{"id":"W2116377224","doi":"10.1109/aps.2007.4396383","title":"Effect of temporal correlation on the adaptive beamforming performance","year":2007,"lang":"en","type":"article","venue":"","topic":"Direction-of-Arrival Estimation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Correlation; Interference (communication); SIGNAL (programming language); Noise (video); Null (SQL); Signal-to-noise ratio (imaging); Adaptive beamformer; Beamforming; Computer science; Algorithm; Statistics; Electronic engineering; Mathematics; Telecommunications; Engineering; Artificial intelligence; Channel (broadcasting); Data mining","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":[0.00252214,0.000835378,0.0005877482,0.0006069645,0.0005603843,0.0007662106,0.000441682,0.0006348203,0.001948102],"category_scores_gemma":[0.02814034,0.0004244996,0.0004393306,0.001163043,0.0007734203,0.001156266,0.0009573591,0.0008051977,0.0003370188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005628456,"about_ca_system_score_gemma":0.001595775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001552207,"about_ca_topic_score_gemma":0.001475482,"domain_scores_codex":[0.9981015,0.0003767434,0.0001459103,0.0002740388,0.0007843543,0.0003174853],"domain_scores_gemma":[0.9766966,0.01760303,0.001427167,0.001306858,0.002491942,0.0004743088],"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":[0.003782607,0.0002584252,0.03090076,0.0006663186,0.0004217077,0.00270941,0.0004605241,0.5251495,0.2597803,0.007453645,0.001680483,0.1667364],"study_design_scores_gemma":[0.00008262227,0.001486779,0.03117662,0.00008641614,0.0004698272,0.002772634,0.0001879765,0.6882854,0.2708583,0.002190962,0.002259323,0.0001431973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.640071,0.001867928,0.3485161,0.0004239045,0.0002410764,0.0000631206,0.0002670175,0.001398408,0.007151434],"genre_scores_gemma":[0.9816533,0.0005995334,0.01644864,0.00009586077,0.00005046437,0.00004172451,0.0001876983,0.0001457829,0.000777083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00252214,"threshold_uncertainty_score":0.01333851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141548983353531,"score_gpt":0.2604822405350971,"score_spread":0.2490667507015618,"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."}}