{"id":"W2148632366","doi":"10.1007/s11265-013-0798-3","title":"Efficient Uniform Quantization Likelihood Evaluation for Particle Filters in Embedded Implementations","year":2013,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Concordia University; Polytechnique Montréal","keywords":"Speedup; Quantization (signal processing); Computer science; Software; Algorithm; Software implementation; Particle filter; Floating point; Parallel computing; Computer engineering; Computer hardware; Artificial intelligence; Programming language; Kalman filter","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":[0.00176342,0.00009681183,0.0001800445,0.0001672177,0.0001499297,0.0004658852,0.0003172597,0.00004553222,0.00001596636],"category_scores_gemma":[0.00006448246,0.00007901333,0.00005342705,0.0004325841,0.00001569018,0.0006460745,0.00002650176,0.0001063159,0.000006510832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070409,"about_ca_system_score_gemma":0.0001924307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000322914,"about_ca_topic_score_gemma":0.000004002249,"domain_scores_codex":[0.9981462,0.0001173605,0.0007884091,0.0001568649,0.0005418482,0.0002492607],"domain_scores_gemma":[0.9981328,0.0001441294,0.0006245518,0.0001253832,0.0008879534,0.00008522206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002980779,0.000246436,0.002011459,0.0001770722,0.00002542388,0.000003663764,0.003688837,0.8454013,0.01082825,0.001651355,0.002798786,0.1331376],"study_design_scores_gemma":[0.0008981416,0.0001238061,0.001024748,0.0002706733,0.00001551005,0.0000301453,0.001017082,0.9952162,0.0005757757,0.0006345322,0.00009678811,0.00009658513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2279741,0.0003892597,0.7705078,0.00025009,0.0003941823,0.0004267619,0.000002392558,0.0000206231,0.00003486115],"genre_scores_gemma":[0.9846909,0.000003114629,0.01504195,0.00004524261,0.0001542737,0.00004151903,0.000005983637,0.000008284565,0.000008742996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7567168,"threshold_uncertainty_score":0.4492542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492705843829245,"score_gpt":0.3081439200539794,"score_spread":0.273216861615687,"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."}}