{"id":"W3006474645","doi":"10.1007/s11760-021-01872-y","title":"Utilizing the wavelet transform’s structure in compressed sensing","year":2021,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials","funders":"National Heart, Lung, and Blood Institute; National Institutes of Health; American Heart Association","keywords":"Compressed sensing; Affine transformation; Wavelet; Transformation (genetics); Wavelet transform; Discrete wavelet transform; Noise reduction; Pattern recognition (psychology); Iterative reconstruction","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001071227,0.0001750398,0.00019677,0.00006035185,0.0001576974,0.0002459444,0.00008156447,0.00007483999,0.0000190466],"category_scores_gemma":[0.00001527794,0.0001397203,0.00003555577,0.0002370717,0.00007364192,0.0002873835,0.0000319386,0.0003280851,7.343344e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001979134,"about_ca_system_score_gemma":0.00003309303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001707729,"about_ca_topic_score_gemma":0.00004658138,"domain_scores_codex":[0.9991427,0.0000472321,0.0002082201,0.0002044088,0.0001260624,0.0002713527],"domain_scores_gemma":[0.9996586,0.00006714757,0.00002468065,0.0001348081,0.00007402792,0.00004075513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009192293,0.000006943915,0.00003917628,0.0001201632,0.00001523702,0.0001720367,0.001120952,0.0005480644,0.7129937,0.00003757913,0.0003254102,0.2846115],"study_design_scores_gemma":[0.0002632567,0.000008184326,0.0003294537,0.0004605528,0.00002283492,0.0001651427,0.0008960055,0.2360853,0.7566589,0.003525144,0.001335233,0.0002500202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7055638,0.01767845,0.2553367,0.001211007,0.0002227869,0.0004430407,0.00001880095,0.001193562,0.01833186],"genre_scores_gemma":[0.9907376,0.0001030117,0.008793145,0.0002352755,0.00007704199,0.000001389259,0.00000642363,0.0000314822,0.00001463947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2851738,"threshold_uncertainty_score":0.569763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01274957384709296,"score_gpt":0.2359281385609132,"score_spread":0.2231785647138202,"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."}}