0.0001505938,0.00003844134,0.0000304417,0.00007632232,0.00001217569],"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":[0.0002671295,0.00006707422,0.001334855,0.0002719625,0.00005616342,0.0003851787,0.0001139337,0.02971009,0.6166335,0.02313877,0.0007189617,0.3273023],"study_design_scores_gemma":[0.00003270925,0.0004059739,0.00152687,0.00002999841,0.0000502012,0.001251575,0.0000443489,0.6957197,0.2879044,0.005236726,0.007742867,0.00005464355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03907334,0.001680146,0.9560215,0.0001914563,0.0000736203,0.00003265377,0.00002057894,0.0003459593,0.002560582],"genre_scores_gemma":[0.618224,0.001757179,0.3758128,0.0002228326,0.00007852205,0.00005123678,0.0000576237,0.00003713823,0.003758651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007980152,"threshold_uncertainty_score":0.002669632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0291560956486077,"score_gpt":0.2570543818333837,"score_spread":0.227898286184776,"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."}}