Convolutional neural networks with feature fusion method for automatic modulation classification

The analogy and application of Automatic modulation classification (AMC) detects the modulation type of received signals. Henceforth, the received signals can be correctly demodulated and, consequently, the transmitted message can be recovered. In Deep Learning (DL) based modulation classification, one major category, challenge is to pre-processing a received signal and representing it in a proper format-manner, before passing the desired-signal into the neural network. However, most existing modulation classification algorithms are neglecting the fact of mixing features between different representations, and the importance of features fusion method. This paper, however, attempted a Feature fusion scheme, for AMC, using convolutional neural networks (CNN). The approach was taken, in order to attempt fuse features extracted from In-phase & Quadrature (IQ) sequences, as well as, the Amplitude & Phase (AP) Sequences and Constellation Diagram images. Finally, simulation results show that fusing features from different representations can incorporate and leads to the best accuracy figures, achieved from each representation separately. Furthermore, our model achieved a classification accuracy of 84.68% at 0dB and 75.29% at -2dB and over 90% accuracy for high SNRs with a maximum accuracy of 94.65%, were available

Expression of Innate Immunity Genes and Damage of Primary Human Pancreatic Islets by Epidemic Strains of Echovirus: Implication for Post-Virus Islet Autoimmunity

Three large-scale Echovirus (E) epidemics (E4, E16, E30), each differently associated to the acute development of diabetes related autoantibodies, have been documented in Cuba. The prevalence of islet cell autoantibodies was moderate during the E4 epidemic but high in the E16 and E30 epidemic. The aim of this study was to evaluate the effect of epidemic strains of echovirus on beta-cell lysis, beta-cell function and innate immunity gene expression in primary human pancreatic islets. Human islets from non-diabetic donors (n = 7) were infected with the virus strains E4, E16 and E30, all isolated from patients with aseptic meningitis who seroconverted to islet cell antibody positivity. Viral replication, degree of cytolysis, insulin release in response to high glucose as well as mRNA expression of innate immunity genes (IFN-b, RANTES, RIG-I, MDA5, TLR3 and OAS) were measured. The strains of E16 and E30 did replicate well in all islets examined, resulting in marked cytotoxic effects. E4 did not cause any effects on cell lysis, however it was able to replicate in 2 out of 7 islet donors. Beta-cell function was hampered in all infected islets (P<0.05); however the effect of E16 and E30 on insulin secretion appeared to be higher than the strain of E4. TLR3 and IFN-beta mRNA expression increased significantly following infection with E16 and E30 (P<0.033 and P<0.039 respectively). In contrast, the expression of none of the innate immunity genes studied was altered in E4-infected islets. These findings suggest that the extent of the epidemic-associated islet autoimmunity may depend on the ability of the viral strains to damage islet cells and induce pro-inflammatory innate immune responses within the infected islets