{"id":"W2014752278","doi":"10.1016/j.bspc.2015.02.016","title":"Neural signal compression using a minimum Euclidean or Manhattan distance cluster-based deterministic compressed sensing matrix","year":2015,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Compressed sensing; Restricted isometry property; Computer science; Matrix (chemical analysis); Algorithm; Euclidean distance; SIGNAL (programming language); Signal reconstruction; Signal compression; Signal processing; Artificial intelligence; Telecommunications","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":[0.0005186996,0.0004419606,0.0004713282,0.0004925221,0.0003814055,0.0006100176,0.0008577225,0.0007212624,0.001225318],"category_scores_gemma":[0.002596695,0.0001892316,0.0003639001,0.0007936368,0.0007465087,0.0009429659,0.0007277324,0.0006924777,0.0002940017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006198286,"about_ca_system_score_gemma":0.000918023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003019587,"about_ca_topic_score_gemma":0.003836028,"domain_scores_codex":[0.9994488,0.0001287466,0.00002713012,0.0001364967,0.0002281534,0.00003053808],"domain_scores_gemma":[0.9993182,0.0002437997,0.00008093511,0.0001339556,0.0001907313,0.0000324488],"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":[0.0004816008,0.0001166758,0.0008670893,0.000183649,0.00007948872,0.0001432414,0.0001412502,0.6337401,0.04491632,0.1076899,0.003598252,0.2080424],"study_design_scores_gemma":[0.000006735765,0.00004706429,0.0002063228,0.000005779543,0.000006058203,0.00005866402,0.000009698798,0.9891266,0.005135885,0.004720438,0.0006639924,0.00001277765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01494821,0.00012305,0.9830888,0.0002560831,0.00004894979,0.00003056882,0.00008400338,0.0001297843,0.001290576],"genre_scores_gemma":[0.450266,0.0003477409,0.5450057,0.0001837228,0.0001255,0.00009869021,0.0002957939,0.00006253797,0.003614315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003019587,"threshold_uncertainty_score":0.006004035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03064223832030545,"score_gpt":0.2798429667781794,"score_spread":0.249200728457874,"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."}}