{"id":"W2145328149","doi":"10.1109/icact.2006.206080","title":"Subspace Separator and Functional Link Neural Network Based Receiver for DS-CDMA Signal","year":2006,"lang":"en","type":"article","venue":"2006 8th International Conference Advanced Communication Technology","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Code division multiple access; Artificial neural network; Subspace topology; Spread spectrum; Electronic engineering; Binary number; Additive white Gaussian noise; Multiuser detection; Interference (communication); Channel (broadcasting); Telecommunications; Engineering; Artificial intelligence; Mathematics","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":[0.0004005246,0.0003293781,0.0002790502,0.0002113753,0.000249114,0.0003065276,0.0004656756,0.0006602001,0.001691546],"category_scores_gemma":[0.0006553596,0.0001447913,0.0002208903,0.0002248068,0.0002542478,0.0006094013,0.0002803481,0.0005530727,0.0005098206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003572556,"about_ca_system_score_gemma":0.0003044603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001159371,"about_ca_topic_score_gemma":0.002135262,"domain_scores_codex":[0.9997961,0.00006872912,0.000007918541,0.00003431068,0.00007369727,0.0000194007],"domain_scores_gemma":[0.9998115,0.00006487878,0.00001753882,0.00001452994,0.00008323382,0.00000847536],"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":[0.0005509648,0.0001514804,0.0009474406,0.000244105,0.0001161208,0.0001711492,0.0001841507,0.312246,0.09805139,0.02434831,0.002910568,0.5600784],"study_design_scores_gemma":[0.00001066644,0.000114545,0.0002837556,0.000007691437,0.00001435694,0.00008039088,0.000008057044,0.9797722,0.01634539,0.001847562,0.001499308,0.00001615433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02756571,0.0006146716,0.9680427,0.0001661438,0.00005447078,0.00002162588,0.00004096625,0.0004671446,0.003026461],"genre_scores_gemma":[0.5775451,0.000708344,0.4040904,0.0002158065,0.0001121798,0.0001027748,0.0001690038,0.00006098497,0.01699542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001691546,"threshold_uncertainty_score":0.005658805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02851363521962652,"score_gpt":0.2978466857158223,"score_spread":0.2693330504961958,"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."}}