{"id":"W2118083070","doi":"10.1109/tcsi.2004.838266","title":"FPGA design and implementation of a low-power systolic array-based adaptive Viterbi decoder","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Viterbi decoder; Viterbi algorithm; Soft output Viterbi algorithm; Computer science; Field-programmable gate array; Iterative Viterbi decoding; Parallel computing; Gate array; Algorithm; Soft-decision decoder; Sequential decoding; Convolutional code; Decoding methods; Computer hardware","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":[0.0006072992,0.000174716,0.0002068447,0.0001728741,0.0003443314,0.0001135296,0.0001569017,0.00006784455,0.000009322927],"category_scores_gemma":[0.000001257163,0.0001684862,0.0000415256,0.0002070806,0.0001207583,0.0002844195,0.000003427985,0.0001195853,0.000003331508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005709669,"about_ca_system_score_gemma":0.00004080312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003241306,"about_ca_topic_score_gemma":0.00001436928,"domain_scores_codex":[0.9987299,0.000225015,0.0003295806,0.0003925828,0.0001500296,0.0001729344],"domain_scores_gemma":[0.9990163,0.0003802833,0.0001512226,0.0002946449,0.00005648907,0.0001010331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001257741,0.0009610986,0.0001651468,0.0004877297,0.0002656648,0.000002530884,0.01046149,0.005789893,0.1191603,0.2329286,0.00008248659,0.6295692],"study_design_scores_gemma":[0.006426534,0.003633242,0.001844597,0.001496517,0.0004313797,0.0007206287,0.01767041,0.1113476,0.8240735,0.0253863,0.003970183,0.002999112],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03026649,0.0002664076,0.9678978,0.000067209,0.00009023069,0.001086367,0.00003638015,0.0001440628,0.0001450689],"genre_scores_gemma":[0.9946948,0.00003096804,0.004409551,0.0001248628,0.00001845847,0.000657897,0.000001318665,0.0000142133,0.00004790808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9644283,"threshold_uncertainty_score":0.6870667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02115444432978213,"score_gpt":0.2785693925274865,"score_spread":0.2574149481977044,"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."}}