{"id":"W3136990667","doi":"10.1109/bmsb49480.2020.9379587","title":"Indoor Object Localization and Tracking Using Deep Learning over Received Signal Strength","year":2020,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Received signal strength indication; Scheme (mathematics); Signal strength; Real-time computing; Computer vision; Perceptron; Multilayer perceptron; Artificial neural network; Trajectory; Object detection; Mobile device; SIGNAL (programming language); Pattern recognition (psychology); Wireless sensor network; Wireless; Telecommunications; Mathematics; Computer network","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":[0.0004266741,0.0008023488,0.0005997433,0.0007129041,0.0002750215,0.0005644925,0.001158744,0.0006678053,0.0009697825],"category_scores_gemma":[0.0009304379,0.0003651682,0.0004963862,0.0009497635,0.0003605364,0.001076621,0.001135897,0.0007250299,0.0005238606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006555308,"about_ca_system_score_gemma":0.0006258195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006318854,"about_ca_topic_score_gemma":0.007524303,"domain_scores_codex":[0.9996874,0.00004619146,0.00001432048,0.0001081671,0.00008576128,0.00005816362],"domain_scores_gemma":[0.9996912,0.00007987436,0.00005936682,0.00005844441,0.00008904428,0.00002209698],"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":[0.0001499221,0.0000983898,0.00334539,0.00009930864,0.00009030486,0.0001129444,0.00008171173,0.5569463,0.01448698,0.00396457,0.00177406,0.41885],"study_design_scores_gemma":[0.000003502495,0.00002248741,0.0004292112,0.000005876236,0.000009505164,0.00002362637,0.000006289556,0.9953964,0.002314584,0.00127674,0.000506167,0.000005605881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01580517,0.0002212207,0.9815367,0.00007144107,0.00002870421,0.0000122636,0.0000639677,0.001177614,0.001083003],"genre_scores_gemma":[0.7614102,0.000481851,0.2328841,0.0001562659,0.00006271987,0.00006501019,0.000445463,0.000078574,0.004415816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006318854,"threshold_uncertainty_score":0.01256418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644580708492488,"score_gpt":0.2195448749053153,"score_spread":0.2030990678203904,"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."}}