{"id":"W2034874210","doi":"10.1109/isspa.2003.1224879","title":"Time-frequency filtering of interferences in spread spectrum communications","year":2003,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Chirp; Chirp spread spectrum; Interference (communication); Direct-sequence spread spectrum; Algorithm; Spread spectrum; SIGNAL (programming language); Time–frequency analysis; Computer science; Signal-to-noise ratio (imaging); Noise (video); Telecommunications; Physics; Artificial intelligence; Optics; Image (mathematics); Radar; Code division multiple access; Channel (broadcasting)","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":[0.0002929424,0.0004949357,0.0003486565,0.0005086935,0.0002787811,0.0004429889,0.0003985689,0.0008637226,0.0009016627],"category_scores_gemma":[0.0007957022,0.0002231519,0.0003580338,0.0005584634,0.0003371035,0.0006732999,0.0003268964,0.0004075185,0.0005809203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002298569,"about_ca_system_score_gemma":0.0002338351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004026543,"about_ca_topic_score_gemma":0.0005960313,"domain_scores_codex":[0.9997208,0.00005207238,0.00001147304,0.00002813566,0.0001667962,0.00002073134],"domain_scores_gemma":[0.9997013,0.000117343,0.0000434053,0.00003592618,0.00008706785,0.00001484473],"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":[0.0004315008,0.00007921718,0.001200486,0.000261655,0.00006294272,0.0006500208,0.0001804856,0.04049203,0.4408965,0.02216676,0.001153278,0.4924251],"study_design_scores_gemma":[0.00005204092,0.0006871305,0.002475085,0.00004966106,0.0001024412,0.0036607,0.00007541376,0.6068845,0.3502528,0.01348247,0.02221745,0.00006028091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03510392,0.00179485,0.9602236,0.0001131351,0.00009519715,0.00002004183,0.00001541175,0.0003816215,0.002252222],"genre_scores_gemma":[0.3776499,0.003444002,0.6117808,0.0001549148,0.0003181946,0.00005998637,0.0001003328,0.00008215687,0.006409698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009016627,"threshold_uncertainty_score":0.003016412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488423869270322,"score_gpt":0.2610809197307342,"score_spread":0.2361966810380309,"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."}}