{"id":"W1989485711","doi":"10.5539/mas.v3n9p78","title":"Design of the License Plate Recognition Platform Based on the DSP Embedded System","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"License; Computer science; Digital signal processing; Intelligent transportation system; Identification (biology); Chip; Image processing; FIFO (computing and electronics); Computer hardware; Artificial intelligence; Embedded system; Computer vision; Real-time computing; Telecommunications; Image (mathematics); Engineering; Transport engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008414557,0.000168004,0.0001459389,0.0001102523,0.0002778788,0.0000693082,0.0004662827,0.00007457007,0.00001160671],"category_scores_gemma":[0.00002732616,0.0001065563,0.00004357204,0.0006582312,0.0001464491,0.0001447999,0.00002082321,0.0002254375,0.00008746592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001542819,"about_ca_system_score_gemma":0.0000565207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001655638,"about_ca_topic_score_gemma":8.071592e-7,"domain_scores_codex":[0.9985812,0.00002409519,0.0002438761,0.0002542439,0.000557626,0.0003389568],"domain_scores_gemma":[0.9991638,0.0001758813,0.00008552728,0.0004466216,0.00006630641,0.0000618284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005571769,0.0000284748,0.000001182763,0.00003046109,0.000005045501,0.000001310403,0.0006390944,0.2463529,0.72462,0.0004805363,0.00005485704,0.02773038],"study_design_scores_gemma":[0.0001987347,0.00002434338,0.0001751906,0.00008233867,0.00001014448,0.000004919612,0.00008470572,0.7900138,0.2080752,0.001211077,0.000004033936,0.0001154419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5754353,0.00001340135,0.3934942,0.0001880748,0.0002655909,0.001313485,0.00001774071,0.0004975165,0.02877475],"genre_scores_gemma":[0.9981412,0.000002412101,0.001525498,0.0002431233,0.00002745918,0.00003633236,0.000002124801,0.00001647337,0.00000540257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5436609,"threshold_uncertainty_score":0.434524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942230379862098,"score_gpt":0.2006985759922455,"score_spread":0.1712762721936245,"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."}}