{"id":"W4242501859","doi":"10.32920/ryerson.14662389","title":"Wavelet OFDM for Power Line Communication","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Cyclic prefix; Power-line communication; Orthogonal frequency-division multiplexing; Computer science; Electronic engineering; Fast Fourier transform; Wavelet; Wavelet transform; Bandwidth (computing); Transmission (telecommunications); Communications system; Channel (broadcasting); Power (physics); Telecommunications; Algorithm; Engineering; Artificial intelligence","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":[0.0001732243,0.0002979607,0.0002393046,0.0002884023,0.0001675979,0.0005557728,0.0002532595,0.0005795325,0.003074206],"category_scores_gemma":[0.0004613923,0.0001125461,0.0001830314,0.0008501196,0.0002697275,0.0005222838,0.000310156,0.0009192896,0.001210751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002577839,"about_ca_system_score_gemma":0.0001739039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003256183,"about_ca_topic_score_gemma":0.0002267541,"domain_scores_codex":[0.9998391,0.0000343873,0.000006966909,0.00002787965,0.00007934807,0.00001237039],"domain_scores_gemma":[0.9998792,0.00004707194,0.00001647861,0.00002017369,0.00003102986,0.000006084219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001990974,0.0001038514,0.0005675105,0.0007047967,0.00004449264,0.0008351505,0.0002003988,0.03856156,0.09870384,0.2317137,0.0163728,0.6119928],"study_design_scores_gemma":[0.00006026819,0.0005732643,0.002139535,0.0003118051,0.00006364472,0.002450211,0.0001777284,0.5197515,0.05639627,0.1428573,0.2751365,0.00008199248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02038524,0.02086843,0.9344926,0.001066981,0.0006412424,0.00005757219,0.0001794272,0.0003926927,0.02191587],"genre_scores_gemma":[0.4661987,0.04465573,0.4407867,0.000575289,0.001301318,0.0001995139,0.0006081887,0.0001675942,0.04550708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003074206,"threshold_uncertainty_score":0.01028425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02619280861070276,"score_gpt":0.2691949583394456,"score_spread":0.2430021497287428,"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."}}