{"id":"W2150410354","doi":"10.1109/tftsa.1992.274173","title":"The design of compactly supported orthonormal wavelets with integer scaling factors","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Orthonormal basis; Wavelet; Scaling; Integer (computer science); Quadrature mirror filter; Mathematics; Filter (signal processing); Filter bank; Lossless compression; Fractal; Filtering theory; Algorithm; Orthonormality; Discrete wavelet transform; Computer science; Applied mathematics; Wavelet transform; Discrete mathematics; Filter design; Mathematical analysis; Artificial intelligence; Prototype filter; Computer vision; Physics; Geometry; Data compression","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":[0.001047494,0.0007060098,0.0005282044,0.0005692757,0.0002080259,0.0007497961,0.00051095,0.0007551268,0.0008934159],"category_scores_gemma":[0.00251516,0.0005042696,0.0003819311,0.0004885109,0.0006170847,0.0008582817,0.0006236206,0.0007101136,0.0004487851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000236495,"about_ca_system_score_gemma":0.0002963137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001290588,"about_ca_topic_score_gemma":0.0001467199,"domain_scores_codex":[0.9995155,0.0001620665,0.00004471507,0.00007759625,0.0001642213,0.00003589012],"domain_scores_gemma":[0.999151,0.0002466725,0.000173705,0.0001826386,0.0001953306,0.00005066763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000404565,0.0001165685,0.0005381352,0.0004243146,0.00006267492,0.0003061681,0.0003315177,0.09126056,0.2028384,0.3185293,0.004288731,0.380899],"study_design_scores_gemma":[0.0001403694,0.0005699423,0.0003652637,0.0000764227,0.00005239693,0.0006119891,0.00007406083,0.8453498,0.0854827,0.04101625,0.02620708,0.00005367613],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01096586,0.000237747,0.9873488,0.00007218473,0.00004589977,0.00002313928,0.00001780951,0.0001088489,0.001179721],"genre_scores_gemma":[0.1646356,0.0008453922,0.8316141,0.0001088399,0.00008309781,0.0001262833,0.0001148982,0.00008001779,0.002391814],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001047494,"threshold_uncertainty_score":0.005539715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03567071483112276,"score_gpt":0.2639260187522674,"score_spread":0.2282553039211446,"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."}}