{"id":"W2150231611","doi":"10.1109/icc.2006.255023","title":"Code Hopping - Direct Sequence Spread Spectrum to Compensate for Intersymbol Interference in an Ultra-wideband System","year":2006,"lang":"en","type":"article","venue":"2006 IEEE International Conference on Communications","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Direct-sequence spread spectrum; Intersymbol interference; Spread spectrum; Algorithm; Phase-shift keying; Computer science; Time-hopping; Bit error rate; Rake receiver; Frequency-hopping spread spectrum; Keying; Interference (communication); Rake; Electronic engineering; Modulation (music); Telecommunications; Channel (broadcasting); Decoding methods; Fading; Pulse-amplitude modulation; Physics; Engineering","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":[0.0002729214,0.0003579638,0.0003236376,0.0003245587,0.000185306,0.0002669763,0.0003735753,0.0004179733,0.0005996736],"category_scores_gemma":[0.0007575828,0.0001139028,0.0001673086,0.0003523927,0.0002193971,0.0003123245,0.0002371224,0.0002941209,0.0002247684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002196103,"about_ca_system_score_gemma":0.0003051429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007828115,"about_ca_topic_score_gemma":0.001212102,"domain_scores_codex":[0.9997821,0.00004188518,0.000008238641,0.00002373961,0.000124906,0.00001908283],"domain_scores_gemma":[0.9998061,0.00005522606,0.00002977214,0.00003285522,0.00006288635,0.00001312952],"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":[0.0003411761,0.0001097475,0.004452121,0.0001994045,0.0001135418,0.0006415521,0.000152492,0.5392866,0.2167584,0.0385589,0.001090107,0.198296],"study_design_scores_gemma":[0.00003951265,0.0003341461,0.001401517,0.00001928817,0.00004415903,0.0006049951,0.00001893267,0.9547434,0.03522402,0.004695176,0.002850481,0.00002442988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1171747,0.0005956442,0.8785697,0.0001004316,0.00007959758,0.00004342044,0.00001929037,0.0006293892,0.002787839],"genre_scores_gemma":[0.7527268,0.0003238767,0.2446999,0.00008545485,0.00003751562,0.00004505972,0.00002892316,0.00001510161,0.002037386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007828115,"threshold_uncertainty_score":0.002006114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08110852820307053,"score_gpt":0.3235285154707956,"score_spread":0.2424199872677251,"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."}}