{"id":"W2047857691","doi":"10.1109/dtis.2006.1708690","title":"Hardware/software approache for the FPGA implementation of a fuzzy logic controller","year":2006,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Field-programmable gate array; Fuzzy logic; Computer science; Software; Programmable logic controller; Fuzzy electronics; Embedded system; Programmable logic device; Controller (irrigation); Gate array; Fuzzy control system; Programmable logic array; Computer hardware; Neuro-fuzzy; Operating system; 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":[0.0003344617,0.00008394315,0.0001371139,0.00005269652,0.00004949771,0.00002553592,0.0001375107,0.00003975385,0.00007890086],"category_scores_gemma":[0.00002681862,0.0000544335,0.00007233485,0.0001114389,0.00002048172,0.00004277164,0.00001597479,0.00005342752,0.00001370717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004224797,"about_ca_system_score_gemma":0.00001286288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000382264,"about_ca_topic_score_gemma":0.0001507926,"domain_scores_codex":[0.9992617,0.00002086103,0.0002304977,0.00009721919,0.0001801094,0.0002096214],"domain_scores_gemma":[0.9994555,0.0002731261,0.00002709007,0.0001571077,0.00007044012,0.00001675685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001958018,0.000187218,0.0583165,0.002071043,0.001307906,0.00000433235,0.001432433,0.2017152,0.03413402,0.03733742,0.452711,0.2105871],"study_design_scores_gemma":[0.01939727,0.0005680582,0.201381,0.0001134011,0.000298053,0.00003687878,0.007123876,0.2231117,0.1806058,0.01171691,0.354021,0.001626019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0389807,0.001318091,0.9405422,0.0003260177,0.0004220605,0.003520987,0.00009362032,0.0005617127,0.01423464],"genre_scores_gemma":[0.9959906,0.000003533478,0.002574627,0.0000151193,0.0001522855,0.0003962056,0.00001743282,0.00002231689,0.0008278547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9570099,"threshold_uncertainty_score":0.2219734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03327438436580994,"score_gpt":0.2915153083022068,"score_spread":0.2582409239363969,"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."}}