White balance is one of the key processes in a camera pipeline. Accuracy can be challenging when a scene is illuminated by multiple color light sources. We designed and built a studio which consisted of a controllable multiple LED light sources that produced a range of correlated color temperatures (CCTs) with high color fidelity that were used to illuminate test scenes. A two Alternative Forced Choice (2AFC) experiment was performed to evaluate the white balance appearance preference for images containing a model in the foreground and target objects in the background indoor scene. The foreground and background were lit by different combinations of cool to warm sources. The observers were asked to pick the one that was most aesthetically appealing to them. The results show that when the background is warm, the skin tones dominated observers' decisions and when the background is cool the preference shifts to scenes with same foreground and background CCT. The familiarity and unfamiliarity of objects in the background scene did not show a significant effect.
ABSTRACT:A wireless sensor network is type of wireless network. Basically it consist a collection of tiny device are called sensor node, sensor node has a resource constraint means battery power, storage and communication capability. Network lifetime is the most important metric for the evaluation of sensor networks. In a resource-constrained environment, the utilization of every limited resource must be considered. The network can only fulfil its purpose as long as it is considered alive, but not after that. It is therefore an indicator for the maximum utility a sensor network can provide. Energy efficiency is therefore of paramount importance in sensor networks that are constrained by limited resources. In this paper we purposed a novel technique in which we added the no of relay nodes in the network to increase the network lifetime. KEYWORDS:Sensor network, relay nodes, sensor nodes, lifetime, and throughput. I.INTRODUCTIONThis wireless sensor networks is depends on a simple equation: Sensing + CPU + Radio = Thousands of possible applications. A wireless sensor network is type of wireless network. It is small and infrastructure less. Basically wireless sensor network consist a number of sensor node, called tiny device and these are working together to detect a region to take data about the environment. Each node in a sensor network is typically equipped with a radio transceiver or other wireless communication device, a small microcontroller, and an energy source, usually a battery. The size of a single sensor node can vary from shoebox-sized nodes down to devices the size of grain of dust. The cost of sensor nodes is similarly variable, ranging from hundreds of dollars to a few cents, depending on the size of the sensor network and the complexity required of individual sensor nodes. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth, among which energy is the scarcest resource of WSN nodes. Every sensor in a WSN has a sensing range and a transmission range. An object can be discovered by a sensor if it is within the sensing range of the sensor, and two sensors can transmit data to each other if they are within each other's transmission range. Routing, one of the most energy-expensive operation, is usually multi-hop (from the source, node to node, towards the destination), due to the polynomial growth in the energy-cost of radio transmission with respect to the transmission distance.WSNs are to be deployed in large numbers in various environments, including remote and hostile regions, with ad-hoc communications. The energy in nodes determines the lifetime of WSNs. The lifetime of WSNs will be shortened if some sensors are used more often than others as their battery power is depleted sooner. For this reason, sensor deployment, algorithms and protocols need to address lifetime maximization, robustness and fault tolerance issues. Therefore, in sensor deployment, network topology is important. To be fair, it is alway...
In this paper, a new one-parameter class of fixed point iterative method is proposed to approximate the fixed points of contractive type mappings. The presence of an arbitrary parameter in the proposed family increases its interval of convergence. Further, we also propose new two-step and three-step fixed point iterative schemes. We also discuss the stability, strong convergence and fastness of the proposed methods. Furthermore, numerical experiments are performed to check the applicability of the new methods, and these have been compared with well-known similar existing methods in the literature.
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