{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005960262,0.000528695,0.0005357639,0.0003847545,0.0002299551,0.0006899306,0.0003681061,0.0004983342,0.001102263],"category_scores_gemma":[0.002338805,0.0002107868,0.0004925419,0.0006535402,0.000497222,0.0007348928,0.0004713226,0.000714271,0.0005002961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002474819,"about_ca_system_score_gemma":0.0004499211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001504461,"about_ca_topic_score_gemma":0.00158533,"domain_scores_codex":[0.9994822,0.0001709034,0.00002885159,0.00006609752,0.0002202646,0.00003166948],"domain_scores_gemma":[0.9990853,0.0005432532,0.00008072579,0.0001038992,0.0001661394,0.00002062267],"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":[0.0003284658,0.0001052803,0.0006597484,0.0004256517,0.00009720984,0.0001728595,0.0001527372,0.5301241,0.0520287,0.0317485,0.003004247,0.3811525],"study_design_scores_gemma":[0.0000107512,0.0000901736,0.000273668,0.00002185356,0.00001465568,0.0001213269,0.000022305,0.9778411,0.01353971,0.005481185,0.002561826,0.0000215204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007809845,0.0009193734,0.989467,0.00006954322,0.00003906956,0.00001573284,0.00003861992,0.0001555182,0.001485388],"genre_scores_gemma":[0.3648022,0.003295007,0.6267877,0.0001169651,0.0001874668,0.00008076159,0.000372825,0.000099636,0.004257412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001504461,"threshold_uncertainty_score":0.003687441,"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."}}