{"id":"W2084507143","doi":"10.1117/12.878849","title":"Photoacoustic sonar: principles of operation, imaging, and signal-to-noise analysis in time and frequency domains","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Innovation Foundation","keywords":"Sonar; Acoustics; Pulse compression; Sonar signal processing; SIGNAL (programming language); Matched filter; Signal processing; Frequency domain; Radar; Signal-to-noise ratio (imaging); Waveform; Filter (signal processing); Time domain; Noise (video); Marine mammals and sonar; Computer science; Optics; Physics; Telecommunications; Artificial intelligence; Computer vision; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005432625,0.0004821695,0.0004340124,0.000593144,0.0002392649,0.0009941236,0.0008836296,0.0007986896,0.001118644],"category_scores_gemma":[0.0006069957,0.0004479059,0.0001855975,0.0006261739,0.001130659,0.0009847657,0.0005912272,0.001034357,0.0008328802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003121548,"about_ca_system_score_gemma":0.0006020995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003031578,"about_ca_topic_score_gemma":0.0004099119,"domain_scores_codex":[0.9995438,0.00005204137,0.00001849127,0.00005114043,0.0003136689,0.00002091013],"domain_scores_gemma":[0.9996288,0.0001431686,0.00005599211,0.00003693011,0.0001161272,0.00001895033],"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":[0.0001361603,0.00009394904,0.0007385564,0.001131053,0.00004181303,0.0003023572,0.0002494013,0.007108764,0.6201457,0.04696149,0.00346514,0.3196255],"study_design_scores_gemma":[0.00005227854,0.001247048,0.003729452,0.0001872184,0.00005030902,0.006551282,0.0001820942,0.07454325,0.6988748,0.0219002,0.1924699,0.0002120666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007527722,0.007013433,0.9795504,0.0003768963,0.0001379086,0.000145052,0.0000854594,0.00074723,0.004415909],"genre_scores_gemma":[0.1347355,0.01185179,0.8395302,0.0004256538,0.0003041358,0.0004241885,0.0001617767,0.0001328391,0.01243404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001118644,"threshold_uncertainty_score":0.003742218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008635738259168853,"score_gpt":0.2032533045441068,"score_spread":0.194617566284938,"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."}}