{"id":"W2131258459","doi":"10.1109/tsp.2003.810285","title":"A new numerical fourier transform in d-dimensions","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Discrete Fourier transform (general); Discrete-time Fourier transform; Non-uniform discrete Fourier transform; Fractional Fourier transform; Fast Fourier transform; Fourier transform; Mathematics; Algorithm; Discrete sine transform; Pseudo-spectral method; Cyclotomic fast Fourier transform; Aliasing; Prime-factor FFT algorithm; Discrete Fourier series; Split-radix FFT algorithm; Harmonic wavelet transform; Short-time Fourier transform; Mathematical analysis; Frequency domain; Fourier inversion theorem; Fourier analysis; Computer science; Wavelet transform; Filter (signal processing); Discrete 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001466759,0.000148349,0.0001349833,0.0002180834,0.0001535375,0.000224535,0.0002073024,0.00004797105,0.0001079323],"category_scores_gemma":[0.000001730528,0.0001431515,0.00006917577,0.0007693793,0.00001555558,0.00131016,6.213868e-7,0.0001888899,0.00005004335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006834847,"about_ca_system_score_gemma":0.0002476669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001756087,"about_ca_topic_score_gemma":0.0000115105,"domain_scores_codex":[0.9987708,0.00004807329,0.0002754169,0.0003174128,0.0002871713,0.0003011716],"domain_scores_gemma":[0.9995813,0.00005072102,0.00003927683,0.000149645,0.00003374543,0.00014528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001628339,0.0001476873,0.000005957557,0.00001044929,0.000006496385,0.00001151731,0.0008456159,0.002459364,0.001843717,0.001243639,0.0001342229,0.993275],"study_design_scores_gemma":[0.005831098,0.001280459,0.0001613916,0.0004373931,0.00006127833,0.0002680926,0.000502873,0.4431609,0.4763603,0.04972434,0.02044539,0.001766504],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005751958,0.00003642659,0.9943029,0.0003446058,0.0001214582,0.0001642795,0.000001553164,0.0001084505,0.004345115],"genre_scores_gemma":[0.9225821,0.000002893642,0.07652666,0.0003789226,0.00001059901,0.00001851032,4.925977e-7,0.00001206013,0.0004677076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9915085,"threshold_uncertainty_score":0.5837548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03088733235383881,"score_gpt":0.2774874548557719,"score_spread":0.2466001225019331,"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."}}