{"id":"W3173320109","doi":"10.1145/3463526","title":"Unlocking the Beamforming Potential of LoRa for Long-range Multi-target Respiration Sensing","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; CHIST-ERA; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Chinese Academy of Sciences; Youth Innovation Promotion Association; Agence Nationale de la Recherche","keywords":"Beamforming; Computer science; Transmitter; Key (lock); Synchronization (alternating current); SIGNAL (programming language); Channel state information; Real-time computing; Channel (broadcasting); Electronic engineering; Wireless; Telecommunications; Engineering; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008254561,0.0006842265,0.0005062295,0.0004898758,0.00033171,0.0006384145,0.0006477442,0.0005801567,0.001389522],"category_scores_gemma":[0.001896111,0.0003349753,0.0004151412,0.0004447781,0.0006354346,0.001212458,0.00134767,0.0009841706,0.001311121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002850947,"about_ca_system_score_gemma":0.0004082011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000789108,"about_ca_topic_score_gemma":0.001361843,"domain_scores_codex":[0.9993317,0.0002519243,0.00002787143,0.0001048978,0.0002228319,0.00006087366],"domain_scores_gemma":[0.9988723,0.0004884695,0.0001369856,0.0001725147,0.0002571031,0.00007264199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003598554,0.0001174185,0.003355475,0.0005088346,0.00009798175,0.0004658725,0.0005577827,0.08272146,0.3621705,0.0198549,0.004040287,0.5257496],"study_design_scores_gemma":[0.00008213913,0.0006702507,0.002964299,0.0001030444,0.00006752084,0.001240047,0.0002897641,0.8310531,0.1092931,0.02025544,0.03380391,0.0001774549],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02205744,0.0009426269,0.9708566,0.0006381415,0.0001103222,0.00004836074,0.00004944662,0.001453876,0.003843231],"genre_scores_gemma":[0.5591582,0.001474344,0.4345441,0.0008562595,0.0002167289,0.0002033244,0.000146396,0.0001822048,0.003218498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001389522,"threshold_uncertainty_score":0.004648447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225004347819988,"score_gpt":0.2413049221978834,"score_spread":0.2290548787196835,"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."}}