{"id":"W4403826732","doi":"10.1109/access.2024.3486776","title":"Reduced Complexity Approximation and Design of Gaussian Impulse Response Filters and Wavelets","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Hellenic Foundation for Research and Innovation; University Research Council, Aga Khan University","keywords":"Finite impulse response; Computer science; Wavelet; Impulse response; Gaussian; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Physics; Mathematical analysis","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":[0.001252476,0.00009783963,0.0001436766,0.0001576377,0.00007273092,0.0004905895,0.0003439004,0.00004126822,0.000004351867],"category_scores_gemma":[0.00006893341,0.00008389556,0.00002133635,0.0003011611,0.0001081575,0.0009007344,0.0001229854,0.00008636186,0.000001674988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001498952,"about_ca_system_score_gemma":0.00005428273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002798556,"about_ca_topic_score_gemma":4.535299e-7,"domain_scores_codex":[0.9988071,0.0004163792,0.0001933342,0.0002940205,0.0001471171,0.0001419999],"domain_scores_gemma":[0.9992251,0.0003872175,0.00004898926,0.0002398554,0.0000387202,0.00006005257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005688231,0.00005029676,0.0000406147,0.0003281382,0.00005085599,0.0001621792,0.004708591,0.0001971106,0.6774985,0.00661632,0.002807664,0.3069709],"study_design_scores_gemma":[0.000630166,0.0002109358,0.008846564,0.0001986553,0.00002410438,0.0001545891,0.00002379248,0.5802546,0.3537915,0.05533983,0.0002043423,0.0003209389],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1821468,0.0002143776,0.8165427,0.0005695719,0.0002262149,0.000135023,0.000002103501,0.00006193056,0.0001012587],"genre_scores_gemma":[0.8404537,0.00001364503,0.1593229,0.0001140061,0.00002718733,0.000005350584,6.321692e-7,0.00000675014,0.00005576751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.658307,"threshold_uncertainty_score":0.4730766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09206705848233916,"score_gpt":0.3530429598608204,"score_spread":0.2609759013784813,"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."}}