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Radar Signal Recognition Based on Manifold Learning Method
Boyang Feng,Yun Lin 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.12
Modulation type is one of the most important characteristics used in radar signal recognition. This paper proposes a method to realize modulation identification. This algorithm applies wavelet transformation to the signal, and then uses manifold learning method to reduce the high dimension and extracts the recognition feature. The proper threshold value is set as the classifier to achieve the purpose of recognizing 5 kinds of signals (2ASK, 2FSK, 2PSK, LFM, CP)in Gauss white noise environment. The algorithm requires priori signal information no other than signal-to-noise rate. Simulation result indicates the algorithm achieves good performance.
International Digital Design Invitation Exhibition
Boyang Feng,Chen Song,HanwenXU,He Gao,Kai Huo,Kuifang Li,Niu Wei,Shaopeng Han,Tingting Qu,Ting Li,Wang, di,Xu Yeni,Yanlin Xie,Bertrand Planes,Chi-Wook Nho,Jean-benoit Lallemant,Yeon Gyu-Seok,Bettina W 한국콘텐츠학회 2010 ICCC International Digital Design Invitation Exhib Vol.2010 No.12
Identifying of Digital Signals Based on Manifold Learning
Qingbo Ji,Boyang Feng,Yun Lin,Zheng Dou,Zhiqiang Wu,Zhiping Zhang 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.2
Modulation type is one of the most important characteristics used in signal recognition. An algorithm to realize signal modulation identification is proposed in this paper. We applied wavelet transformation and STFT to the signal, and then used manifold learning method to reduce the high dimension and extracted the recognition feature. The proper threshold value was set as the classifier to achieve the purpose of recognizing 4 kinds of signals (MASK, MFSK, MPSK,QAM) in Gauss white noise environment. The algorithm requires priori signal information no other than signal-to-noise rate. Simulation result indicates the algorithm achieves good performance.
The microRNA expression profiles of mouse mesenchymal stem cell during chondrogenic differentiation
( Bo Yang ),( Hong Feng Guo ),( Yu Lan Zhang ),( Shi Wu Dong ),( Da Jun Ying ) 생화학분자생물학회(구 한국생화학분자생물학회) 2011 BMB Reports Vol.44 No.1
MicroRNAs are potential key regulators in mesenchymal stem cells chondrogenic differentiation. However, there were few reports about the accurate effects of miRNAs on chondrogenic differentiation. To investigate the mechanisms of miRNAs-mediated regulation during the process, we performed miRNAs microarray in MSCs at four different stages of TGF-β3-induced chondrogenic differentiation. We observed that eight miRNAs were significantly up-regulated and five miRNAs were down- regulated. Interestingly, we found two miRNAs clusters, miR-143/145 and miR-132/212, kept on down-regulation in the process. Using bioinformatics approaches, we analyzed the target genes of these differentially expressed miRNAs and found a series of them correlated with the process of chondrogenesis. Furthermore, the qPCR results showed that the up-regulated (or down-regulated) expression of miRNAs were inversely associated with the expression of predicted target genes. Our results first revealed the expression profiles of miRNAs in chondrogenic differentiation of MSCs and provided a new insight on complicated regulation mechanisms of chondrogenesis. [BMB reports 2011; 44(1): 28-33]