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- 저자 : ( Shuyan G Jia ) (검색결과 2 건)
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Mathematical Analysis Power Spectrum of M-ary MSK and Detection with Optimum Maximum Likelihood
( Zheng Niu ),( Yuzhong Jiang ),( Shuyan G Jia ),( Zhi Huang ),( Wenliang Zou ),( Gang Liu ),( Yu Li ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.8
In this paper, the power spectral density(PSD) for Multilevel Minimum Shift Keyed signal with modulation index h=1/2 (M-ary MSK) are derived using the mathematical method of the Markov Chain model. At first, according to an essential requirement of the phase continuity characteristics of MSK signals, a complete model of the whole process of signal generation is built. Then, the derivations for autocorrelation functions are carried out precisely. After that, we verified the correctness and accuracy of the theoretical derivation by comparing the derived results with numerical simulations using MATLAB. We also divided the spectrum into four components according to the derivation. By analyzing these figures in the graphic, each component determines the characteristics of the spectrum. It is vital for enhanced spectral characteristics. To more visually represent the energy concentration of the main flap and the roll-down speed of the side flap, the specific out-of-band power of M-ary MSK is given. OMLCD(Optimum Maximum Likelihood Coherent Detection) of M-ary MSK is adopted to compare the signal received with prepared in advance in a code element T to go for the best. And M-ary MSK BER(Bit Error Rate) is compared with the same ary PSK (Phase Shift Keying) with M=2,4,6,8. The results show the detection method could improve performance by increasing the length of L(memory inherent) in the phase continuity.
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Xiaoning Wu,Xiaoqian Xu,Jialing Zhou,Yameng Sun,Huiguo Ding,Wen Xie,Guofeng Chen,Anlin Ma,Hongxin Piao,Bingqiong Wang,Shuyan Chen,Tongtong Meng,Xiaojuan Ou,Hwai-I Yang,Jidong Jia,Yuanyuan Kong,Hong Yo 대한간학회 2023 Clinical and Molecular Hepatology(대한간학회지) Vol.29 No.3
Background/Aims: Existing hepatocellular carcinoma (HCC) prediction models are derived mainly from pretreatment or early on-treatment parameters. We reassessed the dynamic changes in the performance of 17 HCC models in patients with chronic hepatitis B (CHB) during long-term antiviral therapy (AVT). Methods: Among 987 CHB patients administered long-term entecavir therapy, 660 patients had 8 years of follow-up data. Model scores were calculated using on-treatment values at 2.5, 3, 3.5, 4, 4.5, and 5 years of AVT to predict threeyear HCC occurrence. Model performance was assessed with the area under the receiver operating curve (AUROC). The original model cutoffs to distinguish different levels of HCC risk were evaluated by the log-rank test. Results: The AUROCs of the 17 HCC models varied from 0.51 to 0.78 when using on-treatment scores from years 2.5 to 5. Models with a cirrhosis variable showed numerically higher AUROCs (pooled at 0.65–0.73 for treated, untreated, or mixed treatment models) than models without (treated or mixed models: 0.61–0.68; untreated models: 0.51–0.59). Stratification into low, intermediate, and high-risk levels using the original cutoff values could no longer reflect the true HCC incidence using scores after 3.5 years of AVT for models without cirrhosis and after 4 years of AVT for models with cirrhosis. Conclusions: The performance of existing HCC prediction models, especially models without the cirrhosis variable, decreased in CHB patients on long-term AVT. The optimization of existing models or the development of novel models for better HCC prediction during long-term AVT is warranted.
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