{"id":"W2803085185","doi":"10.1049/el.2018.1103","title":"Time‐domain approach for LFM signal parameter estimation based on FPGA","year":2018,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Advanced Electrical Measurement Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; University of Electronic Science and Technology of China; University of British Columbia","keywords":"Field-programmable gate array; Time domain; SIGNAL (programming language); Computer science; Estimation; Electronic engineering; Frequency domain; Signal processing; Engineering; Embedded system; Computer hardware; Digital signal processing; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002211346,0.0002036633,0.0001632361,0.000126013,0.00007772778,0.00002628931,0.0001640861,0.00008258539,0.00002250767],"category_scores_gemma":[0.00004281222,0.0002091508,0.00007593795,0.0002131246,0.00004918507,0.00009425887,0.000005222108,0.000225288,0.00002940826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004185634,"about_ca_system_score_gemma":0.00001878194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.871495e-7,"about_ca_topic_score_gemma":3.15874e-7,"domain_scores_codex":[0.9987621,0.00002870861,0.0001792682,0.0002489828,0.0002289133,0.0005520816],"domain_scores_gemma":[0.9995279,0.0001219302,0.00003351634,0.0002300779,0.00003343742,0.00005308096],"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":[0.0001501654,0.0000937505,0.000006013872,0.00006508038,0.00006703483,0.000001075847,0.00004694113,0.2843268,0.6452861,0.0006433082,0.03197636,0.03733737],"study_design_scores_gemma":[0.0003046415,0.000411353,0.000005403795,0.000009760439,0.000012586,9.057395e-7,4.293868e-7,0.780385,0.2137253,0.00161142,0.003312312,0.000220878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009208675,0.00005223439,0.9882313,0.000397713,0.00003427575,0.000568993,0.000003437787,0.000799082,0.0007043036],"genre_scores_gemma":[0.6641273,0.000001897658,0.3333813,0.00192013,0.0001573629,0.0002664963,0.00004978397,0.00007853063,0.00001712482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6549187,"threshold_uncertainty_score":0.8528923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014533895379534,"score_gpt":0.2248442088175538,"score_spread":0.2146988698637584,"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."}}