{"id":"W1864353221","doi":"10.1109/newcas.2004.1359104","title":"Built-in self-test design of motion estimation computing array","year":2004,"lang":"en","type":"article","venue":"The 2nd Annual IEEE Northeast Workshop on Circuits and Systems, 2004. NEWCAS 2004.","topic":"VLSI and Analog Circuit Testing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Harbin Institute of Technology","keywords":"Verilog; VHDL; Computer science; Field-programmable gate array; Encoder; Hardware description language; Motion estimation; Reliability (semiconductor); Embedded system; Computer architecture; Coding (social sciences); Built-in self-test; Logic simulation; Logic synthesis; Computer hardware; Computer engineering; Logic gate; Algorithm","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":[0.0002235167,0.0003088794,0.0002326232,0.0004383309,0.0002413713,0.0004599548,0.001041879,0.000301929,0.002280053],"category_scores_gemma":[0.0005604658,0.000155177,0.0002057711,0.0001491075,0.0002499934,0.0004053263,0.0002200641,0.000250583,0.0003661823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004563494,"about_ca_system_score_gemma":0.0004519025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007489723,"about_ca_topic_score_gemma":0.0008350353,"domain_scores_codex":[0.9996508,0.00008358378,0.0000222462,0.00005955988,0.0001204283,0.00006323794],"domain_scores_gemma":[0.9995085,0.0001138348,0.00008973541,0.00006613987,0.0001872685,0.00003451936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001209881,0.0004033781,0.009639747,0.0005846899,0.0001894899,0.001322392,0.0005463767,0.09600729,0.5855044,0.03056377,0.005749457,0.2682792],"study_design_scores_gemma":[0.000188575,0.002050117,0.00307596,0.00004305996,0.000124392,0.001343188,0.00007352573,0.4426884,0.5322463,0.003243388,0.01487574,0.00004733932],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2100636,0.0004691537,0.7751044,0.0003062534,0.0001345392,0.0002800809,0.0001771649,0.00366264,0.00980218],"genre_scores_gemma":[0.9064373,0.00009218747,0.09008083,0.0001787549,0.00003424904,0.0001166373,0.0001689985,0.00009854119,0.002792444],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002280053,"threshold_uncertainty_score":0.007627547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03697777084458286,"score_gpt":0.2645118536684934,"score_spread":0.2275340828239105,"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."}}