{"id":"W4235254860","doi":"10.22215/etd/2017-12154","title":"Low Latency Compressive Sensing using Multi-Resolution Analysis In Radar Signal Processing","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Nyquist rate; Nyquist–Shannon sampling theorem; Compressed sensing; Computer science; Bandwidth (computing); Wavelet; Signal processing; SIGNAL (programming language); Algorithm; Electronic engineering; Real-time computing; Radar; Sampling (signal processing); Telecommunications; Computer vision; Engineering","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":[0.0003991208,0.0005519502,0.0003560538,0.0005113725,0.0003120444,0.0009648228,0.000361536,0.0007570317,0.003454315],"category_scores_gemma":[0.001097852,0.0002873082,0.000314141,0.001368755,0.0009606874,0.001243649,0.0006493367,0.001563747,0.001455572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004922978,"about_ca_system_score_gemma":0.0004188242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004547102,"about_ca_topic_score_gemma":0.0007363682,"domain_scores_codex":[0.9995793,0.00007582598,0.00001561833,0.00009096273,0.0002130925,0.00002529866],"domain_scores_gemma":[0.9996924,0.0001556052,0.00002646295,0.00003747513,0.0000735361,0.00001455239],"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":[0.0001883555,0.000186527,0.0006975665,0.00178635,0.00006935387,0.0003195509,0.0004831264,0.04665437,0.1057282,0.2159828,0.01061212,0.6172917],"study_design_scores_gemma":[0.00005840134,0.0007187938,0.00263519,0.001084436,0.00009828416,0.001795601,0.0003788049,0.3474447,0.1318791,0.2410938,0.2726372,0.0001756806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02657548,0.0791058,0.838632,0.003653485,0.0009672004,0.0001272651,0.0001734909,0.0003946716,0.05037065],"genre_scores_gemma":[0.400817,0.1139271,0.4306323,0.001108342,0.001873237,0.0002077489,0.0003787444,0.0001838123,0.05087169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003454315,"threshold_uncertainty_score":0.01155585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02718859029859071,"score_gpt":0.2867454646240853,"score_spread":0.2595568743254946,"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."}}