{"id":"W2126908656","doi":"10.1109/pacrim.1999.799510","title":"A systolic array architecture for 2-D inverse wavelet transform","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 Victoria","funders":"","keywords":"Discrete wavelet transform; Wavelet; Wavelet transform; Inverse; Lifting scheme; Second-generation wavelet transform; Computer science; Systolic array; Architecture; Stationary wavelet transform; Wavelet packet decomposition; Algorithm; Mathematics; Artificial intelligence; Very-large-scale integration; Embedded system; Geometry","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":[0.0005028795,0.0001175557,0.0001471713,0.00008772357,0.0001084435,0.0000998343,0.0003742762,0.00005314601,0.00002437209],"category_scores_gemma":[0.00007678868,0.00009017548,0.0001127469,0.0002361521,0.00002459494,0.000178101,0.000009256483,0.00009288276,0.00002409145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001927942,"about_ca_system_score_gemma":0.00006757509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001060308,"about_ca_topic_score_gemma":0.00001384473,"domain_scores_codex":[0.9990762,0.00009241694,0.000143656,0.0002604702,0.000134707,0.0002925142],"domain_scores_gemma":[0.9993756,0.0001375641,0.00002428932,0.0003272994,0.00004777246,0.0000874722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000433371,0.0001222489,0.000008279566,0.0001543085,0.00005844395,0.00003325532,0.00446223,0.0001631084,0.07068019,0.6068573,0.01024211,0.3071752],"study_design_scores_gemma":[0.002282786,0.0002585121,0.00002165774,0.0000310332,0.00001957925,0.0002257516,0.00006524352,0.007522263,0.3813461,0.2290445,0.3786503,0.0005322543],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004784907,0.00003552137,0.9560982,0.0009280264,0.0002254708,0.0002447865,0.000001221985,0.0001242095,0.0418641],"genre_scores_gemma":[0.03941549,0.000002671683,0.9546309,0.002022497,0.00004130007,0.000029906,8.753713e-7,0.00001026535,0.00384606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3778128,"threshold_uncertainty_score":0.3677249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158162289446806,"score_gpt":0.2666070732029819,"score_spread":0.2450254503085139,"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."}}