{"id":"W2172208619","doi":"10.1109/iscas.2009.5118265","title":"Efficient hardware implementation of hybrid cosine-fourier-wavelet transforms on a single FPGA","year":2009,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Field-programmable gate array; Computer science; Discrete cosine transform; Discrete wavelet transform; Parallel computing; Throughput; Fast Fourier transform; Discrete Fourier transform (general); Discrete Hartley transform; Multiplication (music); Computer hardware; Transformation matrix; Transformation (genetics); Matrix multiplication; Wavelet; Algorithm; Wavelet transform; Fourier transform; Mathematics; Fractional Fourier transform; Artificial intelligence; Image (mathematics); Fourier analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004021558,0.0001293525,0.0001737521,0.0001305093,0.00007926103,0.00008447546,0.000343783,0.00001981843,0.00006056041],"category_scores_gemma":[0.0000152489,0.0001021574,0.00009295139,0.0002716109,0.00002064526,0.0001294992,0.0000207549,0.00007329616,0.00001730246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004244853,"about_ca_system_score_gemma":0.00004812635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002736913,"about_ca_topic_score_gemma":0.000002678286,"domain_scores_codex":[0.9987434,0.00006920449,0.0002911332,0.0002695826,0.0003742391,0.0002524651],"domain_scores_gemma":[0.9993658,0.00005735154,0.00007537848,0.0003370649,0.00009752042,0.00006685063],"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":[0.00003575461,0.0002984919,0.000007850807,0.00001329821,0.00001187232,0.00003846025,0.0008309209,0.000986496,0.05375051,0.009258575,0.001798972,0.9329688],"study_design_scores_gemma":[0.001148508,0.001027082,0.0009850876,0.00003315125,0.000007809327,0.00002758645,0.00006479885,0.02586664,0.9676064,0.002152703,0.0008911992,0.00018905],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262045,0.00001894271,0.8673214,0.001211954,0.0001029813,0.0001740715,0.000005106091,0.0000865934,0.004874511],"genre_scores_gemma":[0.8898077,0.000001172423,0.1088498,0.001078576,0.00003040726,0.000002467783,0.000004437317,0.000005217425,0.0002201874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9327797,"threshold_uncertainty_score":0.4165856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427515437627339,"score_gpt":0.2982569867004077,"score_spread":0.2739818323241343,"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."}}