Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats

Block matching (BM)

Conserving water and providing for the future generation is one of the most important principals established on the agenda concerning sustainable development goals. The aim of the paper is to evaluate the sustainability of the quantity and quality of water sources for Erbil City, as well as their safety and security based on the standard limitations. Therefore, the study computed the adapted scale of measuring water quantity and the demand of water and then evaluated the collected data from the water directorates related to both surface water and groundwater for the studied area. The study focused on the management of water supply and main factors that affected the lack of the sustainability. The next step was the planning of appropriate solution for those problems, such as avoiding drilling of illegal groundwater wells and managing water sector that made the poor water management as well. The use of additional surface water accomplished with the construction of extra water treatment plants was seen as an alternative to consuming groundwater. Reusing of processed sewage for various consumption and recharging of groundwater was considered as sustainable strategy and management for the water field in Erbil City.

Object tracking is important and challenging task in many computer

One of the main goals of computer vision is to enable computers to replicate the basic functions of human vision such as motion perception and scene understanding. To achieve the goal of intelligent motion perception, much effort has been spent on object tracking, which is one of the most important challenges in computer vision topics. The formulations of mathematical models of many systems are basic steps in the process of evaluating their behavior; unfortunately, such formulations may become too complex or may not even be possible. Consequently, empirical functional relationships are often developed to describe system behavior using experimental data. Curve fitting, also known as regression analysis, is used to find the "best fit" line or curve for a series of data points. Most of the time, the curve fit will produce an equation that can be used to find points anywhere along the curve. This study proposes new methods to deal with the trajectory by converting the trajectory points into approximation function using curve fitting function to smooth the data; improving the appearance of the trajectory, extracting important features such as slope and intersection point.