Building new cities at the fringes of old ones is a mandatory nowadays to lower the over increasing population in old cities

Bitcoin becomes the focus of scientific research in the modern era. Blockchain is the underlying technology of Bitcoin because of its decentralization, transparency, trust-less, and immutability features. However, blockchain can be considered the cause of Bitcoin scalability issues, especially storage. Nodes in the Bitcoin network need to store the full blockchain to validate transactions. Over time, the blockchain size will be bulky. So, the full nodes will prefer to leave the network. This leads to the blockchain being centralized and trusted, and the security will be adversely affected. This paper proposes a Stateful Layered Chain Model based on storing accounts’ balances to reduce the Bitcoin blockchain size. This model changes the structure of the traditional blockchain from blocks to layers. The experimental results demonstrated that the proposed model reduces the blockchain size by about 50.6 %. Implicitly, the transaction throughput can also be doubled.

Geometric correction is used to correct the registration errors in remotely sensed images. These images are often compared to ground control points (GCPs) either by using an accurate map (image to map) or using another geo-referenced image (image to image) and then resampled. Accordingly, the exact locations and the appropriate pixel values can be calculated in more accurate, time-wise and effortless manner. In the traditional methods, the GCPs are manually selected and then the transformation models are applied which yield time consuming and less accurate processes. The objective of this work is to develop an automatic approach for image registration based on another georeferenced image using five feature extraction models. They are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Discrete Wavelet Transforms (DWT), (SIFT & DWT), and (SURF & DWT). The GCPs were selected based on the least-squares adjustments as the basis for improving the spatial accuracy of all the linking points in both images. The obtained results showed that models have higher accuracy in image registration with Root Mean Square Error (RMSE) less than 0.5. The developed automated image registration method provides more accurate results and saves time, money and effort.