{"id":"W2113844580","doi":"10.1109/ccece.2007.315","title":"FPGA-Based Lossless Data Compression using Huffman and LZ77 Algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Huffman coding; Encoder; Lossless compression; Field-programmable gate array; Data compression; VHDL; Computer hardware; Application-specific integrated circuit; Embedded system; Algorithm; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007480895,0.0001879302,0.0001916758,0.000130372,0.0003076376,0.0002766566,0.001687166,0.00008718284,0.00003583925],"category_scores_gemma":[0.000019155,0.0001466936,0.00002290929,0.0002836001,0.00007865906,0.001245578,0.002234007,0.0001576309,0.00001226675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002430285,"about_ca_system_score_gemma":0.00005729024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002599628,"about_ca_topic_score_gemma":0.00002126067,"domain_scores_codex":[0.998136,0.00004475046,0.0002876317,0.0007365664,0.0004060205,0.000389032],"domain_scores_gemma":[0.9977103,0.0001487045,0.0001002051,0.001757738,0.00006604089,0.000217007],"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":[0.00003968564,0.0003767177,0.001886612,0.00006589515,0.0000269044,0.0002169754,0.0001950708,0.0004960044,0.008511778,0.008835486,0.008829452,0.9705194],"study_design_scores_gemma":[0.0005258748,0.00003357404,0.002026961,0.00006611717,0.000006431275,0.00002931477,0.00002403827,0.9801169,0.006246072,0.0004397319,0.01023392,0.0002510747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0121363,0.0002460054,0.985819,0.000165567,0.0004623673,0.0001326493,0.00002455863,0.0001924675,0.0008210201],"genre_scores_gemma":[0.2293897,0.00001181739,0.7696816,0.0005438852,0.0001796776,9.184095e-7,0.00009132247,0.00001536761,0.00008571831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9796209,"threshold_uncertainty_score":0.5981992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09297801264256336,"score_gpt":0.3406305115956075,"score_spread":0.2476524989530442,"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."}}