In this paper we describe a design and modeling methodology of synchronous generation system for renewable energy. Our choice fell on a synchronous generator structure with permanent magnet and axial flux simple to manufacturing to reduce the production cost of the energy generation system. The modeling approach presented leads to a scientific results of high level and opens the line of research to the study of the optimization of the energy recovered by the energy accumulator.

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Intraspecific nest parasitism is a phenomenon that attracts the attention of biologists since it helps in saving the endangered species such as Slender Billed Gull. The problem comes from the fact that a parasite female lays its eggs in the nest of another female (host) of the same species which causes the abandon of the nest by the host. This behavior causes a significant reduction in future birds number and leads to the expansion of this specie. Thus, there has been an urgent necessity to clean the nest from parasitic eggs. So, our aim is to built an automatic parasitic egg identification system based on egg visual features information. Our system uses deep learning models which have proven their success for image classification. Indeed, our system conduct an egg image's pre-processing phase followed by Fast Beta Wavelet Network (FBWN) to extract the most efficient descriptors (shape, texture, and color). Then, these features will be inputted to the Stacked AutoEncoder for egg classification. Our proposed system, has been evaluated on 91-egg dataset collected from 31 clutches of eggs in Sfax region, Tunisia. Our model has given a parasitic egg identification accuracy of 89.9% which has outperformed the state of the art method and shows the efficiency and the robustness of our system.

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