{"id":"W3198411447","doi":"10.3390/s22030853","title":"Optimizing the Energy Efficiency of Unreliable Memories for Quantized Kalman Filtering","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Agence Nationale de la Recherche","keywords":"Kalman filter; Energy consumption; Computer science; Quantization (signal processing); Energy (signal processing); Computation; Filter (signal processing); Reduction (mathematics); Algorithm; Control theory (sociology); Mathematics; Artificial intelligence; Engineering; Statistics","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":[0.0005129133,0.0004494892,0.0004757323,0.0002794578,0.0003384086,0.0006157356,0.0009147475,0.000400394,0.0009968218],"category_scores_gemma":[0.002160985,0.0001871632,0.0001643432,0.0004713651,0.0005192594,0.001120639,0.0005146568,0.0003771021,0.0001351105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006875693,"about_ca_system_score_gemma":0.0005361286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00259465,"about_ca_topic_score_gemma":0.003136607,"domain_scores_codex":[0.9996729,0.00008832949,0.00002054078,0.00004721543,0.0001251997,0.00004582705],"domain_scores_gemma":[0.999368,0.0003568312,0.0000867914,0.00009054921,0.00008630516,0.00001151352],"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":[0.0002306667,0.00004563971,0.0007362414,0.00008153154,0.00003286744,0.00007265298,0.0000827213,0.8843299,0.01965542,0.02007462,0.0006139897,0.07404372],"study_design_scores_gemma":[0.00001190168,0.00004614198,0.0001365906,0.000006402151,0.00001069055,0.00001397865,0.00001098661,0.9853898,0.01046538,0.003406855,0.0004938127,0.000007467825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08911776,0.0004509215,0.9080405,0.0001425673,0.00002858603,0.00002403182,0.00003600366,0.0003024066,0.001857198],"genre_scores_gemma":[0.9311483,0.0001859,0.06755589,0.00002855465,0.00001545064,0.00004252022,0.00001943308,0.00002821996,0.0009757814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00259465,"threshold_uncertainty_score":0.00515908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02189568285735548,"score_gpt":0.257955113578236,"score_spread":0.2360594307208805,"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."}}