The application system presented here is for use in an Android based mobile phone by the visually impaired for detecting objects in their vicinity, which will help them move around safely, without crashing into objects. The detection of objects is done from a real time video taken from the mobile phone camera. People and objects are detected from this video using OpenCV, YOLO and FaceNet. If a human is detected, the system identifies the person. His identity will then be converted into audio and presented to the user. Similarly, objects detected in the vicinity will be presented in audio format to the user.
Wireless Sensor Network (WSN) has been studied for more than a decades that resulted in evolution of the significant applications towards assisting in sensing physical information from human inaccesible area. It was also observed from existing sysem that energy attribute is the root cause of majority of the problems associated with WSN that also gives rise to various operational reliability issue. Therefore, the prime goal of the proposed study is to present a novel predictive optimization approach of data fusion in order to jointly address the problems associated with energy efficiency and reliable operation of sensor nodes in WSN. An analytical research approach is carried out in order to ensure that a time-based synchronization scheme contributes to offer an evolutionary approach towards significant energy optimization. A simulation-based benchmarking analysis is carried out to find that proposed system offers good energy-efficient performance in comparison to existing approaches.
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