{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006225224,0.0008029822,0.000641727,0.0006389012,0.0002906027,0.0004752675,0.0006662481,0.000811637,0.001813181],"category_scores_gemma":[0.001076141,0.0003192133,0.0004553393,0.0006526909,0.0006726307,0.001209734,0.0008840953,0.001255601,0.0008950117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002288595,"about_ca_system_score_gemma":0.0006929355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006821384,"about_ca_topic_score_gemma":0.000781655,"domain_scores_codex":[0.9996169,0.0001290952,0.0000162987,0.00005786308,0.0001635738,0.00001627336],"domain_scores_gemma":[0.9997851,0.00007835848,0.00001867165,0.00002215694,0.00007841043,0.00001720885],"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":[0.0002088211,0.0001373323,0.0006562588,0.000723569,0.0001026559,0.0002135854,0.0001888262,0.1603379,0.08111901,0.1504025,0.007820521,0.598089],"study_design_scores_gemma":[0.0000247175,0.0001043437,0.0001622246,0.00002931465,0.00001595982,0.0002256566,0.00001952105,0.963487,0.01173669,0.01393232,0.01023318,0.00002913686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001030273,0.0002023889,0.9979765,0.00006095313,0.00003556508,0.0000205724,0.00001087829,0.00008355766,0.0005793317],"genre_scores_gemma":[0.05996148,0.001050754,0.9359119,0.0001359504,0.0001259964,0.0001516685,0.0001130401,0.0001249978,0.002424331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001813181,"threshold_uncertainty_score":0.006065667,"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."}}