{"id":"W3211019912","doi":"10.3390/s21217153","title":"Suppression of Continuous Wave Interference in Loran-C Signal Based on Sparse Optimization Using Tunable Q-Factor Wavelet Transform and Discrete Cosine Transform","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Radio Wave Propagation Studies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"GNSS applications; Computer science; Interference (communication); Wavelet transform; Discrete cosine transform; Filter (signal processing); SIGNAL (programming language); Sparse approximation; Algorithm; Electronic engineering; Wavelet; Telecommunications; Artificial intelligence; Engineering; Computer vision; Global Positioning System","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007961279,0.0001791777,0.0003161147,0.000131642,0.00003790058,0.00002238409,0.00003734879,0.00007505962,0.0000980987],"category_scores_gemma":[0.0000243128,0.0001690042,0.00004516287,0.0002181,0.00005507099,0.00012038,0.000006918624,0.0001452632,8.118068e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007349984,"about_ca_system_score_gemma":0.00002195066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001826882,"about_ca_topic_score_gemma":0.00002688975,"domain_scores_codex":[0.9990697,0.00003990814,0.0003296268,0.0001953032,0.0001563879,0.0002090635],"domain_scores_gemma":[0.9996379,0.00008119047,0.00003657609,0.0001192254,0.00007490172,0.00005022398],"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":[0.0001129465,0.00003254399,0.0002505329,0.000203496,0.00002601846,0.00003131659,0.001150203,0.9546269,0.03892576,0.00001018808,0.00001089849,0.004619223],"study_design_scores_gemma":[0.0007346185,0.00005050651,0.0003374195,0.0002775556,0.000013363,0.000006088134,0.0002605201,0.7298238,0.2683185,0.00001911525,0.00002071291,0.0001377522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8888707,0.0001587704,0.108741,0.0001642101,0.000103886,0.0003105816,0.00008853499,0.00006431471,0.001498004],"genre_scores_gemma":[0.9945849,0.0001035923,0.005176798,0.00001448817,0.00001607375,0.000004695647,0.00002619731,0.00002989123,0.00004332654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2293928,"threshold_uncertainty_score":0.6891792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02166426773101171,"score_gpt":0.2295807552726261,"score_spread":0.2079164875416143,"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."}}