The Global Positioning System (GPS) allows locating an object in any part of the World with a certain degree of accuracy. Some precision farming activities need to operate with a sub-metric level of accuracy (Grewal et al. 2007). In this article, an approach is introduced to determine, by means of relative positioning, the magnitude and direction of error that the GPS presents. With this error vector it is possible to correct any low cost standard GPS receiver to improve the positional accuracy and to obtain thus more exact distances.
One of the methods used for compressing images especially natural images is by benefiting from fractal features of images. Natural images have properties like Self-Similarity that can be used in image compressing. The basic approach in compressing methods is based on the fractal features and searching the best replacement block for the original image. In this approach the best blocks are the neighborhood blocks, this approach tries to find the best neighbor blocks; Huffman coding can offer better fast fractal compression than Arithmetic coding When compare to Arithmetic coding ,Huffman coding is best for compression, It increases the speed of compression and produces high PSNR. This work saves lot of bits in the image transmission and it also decrease the time for producing a compressed image and also increase the quality of decompressed image. Totally genetic algorithm increases the speed of convergence for reaching the best block.