{"id":"W1985308950","doi":"10.1109/icip.2013.6738484","title":"Sensitivity analysis of compressed sensing ISAR imaging to rotational acceleration rate mismatch","year":2013,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Inverse synthetic aperture radar; Sensitivity (control systems); Acceleration; Synthetic aperture radar; Compressed sensing; Iterative reconstruction; Computer science; Radar imaging; Optics; Artificial intelligence; Radar; Physics; Electronic engineering; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002026592,0.00061968,0.0005477677,0.000604042,0.0002500282,0.0005139339,0.0003223741,0.0009958764,0.0008680844],"category_scores_gemma":[0.01844686,0.0002495366,0.0003430435,0.0003933884,0.0009404111,0.0008700457,0.0005966725,0.0004623543,0.0001496944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006883633,"about_ca_system_score_gemma":0.0002168765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00106026,"about_ca_topic_score_gemma":0.000503012,"domain_scores_codex":[0.9984146,0.000540616,0.00006165889,0.000198844,0.0006613667,0.0001228701],"domain_scores_gemma":[0.9846064,0.01324987,0.0007465411,0.0006316199,0.0006826867,0.00008298081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007323925,0.00006027847,0.005409757,0.0002768127,0.0001320394,0.0005838947,0.0001629277,0.8606837,0.09299409,0.008306354,0.0004382329,0.03021955],"study_design_scores_gemma":[0.00001050473,0.0002000211,0.004617223,0.00002741252,0.00003851765,0.0004639157,0.00005340322,0.9416621,0.04908931,0.003267432,0.0005227887,0.00004734414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5128396,0.001967816,0.4772337,0.0009377575,0.00008264153,0.0001053179,0.0002625349,0.0006521132,0.005918507],"genre_scores_gemma":[0.9851624,0.0003001638,0.01378608,0.0001124921,0.00002218183,0.0000269928,0.00009199446,0.0000427114,0.0004549153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002026592,"threshold_uncertainty_score":0.01071775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01349793250026366,"score_gpt":0.2345852058955395,"score_spread":0.2210872733952758,"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."}}