{"id":"W2403170359","doi":"10.1109/isplc.2016.7476272","title":"SVD-based de-noising and parametric channel estimation for power line communication systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Singular value decomposition; Channel (broadcasting); Orthogonal frequency-division multiplexing; Computer science; Frequency domain; Algorithm; Hankel matrix; Matrix decomposition; Parametric statistics; Superposition principle; Exponential function; Frequency-division multiplexing; Matrix (chemical analysis); Estimation theory; Mathematics; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002964174,0.00008344782,0.00009974908,0.0001442528,0.00007925477,0.00004143738,0.0001424076,0.00005527579,0.000006637851],"category_scores_gemma":[0.0001298521,0.00006287853,0.00002364474,0.0001573073,0.00002270236,0.0001329912,0.00002345333,0.00004230082,0.000006601718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006093443,"about_ca_system_score_gemma":0.00001252142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002408904,"about_ca_topic_score_gemma":0.000006273523,"domain_scores_codex":[0.9995385,0.00002819471,0.0001777286,0.00007471365,0.00004835206,0.0001325524],"domain_scores_gemma":[0.9989647,0.0004342637,0.00003170168,0.0004574457,0.00006235631,0.00004954312],"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":[0.0001487476,0.0005169117,0.002100512,0.0007938849,0.0002767782,0.000001308584,0.0009757333,0.7331217,0.03167593,0.05809491,0.01743132,0.1548623],"study_design_scores_gemma":[0.0004885423,0.00003620384,0.0005525262,0.000102551,0.00001139386,0.000001982908,0.00002222037,0.9942342,0.001698418,0.0005488684,0.002186421,0.0001166758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07804389,0.002216304,0.9175187,0.0006106293,0.00007507333,0.0002903234,0.00001430108,0.0002638451,0.0009669599],"genre_scores_gemma":[0.9732946,0.0001919268,0.02626599,0.00002875064,0.00001115272,0.00008504413,0.00001648251,0.00002030284,0.00008571555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8952507,"threshold_uncertainty_score":0.2564112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01868938548223992,"score_gpt":0.2491058081067396,"score_spread":0.2304164226244997,"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."}}