{"id":"W2908306509","doi":"10.1109/jsen.2018.2890094","title":"A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation","year":2018,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Else Kröner-Fresenius-Stiftung; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Odometer; Inertial navigation system; Global Positioning System; Quadcopter; Computer science; Inertial measurement unit; Dead reckoning; Kalman filter; Real-time computing; Extended Kalman filter; Power consumption; Navigation system; Simulation; Engineering; Power (physics); Artificial intelligence; Aerospace engineering; Inertial frame of reference; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004384356,0.0002769594,0.0002874146,0.0002155908,0.0002275225,0.0001478224,0.0001266381,0.0001832198,0.00007381969],"category_scores_gemma":[0.0001121182,0.000246294,0.0001987067,0.0002162477,0.00003957072,0.0001218901,0.0000041094,0.0003420155,0.00009429309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001707695,"about_ca_system_score_gemma":0.00005788404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001335075,"about_ca_topic_score_gemma":0.000005188406,"domain_scores_codex":[0.9985291,0.0001190617,0.0003844597,0.0002121467,0.0003459425,0.0004093124],"domain_scores_gemma":[0.9989561,0.0002613924,0.0001052139,0.0002170738,0.000199563,0.0002606947],"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":[0.0002547406,0.00002494587,0.0001848676,0.00006953922,0.00008444276,0.00002867596,0.000220954,0.9548399,0.01928302,0.00003106779,0.01706453,0.007913279],"study_design_scores_gemma":[0.001714651,0.0006440888,0.000296955,0.000137797,0.00006316561,0.00006203946,0.000010331,0.8758729,0.1179332,0.0001622469,0.00280585,0.0002968083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6408297,0.00001522887,0.3558357,0.0001708327,0.001985461,0.0003360325,0.00001472679,0.0001519361,0.0006603876],"genre_scores_gemma":[0.9838298,0.000006527778,0.01375566,0.0001831593,0.00187732,0.00000473125,0.00002057954,0.00008983319,0.0002324245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.343,"threshold_uncertainty_score":0.9999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01222257140764144,"score_gpt":0.2433722417392004,"score_spread":0.2311496703315589,"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."}}