{"id":"W2955240866","doi":"10.1049/iet-map.2019.0238","title":"Towards real‐time through‐obstacle imaging based on compressed sensing for sparse objects","year":2019,"lang":"en","type":"article","venue":"IET Microwaves Antennas & Propagation","topic":"Microwave Imaging and Scattering Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Natural Science Foundation of China","keywords":"Compressed sensing; Obstacle; Computer vision; Computer science; Artificial intelligence; Real-time computing; Geography","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":[0.0003679266,0.0004604654,0.0003678412,0.0002910524,0.0001309228,0.0005819832,0.0004704254,0.0006215124,0.001206264],"category_scores_gemma":[0.0007791849,0.000219128,0.0002933211,0.00035647,0.0005555527,0.001015352,0.0007074559,0.0006659606,0.0002763918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002566903,"about_ca_system_score_gemma":0.0003871625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005024981,"about_ca_topic_score_gemma":0.0006604632,"domain_scores_codex":[0.999757,0.00004904668,0.000008853212,0.00003217864,0.0001329475,0.00001996594],"domain_scores_gemma":[0.9995319,0.0002376328,0.00007600505,0.00005502022,0.00008043359,0.00001904237],"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":[0.0002616537,0.0001062489,0.0007202654,0.0003995672,0.00004200247,0.0003411468,0.000426667,0.08526523,0.750753,0.0355274,0.002043555,0.1241133],"study_design_scores_gemma":[0.00002027407,0.00009116796,0.0003025869,0.00002046104,0.000009862434,0.0002780849,0.00004646862,0.8762724,0.114948,0.004512614,0.00346655,0.00003165788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.052905,0.0002430122,0.9426503,0.0002646649,0.00004939649,0.00004810804,0.0000519245,0.0003777633,0.003409808],"genre_scores_gemma":[0.4168403,0.0003961109,0.5799841,0.0001416698,0.00003803591,0.00007928008,0.0001737626,0.00009633384,0.002250444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001206264,"threshold_uncertainty_score":0.004035294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00870703248835158,"score_gpt":0.220968175133726,"score_spread":0.2122611426453744,"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."}}