{"id":"W2151353857","doi":"10.1109/icecs.2007.4511115","title":"Design and Implementation of a Decimation Filter For High Performance Audio Applications","year":2007,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advantage Forensics (Canada)","funders":"","keywords":"Decimation; ModelSim; Finite impulse response; Computer science; Filter (signal processing); Field-programmable gate array; Digital signal processing; Computer hardware; Cascaded integrator–comb filter; Filter design; Low-pass filter; High-pass filter; Digital filter; Electronic engineering; Embedded system; Root-raised-cosine filter; Algorithm; Engineering; VHDL; Computer vision","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":[0.0003863878,0.00005809437,0.00006204235,0.00009041938,0.00005598844,0.00005971285,0.0001316512,0.0000152348,0.00001181882],"category_scores_gemma":[0.000002761425,0.00005400642,0.0000122449,0.0001535091,0.00001281785,0.0008015108,0.00003161999,0.00001243386,0.000003558057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001796416,"about_ca_system_score_gemma":0.00002079993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001163122,"about_ca_topic_score_gemma":0.000005646948,"domain_scores_codex":[0.9993752,0.00000858148,0.0002376557,0.0001461026,0.0001053698,0.0001271187],"domain_scores_gemma":[0.9995438,0.0001145954,0.00009044422,0.000132876,0.00008813467,0.0000301822],"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":[0.0000137121,0.00002466014,0.0008937985,0.00004050658,0.00001058337,5.531471e-8,0.000390061,0.00002456214,0.01272931,0.1167528,0.000572538,0.8685474],"study_design_scores_gemma":[0.001693226,0.0007087942,0.08078539,0.00001226932,0.00001739158,0.000008107655,0.0002115736,0.03245984,0.8634444,0.0185116,0.001877402,0.0002699484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03381689,0.000006092548,0.9649779,0.0001010357,0.0000273165,0.0007880751,0.000003077588,0.0000362964,0.0002433387],"genre_scores_gemma":[0.6361189,0.000003202916,0.3636312,0.00008609573,0.000009965832,0.00008557818,0.00001380497,0.000002612223,0.00004856355],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8682774,"threshold_uncertainty_score":0.2202318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03136805823095339,"score_gpt":0.3073180147996482,"score_spread":0.2759499565686948,"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."}}