{"id":"W2136491700","doi":"10.1109/iscas.2006.1693156","title":"Per-Survivor Processing Viterbi Decoder for Bluetooth Applications","year":2006,"lang":"en","type":"article","venue":"","topic":"Bluetooth and Wireless Communication Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Viterbi decoder; Computer science; Viterbi algorithm; Soft-decision decoder; Bluetooth; Field-programmable gate array; Trellis (graph); Computer hardware; Soft output Viterbi algorithm; Software; Bit error rate; Decoding methods; Real-time computing; Algorithm; Wireless; Sequential decoding; Telecommunications","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":[0.0003751193,0.000270113,0.0003057349,0.0003113666,0.0001972309,0.0003502966,0.0004908335,0.0003825039,0.002297547],"category_scores_gemma":[0.0009954652,0.0001620657,0.0001598529,0.0002870541,0.0001704648,0.0003377053,0.000238333,0.0004550896,0.0008056551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003701143,"about_ca_system_score_gemma":0.0008008596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007862505,"about_ca_topic_score_gemma":0.001825386,"domain_scores_codex":[0.9997829,0.00005987593,0.00001228801,0.00002808385,0.0001004902,0.00001638591],"domain_scores_gemma":[0.9996531,0.0001296963,0.00003763851,0.00005518649,0.0001113079,0.00001303661],"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":[0.0006637309,0.0001362075,0.001774604,0.0003725406,0.00007456078,0.0002994869,0.0001668989,0.08482875,0.3575638,0.04283966,0.003608627,0.5076712],"study_design_scores_gemma":[0.0001309678,0.0005440224,0.001688523,0.00003869441,0.000058824,0.0007082585,0.00002216346,0.7506483,0.2191269,0.007799,0.0191975,0.00003693042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05497955,0.0008146334,0.9390742,0.0002058167,0.00004910999,0.0000558884,0.00008517267,0.001045379,0.003690248],"genre_scores_gemma":[0.5209302,0.0007305933,0.4648246,0.000174653,0.00008211262,0.0001136769,0.0002990341,0.00008387584,0.01276121],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002297547,"threshold_uncertainty_score":0.007686079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252121199973857,"score_gpt":0.2497055279642542,"score_spread":0.2371843159645156,"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."}}