{"id":"W1990583874","doi":"10.1007/s11265-006-5919-9","title":"Design and Implementation of Flexible Resampling Mechanism for High-Speed Parallel Particle Filters","year":2006,"lang":"en","type":"article","venue":"The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Resampling; Mechanism (biology); Speedup; Particle filter; Parallel computing; Auxiliary particle filter; Algorithm; Filter (signal processing); Artificial intelligence; Physics; Kalman filter; Ensemble Kalman filter; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007070596,0.0006816016,0.0005965403,0.0007439918,0.000651583,0.0008748844,0.002139929,0.0008500127,0.002808923],"category_scores_gemma":[0.001317487,0.0005671767,0.0004228744,0.0004866962,0.0002771924,0.0007945138,0.0004957261,0.0005817727,0.0008216881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005316685,"about_ca_system_score_gemma":0.0009040009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00204514,"about_ca_topic_score_gemma":0.002568449,"domain_scores_codex":[0.9996372,0.00005037192,0.00003338433,0.0000804008,0.0001504542,0.00004810012],"domain_scores_gemma":[0.9992931,0.0001236641,0.00009240855,0.0001815903,0.000272463,0.00003670441],"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":[0.001466776,0.0003227169,0.001581233,0.0002820836,0.0002575725,0.0004156289,0.0002058299,0.05144863,0.3583385,0.02452453,0.007508056,0.5536485],"study_design_scores_gemma":[0.0003023083,0.0005575151,0.001333085,0.00001884269,0.0001274316,0.0005077748,0.00002610521,0.7214415,0.2565916,0.002983966,0.01603249,0.00007739309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01358315,0.0002126093,0.9831844,0.00008318543,0.00009790569,0.00008244588,0.00003797597,0.001645656,0.00107264],"genre_scores_gemma":[0.3270197,0.0001572577,0.6694023,0.0001388424,0.00007952934,0.0001874937,0.0001756918,0.0001040249,0.002735152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002808923,"threshold_uncertainty_score":0.009396791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01699236377702789,"score_gpt":0.2817532261074687,"score_spread":0.2647608623304408,"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."}}