{"id":"W2059947141","doi":"10.1109/ccece.2007.302","title":"A High-Resolution, Multi-Template Deconvolution Algorithm for Time-Domain UWB Channel Characterization","year":2007,"lang":"en","type":"article","venue":"","topic":"Ultra-Wideband Communications Technology","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deconvolution; Characterization (materials science); Computer science; Algorithm; Bandwidth (computing); A priori and a posteriori; Channel (broadcasting); Time domain; Wideband; Distortion (music); Resolution (logic); High resolution; Electronic engineering; Telecommunications; Engineering; Artificial intelligence; Optics; Physics; Amplifier; Remote sensing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001006445,0.0007913826,0.0007655194,0.0008614832,0.0005132921,0.0009067403,0.001218018,0.001601337,0.002061764],"category_scores_gemma":[0.00348561,0.0004646741,0.0008840186,0.0009328604,0.0006594242,0.001786492,0.001000313,0.001760527,0.001800325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004112425,"about_ca_system_score_gemma":0.000969189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005303513,"about_ca_topic_score_gemma":0.0008620527,"domain_scores_codex":[0.9992886,0.0001594717,0.00004617083,0.0001289852,0.0003359232,0.00004078383],"domain_scores_gemma":[0.9987638,0.000374373,0.0001368092,0.0002977112,0.0003714185,0.00005583418],"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":[0.0003467751,0.0001722959,0.0007372998,0.0002436707,0.0001078155,0.0002341402,0.0001426567,0.08808147,0.2565119,0.03390846,0.003066483,0.6164471],"study_design_scores_gemma":[0.00002560163,0.0001141457,0.0006157939,0.00001598527,0.00003257038,0.0006768397,0.000019067,0.8778605,0.1074084,0.007021939,0.006135085,0.00007402342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009128196,0.00004228816,0.998655,0.00002138266,0.00001282754,0.00001130433,0.00001333259,0.0001634343,0.0001675658],"genre_scores_gemma":[0.02054695,0.0001071573,0.9781622,0.00004190469,0.00002105592,0.00004663442,0.00006757397,0.00006334454,0.0009429999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002061764,"threshold_uncertainty_score":0.006897271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005989961296374,"score_gpt":0.2224441379293681,"score_spread":0.2123842383164044,"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."}}