Several factors can interfere in the quality of image such as aliasing, noise, artifact, and blurring, these factors can cause the degradation of image especially in edge regions. In order to reduce the effect of these factors, it is necessary to choose a robust interpolation method which can play important role of the reconstruction of the high-resolution image from its low-resolution counterpart, so as to preserving the edges and textures, increasing the resolution, and improving the image quality.
In this paper, a new image interpolation method is proposed using gradient orientation; in the first step, we estimate the edges directions for a missing pixel location using the gradient-orientation in horizontal and vertical directions. Then, in the second step we interpolate the missing pixels along the detected edge directions using a cubic spline interpolation.
We begin from a gray high-resolution image which is down-sampled by a factor of two, to obtain the low-resolution image, then; this image is reconstructed using the proposed algorithm. Our method is implemented and tested to several gray test images, and compared to other image interpolation methods. The simulation results show the effectiveness of the proposed technique using the PSNR and compared with the traditional interpolation techniques. The results showed that the proposed technique has higher accuracy, and can preserve the sharp edges and textures, and avoid the problems of blurring and the visual artifacts caused by the classical interpolation methods.

Wireless Sensor Network (WSN) is a collection of large number of tiny sensor nodes that are deployed to monitor the physical environment such temperature, humidity, etc. The sensor readings must be routed to the base station and then to the end-user. These sensor nodes have limited capabilities, especially the energy reserve, the processing ability and the memory storage. So, the routing protocols design for this kind of networks is a crucial challenge. Since these routing protocols should be simple, energy-efficient, and robust to operate with a very large number of nodes. They should also be auto-configurable to node failures and changes of the network topology dynamically. This paper presents a new algorithm for gathering data in WSN based on chain forming using greedy algorithm. It focuses on equitably distributing the energy load over the whole network nodes. To avoid fast node dying, the leader role is better distributed over nodes based on their required energies to transmit to the sink. Thus, the entire network nodes would have the same lifetime and then as result, the network lifetime would be extended. We have conducted simulation-based evaluations to illustrate the performance of the proposed technique. The simulation results show that this algorithm allows network stability extension compared to the most known chaining algorithm.