{"id":"W3120110670","doi":"10.18280/ts.370607","title":"Design and Realization of a Hyperchaotic Memristive System for Communication System on FPGA","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Memristor; Attractor; Field-programmable gate array; Chaotic; Lyapunov exponent; Nonlinear system; Computer science; Electronic circuit; Communications system; Secure communication; Realization (probability); CHAOS (operating system); Topology (electrical circuits); Electronic engineering; Control theory (sociology); Computer hardware; Mathematics; Engineering; Telecommunications; Physics; Encryption; Electrical engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000144919,0.00008879624,0.0001474557,0.00002598531,0.00006326608,0.000008106296,0.00006696318,0.0000261399,0.000001432262],"category_scores_gemma":[0.00001174017,0.00008687585,0.00002145437,0.00006938414,0.00001326818,0.00005221443,0.00001138116,0.00004788164,0.000001075486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005014561,"about_ca_system_score_gemma":0.000004207081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.332136e-7,"about_ca_topic_score_gemma":1.24563e-7,"domain_scores_codex":[0.9994649,0.00005704182,0.0002087744,0.0001023361,0.00007864215,0.00008829103],"domain_scores_gemma":[0.9995997,0.0001852693,0.00006744092,0.00007052732,0.00003501558,0.00004206064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002130427,0.00001470454,0.000009684403,0.002155121,0.00004705104,0.000002063632,0.001714025,0.9035205,0.07773089,0.009111262,0.00008671277,0.005394894],"study_design_scores_gemma":[0.0006898554,0.0003112988,0.00005881592,0.0003903368,0.00004009924,0.000004163237,0.0007553957,0.8743536,0.1231906,0.00003268878,0.00005954593,0.0001135621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0727534,0.0001160508,0.926196,0.00002723231,0.00003162399,0.0005415988,0.000008414024,0.0001870385,0.0001386002],"genre_scores_gemma":[0.9964767,0.000007744891,0.003390556,0.00002187942,0.00004038129,0.00003259095,0.00001284735,0.00001611535,0.00000123412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9237232,"threshold_uncertainty_score":0.3542694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04625716915970857,"score_gpt":0.2347973456922702,"score_spread":0.1885401765325617,"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."}}