{"id":"W3213057597","doi":"10.1109/jsac.2021.3126074","title":"Efficient Residual Shrinkage CNN Denoiser Design for Intelligent Signal Processing: Modulation Recognition, Detection, and Decoding","year":2021,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Noise reduction; Decoding methods; Artificial intelligence; Signal processing; Speech recognition; Pattern recognition (psychology); Algorithm; Digital signal processing","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":[0.0007097587,0.0007951993,0.0004638032,0.0004250327,0.0002613036,0.0006009209,0.0008620638,0.000718053,0.001169551],"category_scores_gemma":[0.001554078,0.0002913865,0.0004705265,0.0003563683,0.0004851968,0.0008965169,0.0006397814,0.0009656759,0.00053322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004722961,"about_ca_system_score_gemma":0.0006919315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003101048,"about_ca_topic_score_gemma":0.006406959,"domain_scores_codex":[0.9996415,0.00004474354,0.00002547191,0.00008680414,0.0001559844,0.00004545651],"domain_scores_gemma":[0.9996578,0.00006201927,0.00003758155,0.00005500075,0.0001717243,0.00001598141],"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":[0.0004368006,0.0001207082,0.002480644,0.0002459609,0.0001169387,0.000223416,0.0001522756,0.280727,0.2685956,0.01471996,0.004347237,0.4278334],"study_design_scores_gemma":[0.00001538997,0.00007043318,0.0005584824,0.00001552142,0.00003126002,0.000114702,0.00001797322,0.9159051,0.07771193,0.00180981,0.003728519,0.00002097058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01439532,0.0002028007,0.9831082,0.00008935905,0.00005626573,0.00003607582,0.0000369872,0.0004050213,0.001669791],"genre_scores_gemma":[0.4725054,0.0005146117,0.5186284,0.0002408786,0.00007242817,0.0001306419,0.0003534346,0.0001492828,0.007404895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003101048,"threshold_uncertainty_score":0.006165981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09451946008476451,"score_gpt":0.3089147766821053,"score_spread":0.2143953165973408,"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."}}