{"id":"W2096029268","doi":"10.1109/tsp.2004.828936","title":"Efficient Design of Oversampled NPR GDFT Filterbanks","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Filter bank; Aliasing; Distortion (music); Filter design; Filter (signal processing); Prototype filter; Algorithm; Computer science; Adaptive filter; Control theory (sociology); Mathematics; Telecommunications; Bandwidth (computing); Artificial intelligence; Computer vision","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":[0.000567745,0.0002106922,0.0002573658,0.0002614832,0.0003188705,0.0001529481,0.0005226867,0.00009238302,0.00003474543],"category_scores_gemma":[0.000007347645,0.0001959507,0.0001322612,0.000727478,0.00009602988,0.0002969054,0.000003403051,0.0002781875,0.00002595989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009359671,"about_ca_system_score_gemma":0.0002860595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002540265,"about_ca_topic_score_gemma":7.623621e-7,"domain_scores_codex":[0.9982346,0.0001450981,0.0003721513,0.0004232098,0.0004842081,0.0003406992],"domain_scores_gemma":[0.9990563,0.0002111595,0.0001446847,0.000316094,0.0001698499,0.0001019549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007896938,0.0002396306,2.739781e-7,0.00004223489,0.0000164325,0.00001729981,0.0009726381,0.7825081,0.0776725,0.0001449173,0.000006892015,0.1383001],"study_design_scores_gemma":[0.001542004,0.0003318932,0.0000131634,0.0002513106,0.00003493686,0.00004391156,0.00003712561,0.3845665,0.6098366,0.003011879,0.00002845198,0.0003022432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002636425,0.0001154641,0.9962676,0.0001425619,0.0001982213,0.0001876507,0.000003281559,0.0001663873,0.0002824062],"genre_scores_gemma":[0.7128901,0.000002080196,0.286825,0.0001672003,0.00002194933,0.00001045748,2.013067e-7,0.00001377537,0.00006922112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7102537,"threshold_uncertainty_score":0.7990636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0387772456183838,"score_gpt":0.2833965363452908,"score_spread":0.244619290726907,"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."}}