{"id":"W2757145559","doi":"","title":"RSS-based WLAN Indoor Positioning and Tracking System Using Compressive Sensing and Its Implementation on Mobile Devices","year":2010,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Strong","keywords":"RSS; Computer science; Indoor positioning system; Real-time computing; Mobile device; Hybrid positioning system; Tracking (education); Compressed sensing; Location awareness; Tracking system; Embedded system; Global Positioning System; Positioning system; Computer vision; Engineering; Artificial intelligence; Computer network; Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002946511,0.0002556147,0.0003663066,0.0002799458,0.0001680894,0.0003811958,0.0005704783,0.0004244881,0.001304873],"category_scores_gemma":[0.0006353877,0.0001421502,0.0001886098,0.0003683499,0.0001672757,0.0005758722,0.000370088,0.0002489312,0.000522178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001762843,"about_ca_system_score_gemma":0.0001898624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005696211,"about_ca_topic_score_gemma":0.0005389774,"domain_scores_codex":[0.9997119,0.0000721535,0.00001682906,0.0000523282,0.0001245181,0.0000222658],"domain_scores_gemma":[0.9997496,0.00005853897,0.00002752871,0.000062254,0.00009023856,0.00001190461],"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":[0.0007277382,0.0001815198,0.00282016,0.0001895654,0.00007063493,0.0002736973,0.0001960995,0.05885767,0.2749816,0.007596218,0.004075133,0.65003],"study_design_scores_gemma":[0.0001279314,0.0009967859,0.003885386,0.00003535649,0.00008539156,0.0008746964,0.00008545788,0.83097,0.1513716,0.001342467,0.01017038,0.00005449696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1174699,0.0003033839,0.8753949,0.0002336586,0.00008487142,0.00007320575,0.00007668239,0.001894546,0.004468845],"genre_scores_gemma":[0.7146664,0.0003780101,0.2801083,0.00008663079,0.00006655732,0.000101785,0.0001350986,0.00003255385,0.004424694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001304873,"threshold_uncertainty_score":0.004365206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01442507212550492,"score_gpt":0.3212413538031006,"score_spread":0.3068162816775957,"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."}}