The intention of image inpainting is to complete or fill the corrupted or missing zones of an image by considering the knowledge from the source region. A novel fractional order variational image inpainting model in reference to Caputo definition is introduced in this article. First, the fractional differential, and its numerical methods are represented according to Caputo definition. Then, a fractional differential mask is represented in 8-directions. The complex diffusivity function is also defined to preserve the edges. Finally, the missing regions are filled by using variational model with fractional differentials of 8-directions. The simulation results and analysis display that the new model not only inpaints the missing regions, but also heightens the contrast of the image. The inpainted images have better visual quality than other fractional differential filters.
Today the wireless sensor networks (WSN) play a crucial role in wireless technology in various domains like military, medicine, communications etc. The energy conservation is the crucial factor in the WSN. The WSN is a system which has more number of nodes in which various sensors are fabricated on the nodes to monitor various factors of the given task. These nodes will form a network by connecting the one to other for the effective communication between the nodes, and sends the whole information to the base station (BS). As the nodes which we use for the WSN are of low cost and are battery operated. The main drawback is replacement of the battery in the WSN. The main goal is to conserve the energy consumption in WSN and also to balance the load on WSN. For this many protocols are designed like LEACH, PEGASIS, PEDAP, etc. in those balancing the load and time delayed. some drawbacks are there. So we proposed a protocol so called “Tree Based Energy Balancing routing Protocol by Self Organizing” (TEBRSO), in which instead of routing tables a routing tree will be used for routing from nodes to base station (BS), which chooses one root/control node for the broadcasting messages to the selected sensor nodes. By this protocol we can save the energy consumption in WSN and can extend the life time of it. The performance of this protocol is better when we compare with other energy saving protocols.
Wireless network is ready for hundreds or thousands of nodes, where each node is connected to one or sometimes more sensors. WSN sensor integrated circuits, embedded systems, networks, modems, wireless communication and dissemination of information. The sensor may be an obligation to technology and science. Recent developments underway to miniaturization and low power consumption. They act as a gateway, and prospective clients, I usually have the data on the server WSN. Other components separate routing network routers, called calculating and distributing routing tables. Discussed the routing of wireless energy balance. Optimization solutions, we have created a genetic algorithm. Before selecting an algorithm proposed for the construction of the center console. In this study, the algorithms proposed model simulated results based on "parameters depending dead nodes, the number of bits transmitted to a base station, where the number of units sent to the heads of fuel consumption compared to replay and show that the proposed algorithm has a network of a relative.
Software Reliability Growth model (SRGM) is a mathematical model of how the software reliability improves as faults are detected and repaired. The development of many SRGMs over the last several decades have resulted in the improvement of software facilitating many engineers and managers in tracking and measuring the growth of reliability. This paper proposes Burr type XII based Software Reliability growth model with time domain data. The unknown parameters of the model are estimated using the maximum likelihood (ML) estimation method. Reliability of a software system using Burr type XII distribution, which is based on NonHomogenous Poisson process (NHPP), is presented through estimation procedures. The performance of the SRGM is judged by its ability to fit the software failure data. How good does a mathematical model fit to the data is also being calculated. To access the performance of the considered SRGM, we have carried out the parameter estimation on the real software failure datasets.
General TermsSoftware failure data, Mean value function.
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