{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001954454,0.000196486,0.0002589214,0.00007541946,0.0000622353,0.00009293866,0.0007140994,0.0002321009,0.0002855413],"category_scores_gemma":[0.00005195885,0.0002071409,0.0001581209,0.00007720895,0.00002244884,0.00005754664,0.0007223467,0.0004726435,0.00001568867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000646249,"about_ca_system_score_gemma":0.0000487332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002618733,"about_ca_topic_score_gemma":0.00009096468,"domain_scores_codex":[0.9992343,0.00003227478,0.0003249877,0.0001679975,0.00007695789,0.0001635213],"domain_scores_gemma":[0.9973055,0.0001297541,0.00004548974,0.002288517,0.0001766352,0.00005410019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009708479,0.001929202,0.0002691536,0.004717289,0.003351195,0.00001137218,0.008755107,0.2207776,0.01481056,0.06278455,0.5624415,0.1200554],"study_design_scores_gemma":[0.0009811368,0.00006055638,0.0007861953,0.0006139438,0.0001491861,0.000007336963,0.000496759,0.5014229,0.009773821,0.004737703,0.4795324,0.001438047],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1030627,0.04121036,0.6226638,0.005286855,0.002546999,0.002511201,0.000472291,0.002946707,0.2192991],"genre_scores_gemma":[0.8754783,0.003277336,0.117862,0.0001326028,0.00005332123,0.0002923015,0.001589321,0.00007068455,0.001244216],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7724155,"threshold_uncertainty_score":0.844696,"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."}}