{"id":"W1981432997","doi":"10.1109/isccsp.2010.5463480","title":"Fast method to detect specific frequencies in monitored signal","year":2010,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fast Fourier transform; Computer science; Split-radix FFT algorithm; SIGNAL (programming language); Computation; Algorithm; Speedup; Signal processing; Discrete Fourier transform (general); Digital signal processing; Parallel computing; Multidimensional signal processing; Prime-factor FFT algorithm; Fourier transform; Rader's FFT algorithm; Computational complexity theory; Short-time Fourier transform; Computer hardware; Mathematics; Fourier analysis","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":[0.0003049987,0.0005496628,0.0003973072,0.0009043012,0.0003083801,0.0004648779,0.0005787781,0.0006423034,0.005221433],"category_scores_gemma":[0.0008816267,0.0001809123,0.000308989,0.0006314778,0.0002936916,0.0007374061,0.0004675359,0.0005992719,0.00208084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002789305,"about_ca_system_score_gemma":0.0005150815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007070764,"about_ca_topic_score_gemma":0.0009179447,"domain_scores_codex":[0.9997258,0.00003934692,0.00001115425,0.0000569001,0.0001521535,0.00001457074],"domain_scores_gemma":[0.999653,0.0001118312,0.00003396162,0.00006360545,0.0001246817,0.00001291344],"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":[0.0002625332,0.00005855841,0.0006225469,0.000275023,0.00004902673,0.000129026,0.0001329753,0.01535644,0.1885228,0.01808453,0.006567269,0.7699392],"study_design_scores_gemma":[0.0001316234,0.0003446589,0.002351467,0.00008462699,0.00008305643,0.00206299,0.0001011283,0.6720884,0.2481644,0.0146669,0.05981497,0.000105801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004243255,0.0003261299,0.9921524,0.00007186911,0.0000711457,0.00004080134,0.00005591356,0.001166931,0.001871595],"genre_scores_gemma":[0.08335295,0.0006638631,0.9066561,0.0001170433,0.00007718928,0.0001338284,0.0001945972,0.0001033879,0.008701071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005221433,"threshold_uncertainty_score":0.01746744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02136040261008393,"score_gpt":0.2991678125797351,"score_spread":0.2778074099696511,"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."}}