{"id":"W793298464","doi":"10.1109/glocom.2014.7417132","title":"Ultra Low-Complexity Detection of Spectrum Holes in Compressed Wideband Spectrum Sensing","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Undersampling; Compressed sensing; Wideband; Nyquist rate; Cognitive radio; Computer science; Block (permutation group theory); Electronic engineering; Nyquist–Shannon sampling theorem; Signal-to-noise ratio (imaging); Spectrum (functional analysis); Algorithm; Sampling (signal processing); Telecommunications; Mathematics; Wireless; Engineering; Physics; Detector","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002709755,0.0002994118,0.0004701202,0.0001512874,0.0001435361,0.00008654688,0.0009569384,0.000161201,0.00001682039],"category_scores_gemma":[0.00005260808,0.0003390414,0.00009634226,0.000470714,0.0004637828,0.0002072355,0.0001313344,0.0003929918,0.00002949838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001966655,"about_ca_system_score_gemma":0.00004616897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425737,"about_ca_topic_score_gemma":0.007415309,"domain_scores_codex":[0.9982917,0.0002561761,0.000565621,0.0002703254,0.0002190596,0.0003971518],"domain_scores_gemma":[0.9975966,0.0001456368,0.0001621441,0.001861521,0.0001331583,0.0001009207],"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":[0.0002296691,0.001024646,0.01250134,0.0004000768,0.000335739,0.00001573663,0.001142428,0.0313663,0.7813586,0.07113939,0.004471406,0.09601465],"study_design_scores_gemma":[0.00105034,0.000115871,0.02089092,0.0007808107,0.00005911919,0.00004864209,0.0001900073,0.4725927,0.4220846,0.07894287,0.002337612,0.0009065073],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6077149,0.0007657054,0.353233,0.0009515625,0.0005694901,0.0005801312,0.00007798678,0.001157389,0.03494987],"genre_scores_gemma":[0.9956004,0.000421021,0.003826514,0.00004545147,0.00004386387,0.000007067156,0.00002429014,0.00002481571,0.000006548505],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4412264,"threshold_uncertainty_score":0.9999062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04200556769959409,"score_gpt":0.2749384162343494,"score_spread":0.2329328485347553,"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."}}