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Coronavirus (COVID-19) is a pandemic, which caused suddenly unexplained pneumonia cases and caused a devastating effect on global public health. Computerized tomography (CT) is one of the most effective tools for COVID-19 screening. Since some specific patterns such as bilateral, peripheral, and basal predominant ground-glass opacity, multifocal patchy consolidation, crazy-paving pattern with a peripheral distribution can be observed in CT images and these patterns have been declared as the findings of COVID-19 infection. For patient monitoring, diagnosis and segmentation of COVID-19, which spreads into the lung, expeditiously and accurately from CT, will provide vital information about the stage of the disease. In this work, we proposed a SegNet-based network using the attention gate (AG) mechanism for the automatic segmentation of COVID-19 regions in CT images. AGs can be easily integrated into standard convolutional neural network (CNN) architectures with a minimum computing load as well as increasing model precision and predictive accuracy. Besides, the success of the proposed network has been evaluated based on dice, Tversky, and focal Tversky loss functions to deal with low sensitivity arising from the small lesions. The experiments were carried out using a fivefold cross-validation technique on a COVID-19 CT segmentation database containing 473 CT images. The obtained sensitivity, specificity, and dice scores were reported as 92.73%, 99.51%, and 89.61%, respectively. The superiority of the proposed method has been highlighted by comparing with the results reported in previous studies and it is thought that it will be an auxiliary tool that accurately detects automatic COVID-19 regions from CT images.

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While Wireless Sensor Networks (WSN) are used in various areas nowadays, they also come in front of us in the remote follow up and management of especially main street, road and city lighting systems and in autonomous applications relating with them. This study has been conducted with the aim to determine the energy consumed by Wireless Sensor Network (WSN) based monitoring and management systems as per topological sequence of lighting systems with renewable energy sources (RES) in a grid-free environment. In this way it was aimed to maximize the life time of WSN which are formed by minimum energy consumption of lighting elements that store energy with accumulator-battery in grid-free RES lighting systems and which use this energy later on. Physical installation of lighting systems having different topological distributions will show differences with respect to costs, labour force and time. Starting from here on, different topologies for grid-free lighting systems have been created in simulation environment and they have been analyzed and an optimal solution has been searched for. Energy consumptions of each lighting system having linear, random and tree lighting topology have been determined during data exchange. For each topology lighting systems with 25, 50, 100 and 200 armatures have been designed and their energy consumptions for data exchange have been found. It has been seen that data packages were influenced at first degree from node hopping numbers within topology and as being parallel to this, it has been seen that topology consuming most energy was linear lighting and that topology consuming minimum energy was tree lighting.