{"id":"W3021704413","doi":"10.5539/mas.v14n5p86","title":"A Spectral Gradient Projection Method for Sparse Signal Reconstruction in Compressive Sensing","year":2020,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Rajamangala University of Technology Phra Nakhon","keywords":"Conjugate gradient method; Jacobian matrix and determinant; Compressed sensing; Proximal Gradient Methods; Gradient method; Algorithm; SIGNAL (programming language); Matrix (chemical analysis); Computer science; Projection (relational algebra); Mathematical optimization; Nonlinear conjugate gradient method; Signal reconstruction; Mathematics; Convex optimization; Sparse matrix; Regular polygon; Signal processing; Gradient descent; Applied mathematics; Physics; Artificial intelligence; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002006474,0.0001298019,0.0001631136,0.0001421474,0.0001115181,0.00006548942,0.0001408974,0.00004458782,0.000002293321],"category_scores_gemma":[0.0000126597,0.0001359963,0.00003331045,0.0004303599,0.00009987543,0.0001348923,0.00003311095,0.0001533262,0.000002310034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116446,"about_ca_system_score_gemma":0.00003536531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001594963,"about_ca_topic_score_gemma":0.00001280462,"domain_scores_codex":[0.9989765,0.00001182994,0.000181685,0.0003565577,0.0001713761,0.0003020035],"domain_scores_gemma":[0.9996892,0.00003176726,0.00004040555,0.0001194908,0.00004113729,0.00007799259],"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":[0.00002673108,0.000005649109,0.00001084708,0.000009991079,0.000003295951,0.000002223435,0.00110862,0.04505951,0.867029,0.0004491279,0.00005257034,0.08624242],"study_design_scores_gemma":[0.0001413486,0.00001998634,0.00005805821,0.0000170884,0.000003832342,0.0000137185,0.00006339746,0.6672237,0.3265769,0.005725581,0.00004763722,0.0001088002],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1148624,0.00001597312,0.8820477,0.00006250159,0.0001133168,0.0004479963,0.000002122836,0.0003998213,0.002048171],"genre_scores_gemma":[0.8088107,0.000002483362,0.1910125,0.00006917107,0.00007233949,0.00001637495,0.000001044869,0.00001400251,0.000001389076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6939483,"threshold_uncertainty_score":0.5545768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03489638008606157,"score_gpt":0.2619194896402945,"score_spread":0.2270231095542329,"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."}}