{"id":"W2043069670","doi":"10.1145/2512840.2512869","title":"Performances of trigonometric chirp spread spectrum modulation in AWGN &amp; rayleigh channels","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Additive white Gaussian noise; Chirp spread spectrum; Chirp; Modulation (music); Frequency modulation; Spread spectrum; Spectrum (functional analysis); Electronic engineering; Physics; Mathematics; Computer science; Telecommunications; Direct-sequence spread spectrum; Acoustics; Bandwidth (computing); White noise; Optics; Engineering; Channel (broadcasting)","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":[0.0007727746,0.0007779729,0.0006256794,0.0006280327,0.0004617597,0.0005786358,0.0003677925,0.0009848755,0.001333985],"category_scores_gemma":[0.0033876,0.0001939875,0.0002113172,0.0007202201,0.0008310165,0.000599635,0.0005099985,0.0005200015,0.0004342191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004930464,"about_ca_system_score_gemma":0.0004675314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002557565,"about_ca_topic_score_gemma":0.001389868,"domain_scores_codex":[0.9989491,0.0002936553,0.00004202947,0.0001064618,0.0003754461,0.0002332546],"domain_scores_gemma":[0.9961663,0.002430638,0.0004308207,0.0001871245,0.0006728907,0.0001122366],"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":[0.005802871,0.0002653455,0.01738863,0.0007674366,0.000284132,0.001743757,0.00101758,0.5224416,0.3497366,0.009956434,0.001595526,0.08900004],"study_design_scores_gemma":[0.0001082319,0.001507643,0.01121085,0.0001324605,0.0001561034,0.0009230952,0.0003015685,0.8253228,0.1565818,0.002603858,0.001025592,0.0001260195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622377,0.001010978,0.02658062,0.0002547858,0.00007117112,0.00001921608,0.0001562312,0.0004182895,0.009251012],"genre_scores_gemma":[0.9977319,0.0002437619,0.001482053,0.00003514232,0.00001356645,0.000006816249,0.00003922091,0.00001129866,0.0004362705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002557565,"threshold_uncertainty_score":0.005085349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03572364634372791,"score_gpt":0.2758845850497936,"score_spread":0.2401609387060657,"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."}}