{"id":"W1981339237","doi":"10.1541/ieejeiss.131.1081","title":"An Efficient IEEE-Compliant 8*8 Inv-DCT Architecture with 24 Adders","year":2011,"lang":"en","type":"article","venue":"IEEJ Transactions on Electronics Information and Systems","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Discrete cosine transform; Adder; Computer science; Integer (computer science); Encoding (memory); Algebraic number; Scheme (mathematics); Arithmetic; Inverse; Architecture; Basis (linear algebra); Algorithm; Parallel computing; Computational science; Computer hardware; Mathematics; Image (mathematics); Artificial intelligence; Telecommunications; 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":[0.0001972989,0.0001651911,0.000131049,0.0002635654,0.0002101555,0.0004011549,0.0002680223,0.00005075229,0.00001145743],"category_scores_gemma":[0.000001048987,0.0001315309,0.00003466552,0.0003015739,0.00003122038,0.001498588,0.000001934842,0.0001840936,0.0000347463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007058994,"about_ca_system_score_gemma":0.00009766079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004650352,"about_ca_topic_score_gemma":0.00003201479,"domain_scores_codex":[0.9988401,0.00005373272,0.0003287871,0.000180405,0.0002964683,0.0003005166],"domain_scores_gemma":[0.9993252,0.0000201877,0.0001173687,0.000325125,0.00008314286,0.0001289839],"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":[0.000542601,0.0009293925,0.00006635366,0.0003062064,0.0003479864,0.000009404623,0.0595091,0.2196546,0.001424162,0.1851806,0.0009491555,0.5310804],"study_design_scores_gemma":[0.002207171,0.004004538,0.0003151878,0.000118785,0.00003970839,0.0003776467,0.002091799,0.9624861,0.009574631,0.0006966204,0.01725213,0.0008356103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01150641,0.00003090034,0.9838799,0.00006427101,0.0003106056,0.0004079251,0.00001600001,0.0001746912,0.003609272],"genre_scores_gemma":[0.997306,0.0000170662,0.002253354,0.000262342,0.0000107882,0.00005719974,0.00001512368,0.000007450253,0.00007064469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9857996,"threshold_uncertainty_score":0.5363676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638297337067386,"score_gpt":0.2288205831852141,"score_spread":0.2024376098145403,"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."}}