{"id":"W2105226795","doi":"10.1109/icassp.2013.6637798","title":"Reconstruction of ECG signals for compressive sensing by promoting sparsity on the gradient","year":2013,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Compressed sensing; Conjugate gradient method; Algorithm; Computation; Reduction (mathematics); Computer science; Signal reconstruction; Regularization (linguistics); Block (permutation group theory); SIGNAL (programming language); Least-squares function approximation; Bayesian probability; Pattern recognition (psychology); Signal processing; Artificial intelligence; Mathematics; Statistics; Telecommunications","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":[0.0004856632,0.0005668778,0.0004936772,0.0003011901,0.0001999753,0.0005217795,0.000504402,0.0006708928,0.001287257],"category_scores_gemma":[0.00141439,0.0002402071,0.0003423857,0.0004328068,0.0003473627,0.0007346538,0.0007087397,0.0009019502,0.0005179205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001864635,"about_ca_system_score_gemma":0.0004463819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005981429,"about_ca_topic_score_gemma":0.001098913,"domain_scores_codex":[0.9997538,0.00007014042,0.00001339702,0.00003090448,0.0001175673,0.00001428906],"domain_scores_gemma":[0.9997402,0.0001331535,0.00002865852,0.00003127932,0.00005309073,0.00001370444],"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":[0.0002820598,0.0001139014,0.0009596477,0.0002939322,0.00007735465,0.0002243734,0.0001603645,0.2636679,0.1064398,0.04930668,0.004936079,0.5735379],"study_design_scores_gemma":[0.00001522797,0.00007566018,0.0001431165,0.00001089996,0.00000809348,0.000159477,0.000009630833,0.9867074,0.007154013,0.003196293,0.002510387,0.000009733232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002246906,0.0001018654,0.9970919,0.00007169443,0.00002180628,0.00001539192,0.00001048826,0.00008568966,0.0003541916],"genre_scores_gemma":[0.07291589,0.0004280961,0.9250251,0.00008780591,0.0000682586,0.00008023745,0.00008270294,0.00004412069,0.001267803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001287257,"threshold_uncertainty_score":0.004306257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01917047420036618,"score_gpt":0.2070381057243167,"score_spread":0.1878676315239506,"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."}}