{"id":"W2221022228","doi":"10.1007/978-1-4615-0288-3_14","title":"Scalable Parallel Implementation of Wavelet Transforms","year":2003,"lang":"en","type":"book-chapter","venue":"High Performance Computing Systems and Applications","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Scalability; Wavelet transform; Wavelet; Parallel computing; Parallel algorithm; Wavelet packet decomposition; Algorithm; Workstation; Second-generation wavelet transform; Stationary wavelet transform; Artificial intelligence","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":[0.0004252315,0.000984443,0.0007740134,0.000774282,0.00046249,0.001203208,0.001601965,0.0005205148,0.01077114],"category_scores_gemma":[0.001551346,0.0004707109,0.0005467622,0.001447214,0.000350874,0.001724461,0.001158296,0.001065295,0.003161962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000551008,"about_ca_system_score_gemma":0.0009628256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002146761,"about_ca_topic_score_gemma":0.0041752,"domain_scores_codex":[0.9995983,0.00005012662,0.00002795026,0.00005590983,0.0001985057,0.00006913318],"domain_scores_gemma":[0.9993994,0.0001496132,0.00002921628,0.0002199178,0.000165447,0.00003637946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000902496,0.0003414285,0.0007607457,0.0003271665,0.0001221161,0.0003784167,0.0001983131,0.1069014,0.06590454,0.06854885,0.0410754,0.7145391],"study_design_scores_gemma":[0.000213315,0.0001147811,0.0003741799,0.00003140329,0.0000500315,0.0002078335,0.00006521879,0.8974096,0.04078194,0.04174364,0.01898198,0.00002601989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03196985,0.0007630076,0.9423887,0.0002886072,0.0002656729,0.00007609049,0.0002513669,0.006277281,0.01771937],"genre_scores_gemma":[0.2445177,0.0007807625,0.7328605,0.0001809833,0.0001642138,0.0003122075,0.001286546,0.0009427769,0.01895427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01077114,"threshold_uncertainty_score":0.03603309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01942683612865641,"score_gpt":0.2711117717307919,"score_spread":0.2516849356021355,"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."}}