{"id":"W2510046743","doi":"10.1109/iscas.2016.7539134","title":"Analog cellular neural network for application in physical unclonable functions","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Physical unclonable function; Computer science; Hardware security module; Artificial neural network; Hamming distance; Authentication (law); CMOS; Electronic engineering; Key (lock); Cryptography; Algorithm; Engineering; Artificial intelligence","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":[0.0001012005,0.0001601486,0.0001290971,0.0001280886,0.000111473,0.0003162318,0.0003442342,0.0003989863,0.001841293],"category_scores_gemma":[0.0003188383,0.00006167353,0.0001244581,0.00018668,0.0002096675,0.000343088,0.00016372,0.0002898821,0.0002393311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002911818,"about_ca_system_score_gemma":0.0001955551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008745548,"about_ca_topic_score_gemma":0.0009512591,"domain_scores_codex":[0.999948,0.00001019382,0.000002749003,0.00001224586,0.00001980264,0.000007041757],"domain_scores_gemma":[0.9999372,0.00002091485,0.000006510683,0.000008764993,0.00002317886,0.00000343428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000265675,0.00009046729,0.001806949,0.0004614745,0.00008661948,0.0005372248,0.0001135051,0.4534113,0.1458278,0.1167203,0.004100855,0.2765777],"study_design_scores_gemma":[0.00000886,0.00007325739,0.000390852,0.00001769167,0.00001828655,0.000121824,0.00001616512,0.9580919,0.02270447,0.008544723,0.01000193,0.00001009484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06971637,0.003131202,0.9052318,0.000699795,0.0003128946,0.00004368923,0.0001136081,0.0008236923,0.01992699],"genre_scores_gemma":[0.9128132,0.001286914,0.07811095,0.0001874826,0.00003489517,0.0000527012,0.00007546686,0.00002354344,0.007414818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001841293,"threshold_uncertainty_score":0.006159782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175882259239259,"score_gpt":0.2236487829642554,"score_spread":0.2118899603718629,"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."}}