{"id":"W2553566142","doi":"10.1109/icet.2013.6743515","title":"Non-linear trigonometric and hyperbolic chirps in multiuser spread spectrum communication systems","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Chirp; Hyperbolic function; Chirp spread spectrum; Additive white Gaussian noise; Mathematics; Rayleigh fading; Raised-cosine filter; Trigonometric functions; Bit error rate; Fading; White noise; Spread spectrum; Algorithm; Computer science; Channel (broadcasting); Mathematical analysis; Direct-sequence spread spectrum; Telecommunications; Physics; Bandwidth (computing); Decoding methods; Statistics; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006183009,0.0001341959,0.0002237492,0.0006219909,0.0001271187,0.0003710367,0.002040268,0.00008518653,0.00003149396],"category_scores_gemma":[0.00005275974,0.0001178739,0.00002718347,0.001775428,0.00007937345,0.000863457,0.00110661,0.00034358,0.000316841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007803202,"about_ca_system_score_gemma":0.00004086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003690583,"about_ca_topic_score_gemma":0.0001684475,"domain_scores_codex":[0.9985023,0.0002285644,0.0003611158,0.0003073289,0.0002496327,0.0003510391],"domain_scores_gemma":[0.9973113,0.0003960481,0.00008444546,0.001986891,0.00008583175,0.0001354754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005904249,0.002306059,0.330748,0.000267581,0.0001860796,0.00001545358,0.006341067,0.03108927,0.002590099,0.3215682,0.01127765,0.2935515],"study_design_scores_gemma":[0.0004532431,0.00003089569,0.1154509,0.0000276634,7.52152e-7,0.000006469424,0.00005649231,0.8826294,0.00008155059,0.0002485028,0.0008769713,0.0001371337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145465,0.00363581,0.05866093,0.005623007,0.0001408474,0.001683613,8.700862e-7,0.0002626032,0.01544576],"genre_scores_gemma":[0.9818755,0.001096199,0.01608395,0.00007416546,0.00002618161,0.0001635889,0.000004200956,0.00001256453,0.0006636359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8515401,"threshold_uncertainty_score":0.5579082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02456809955757979,"score_gpt":0.2686997004910406,"score_spread":0.2441316009334608,"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."}}