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Telecommunications Industry Association TR-30.3/11-08-014 Telecon August 16, 2011

Telecommunications Industry Association TR-30.3/11-08-014 Telecon August 16, 2011. HMM Cluster Analysis. Les J. Wu August 2011. G1050 Examples (Counter results from Telchemy vqcapture alpha). Loss Generation. TGT="SD_cbr" TGT="HD_cbr" # 4 state default

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Telecommunications Industry Association TR-30.3/11-08-014 Telecon August 16, 2011

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  1. Telecommunications Industry Association TR-30.3/11-08-014 Telecon August 16, 2011

  2. HMM Cluster Analysis Les J. WuAugust 2011

  3. G1050 Examples(Counter results from Telchemy vqcapture alpha)

  4. Loss Generation TGT="SD_cbr" TGT="HD_cbr" # 4 state default tpkloss -m 4 -s 2000 -e 500 -pba 0.3 -pbc 0.02 -g 0.05 -b 0.3 -i $TGT.pcap –o $TGT.def.pcap # 4 state custom tpkloss -m 4 -s 2000 -e 500 -pba 0.3 -pbc 0.02 -pdc 0.4 -pcd 0.02 -pcb 0.5 -g 0.05 -b 0.3 -i $ TGT.pcap -o $ TGT.cus.pcap # 1-7% loss in .5% steps for loss in 10 15 20 25 30 35 40 45 50 55 60 65 70 do echo "Processing $loss" tpkloss -loss_ratio 0.0$loss -gap_ratio 0.7 -i $ TGT.pcap -o $ TGT.$loss.pcap done

  5. Pairwise P-Plot TIA-921 Model tpkloss

  6. Probability Comparison TIA-921 Model tpkloss

  7. HMM Probabilities and Component Comparison (1) TIA-921 Model tpkloss

  8. HMM Probabilities and Component Comparison (2) TIA-921 Model tpkloss

  9. Model Comparison

  10. Backup

  11. R Commands > hmm<-read.csv("hmm.csv") > vec<-c("red", "red", "blue", "blue", "green", "green") > class<-unclass(hmm$file) > class2<-unclass(hmm$codec) > vec2=c("blue", "red", "green") > class > class2 > pairs(na.omit(hmm[10:16]), pch=21, bg=vec[class]) > pairs(na.omit(hmm[17:20]), pch=21, bg=vec[class]) > hmm.prc<-princomp(na.omit(hmm[,10:16])) > summary(hmm.prc, loadings=TRUE) > pairs(hmm.prc$scores, pch=21, bg=vec[class]) > hmm.prc<-princomp(na.omit(hmm[,18:20])) > summary(hmm.prc, loadings=TRUE) > pairs(hmm.prc$scores, pch=21, bg=vec[class]) > plot(hmm.prc$scores[,1:2], pch=21, bg=vec2[class2],main="r-video b-audio") > plot(hmm.prc$scores[,1:2], pch=21, bg=vec[class],main="r-HD b-SD") > plot(hmm.prc$scores[,1:2], pch=21, bg=vec[class]) > text(hmm.prc$scores[,1:2], labels=class, pos=4) > vec<-c("red", "red", "blue", "blue", "green", "green") > pairs(hmm.prc$scores, pch=21, bg=vec[unclass(hmm$file)]) Save Me as hmm.csv:

  12. TPKLOSS Model Data Save Me as sg12.csv:

  13. Pairwise P-Plot TIA-921 Model tpkloss

  14. Pairwise Component Plot TIA-921 Model tpkloss

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