{"id":"W2034150483","doi":"10.1117/12.831687","title":"A new deconvolution technique for time-domain signals in diffuse optical tomography without a priori information","year":2009,"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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Impulse response; Time domain; Computer science; SIGNAL (programming language); Blind deconvolution; A priori and a posteriori; Optics; Convolution (computer science); Shot noise; Signal processing; Noise (video); Physics; Acoustics; Algorithm; Artificial intelligence; Computer vision; Telecommunications; 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":[0.0009103899,0.0006776271,0.0004985242,0.0007389505,0.0003542126,0.0006918348,0.0007748104,0.001079467,0.00157609],"category_scores_gemma":[0.001311924,0.0003188128,0.0007673168,0.0006886064,0.000704603,0.001467418,0.001087785,0.001372616,0.001181144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003507384,"about_ca_system_score_gemma":0.0005572017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004530755,"about_ca_topic_score_gemma":0.0006139668,"domain_scores_codex":[0.999499,0.00009134276,0.00002800907,0.00009964684,0.0002487521,0.00003322259],"domain_scores_gemma":[0.9994891,0.0001643315,0.00005132191,0.0001133013,0.0001498087,0.00003214827],"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":[0.0002921698,0.0001278605,0.0003890215,0.0004068292,0.00008110832,0.0002641105,0.0001882741,0.01601264,0.5600525,0.03775765,0.001982372,0.3824455],"study_design_scores_gemma":[0.00004822695,0.000216653,0.001041975,0.00006136333,0.00008072167,0.002140751,0.00005019508,0.5477176,0.3975627,0.0164069,0.03453774,0.000135096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002641609,0.0001753024,0.9964702,0.00003838731,0.00003389572,0.00001594472,0.0000173418,0.0001587425,0.0004486051],"genre_scores_gemma":[0.03071399,0.0004262399,0.9661283,0.00006264548,0.00003800908,0.00005327443,0.00008323637,0.00009892608,0.002395335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00157609,"threshold_uncertainty_score":0.005272567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005160472246506623,"score_gpt":0.2099787908946164,"score_spread":0.2048183186481097,"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."}}