This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.
Todays, Smart Grids as the goal of next generation power grid system span wide and new aspects of power generation from distributed and bulk power generators to the end-user utilities. There are many advantages to develop these complex and multilayer system of systems such as increasing agility, reliability, efficiency, privacy, security for both Energy and ICT sections in smart grid architecture. In emerging smart grids, the communication infrastructures play main role in grid development and as a result multimedia applications are more practical for the future power systems. In this work, we introduce our method for monitoring and inspection of Wind Turbine (WT) farms in smart grids. In our proposed system, a thermal vision camera is embedded on a wireless sensor node for each WT to capture appropriate images and send video streams to the coordinator. It gets video frames to perform machine Vision Inspection (VI) and monitoring purposes. In our constructed model, turbine blade velocity estimation is targeted by detecting two important landmarks in the image that are named hub and blade. By tracking the blade in the consecutive frames and based on proposed scoring function, we can estimate the velocity of the turbine blade. Obtained results clearly indicate that accurate hub and blade positions extraction lead to error free estimation of turbine blade velocity.
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