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Free Space Optics (FSO) is one of the technologies which supports immense data transfer requirements. Though it offers high data rate, but experiences atmospheric attenuation due to dynamic weather conditions. On the other hand, RF communication has lower data rates but are comparatively insensitive to weather conditions. This paper focuses on a hybrid FSO/RF system with the application of Machine Learning (ML) on the prediction of Link Margin (LM) and a ML based switching mechanism between FSO/RF based on the current weather conditions. LM is considered as an important quality parameters in the design and analysis of the FSO link. Mainly rain and fog meteorological data are considered for the estimation and classification of link.

Abstract-This research work proposes an efficient four-wave mixing (FWM) based routing and wavelength assignment (RWA) scheme for the improvement of connection blocking probability in WDM/DWDM networks. However, the traditional RWA schemes are less efficient for the better quality of transmission, and the proposed RWA scheme partitions the entire fiber transmission window into N number of bands and assigns wavelength randomly from one of the band based on connection length. Finally, the analytical result proves that the mechanism reduces the FWM effect significantly in terms of connection blocking probability with higher partition, lower FWM effect and better performance.

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The flow boiling heat transfer in a vertical pipe of inner diameter 7.5 mm was investigated with pure water and Al2O3/water nanofluid as working fluids. The main heater section was made up of borosilicate glass for better visualization of flow regime. For this study, particle concentrations of 0.001%, 0.005% and 0.01% were considered. The influence of mass flux and heat flux, on flow boiling heat transfer was analysed. From the results, it is observed that boiling heat transfer coefficient is increasing with mass flux for both water and nanofluids. Use of nanofluid decreases wall superheat. The average reduction of wall superheat, as compared to water, at mass flux of 905.42 kg/s-m2 for 0.001%, 0.005% and 0.01% nanofluids is 10.8%, 21.34% and 26.79% respectively. It is also observed that heat transfer coefficient increases with particle concentration due to the changed heater surface characteristics and amendment in bubble formation mechanism. The average enhancement in heat transfer coefficient, as compared to water, for the particle concentrations of 0.001%, 0.005% and 0.01% at a mass flux of 905.42 kg/s-m2 is found to be 12.11%, 21.75% and 27.97%, respectively. Flow visualization study was also done to differentiate flow patterns of water and nanofluids. Churn flow regime was observed for water at moderate heat fluxes. However, in case of nanofluids, churn flow was not observed. The flow boiling heat transfer coefficient is observed to be high for the nanofluids compared to water. An effort has been made to explain the heat transfer mechanism, based on the existing flow boiling regime under the given conditions.