{"id":"W1546169424","doi":"10.1109/icc.2015.7248735","title":"Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity","year":2015,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"RSS; Euclidean distance; Computer science; Cosine similarity; Antenna diversity; Similarity (geometry); Signal strength; Fingerprint recognition; Trigonometric functions; Key (lock); Algorithm; Discrete cosine transform; Indoor positioning system; Fingerprint (computing); Computer vision; Artificial intelligence; Pattern recognition (psychology); Wireless; Mathematics; Telecommunications; Image (mathematics); Computer security","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":[0.0006904194,0.0006323209,0.001026159,0.001424049,0.0005400751,0.0008641581,0.001394745,0.0007738508,0.001061632],"category_scores_gemma":[0.002617352,0.0002962518,0.0005148923,0.002414088,0.0003934351,0.001391868,0.000827019,0.0007246452,0.0008465072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003980004,"about_ca_system_score_gemma":0.0008685046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002916877,"about_ca_topic_score_gemma":0.001472996,"domain_scores_codex":[0.9987736,0.0001824016,0.00009185298,0.0002858617,0.0005676451,0.00009853785],"domain_scores_gemma":[0.9991633,0.0001512561,0.0001030059,0.0001612377,0.0003805229,0.00004065466],"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":[0.0004011786,0.000115924,0.004370694,0.0002805297,0.0001055556,0.0003544443,0.0002322811,0.0713496,0.05596997,0.006851591,0.004206507,0.8557616],"study_design_scores_gemma":[0.0001035261,0.0004627714,0.007380443,0.00005888395,0.000103919,0.002179261,0.0002022238,0.8931079,0.07405573,0.004276038,0.01795417,0.0001151334],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04269196,0.001335045,0.9512328,0.0001541293,0.0002392537,0.00008914919,0.0001110573,0.00114433,0.003002306],"genre_scores_gemma":[0.5503777,0.001461399,0.4405626,0.0001417157,0.0001540588,0.0001749128,0.0005118845,0.00009066251,0.006525043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002916877,"threshold_uncertainty_score":0.00579977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02584608743093766,"score_gpt":0.2211795704525686,"score_spread":0.1953334830216309,"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."}}