{"id":"W2104440067","doi":"10.1109/newcas.2006.250906","title":"Real Time ELA De-Interlacing with the Xtensa Reconfigurable Processor","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"NTSC; Computer science; Interlacing; Software; Frame rate; Interpolation (computer graphics); Frame (networking); Parallel computing; Computer hardware; Enhanced Data Rates for GSM Evolution; Real-time computing; Embedded system; High-definition television; Artificial intelligence; Operating system","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":[0.0003100386,0.0005133095,0.0003291668,0.0005417609,0.0002553217,0.0005874929,0.001076256,0.0002549459,0.004158398],"category_scores_gemma":[0.0006501941,0.0002819317,0.0003001298,0.0003199702,0.0002341017,0.0006435353,0.000321542,0.0005957647,0.001097554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004026903,"about_ca_system_score_gemma":0.0004021706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008617156,"about_ca_topic_score_gemma":0.00160968,"domain_scores_codex":[0.9996805,0.00003735767,0.00002294802,0.00006419198,0.0001480036,0.00004692831],"domain_scores_gemma":[0.9995921,0.0001164422,0.00007232672,0.00008623869,0.000112129,0.00002070181],"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":[0.001305841,0.0001302288,0.001649148,0.0001436183,0.00008119873,0.0002976788,0.0001380228,0.03409673,0.2973113,0.007449088,0.004578393,0.6528188],"study_design_scores_gemma":[0.0001256241,0.0005437049,0.002574035,0.00002918299,0.0001024494,0.0006189,0.00004403833,0.4596911,0.4931453,0.002162337,0.04089564,0.0000676358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05867582,0.0004607312,0.9296228,0.000151943,0.00009551668,0.0000527457,0.00005221588,0.005541124,0.005347089],"genre_scores_gemma":[0.3675457,0.0002395535,0.6188616,0.0001626605,0.00008590697,0.00007656056,0.0001911474,0.0003969325,0.01243996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004158398,"threshold_uncertainty_score":0.01391125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008670330517443486,"score_gpt":0.236036470364888,"score_spread":0.2273661398474446,"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."}}