{"id":"W1859137248","doi":"10.1109/iwsoc.2004.61","title":"SOC design of an IF subsampling terminal for a gigabit wireless LAN with asymmetric equalization","year":2004,"lang":"en","type":"article","venue":"IEEE International Workshop on System-on-Chip for Real-Time Applications","topic":"Advancements in PLL and VCO Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Gigabit; Computer science; Terminal (telecommunication); Equalization (audio); Bandwidth (computing); Wireless lan; Wireless; QAM; Quadrature amplitude modulation; Computer hardware; Base station; Electronic engineering; Computer network; Engineering; Bit error rate; Telecommunications; 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":[0.0002035706,0.0005244386,0.0004156671,0.0003288535,0.000354546,0.0007853904,0.0009528266,0.0004219301,0.003610669],"category_scores_gemma":[0.0004467063,0.0002246634,0.0003173417,0.000168291,0.0001598695,0.0004558494,0.0002355689,0.0004080048,0.001270686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004993867,"about_ca_system_score_gemma":0.0009849848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001174374,"about_ca_topic_score_gemma":0.001771362,"domain_scores_codex":[0.9997242,0.00003280819,0.00002020528,0.00004946709,0.0001118214,0.00006140905],"domain_scores_gemma":[0.9996765,0.00004573221,0.00004746391,0.00003976111,0.0001553906,0.00003524003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007359445,0.0002301969,0.003088288,0.0007593048,0.0003180519,0.000982783,0.0002446119,0.04024183,0.7405434,0.01237805,0.007007603,0.1934699],"study_design_scores_gemma":[0.0002930487,0.002716101,0.004978932,0.0001146124,0.0004207211,0.002802776,0.00009284738,0.4770331,0.4618089,0.002215661,0.04741519,0.0001081667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1852347,0.0008005693,0.7887266,0.0003942109,0.0004269283,0.0005861555,0.0004274131,0.006293093,0.01711032],"genre_scores_gemma":[0.8189262,0.0002696844,0.171856,0.0003378358,0.0001328201,0.0002286284,0.0004475286,0.0001512921,0.007649944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003610669,"threshold_uncertainty_score":0.01207888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03550726622755309,"score_gpt":0.3087415705997912,"score_spread":0.2732343043722382,"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."}}