Image Inpainting by Kriging Interpolation Technique  Firas A. Jassim Management Information Systems Department, Irbid National University,  Jordan. Abstract— Image inpainting is the art of predicting damaged regions of an image. The  manual way of image inpainting is a time consuming. Therefore, there must be an  automatic digital method for image inpainting that recovers the image from the  damaged regions. In this paper, a novel statistical image inpainting algorithm based on Kriging interpolation technique was proposed. Kriging technique automatically fills  the damaged region in an image using the information available from its surrounding  regions in such away that it uses the spatial correlation structure of points inside the  kk block. Kriging has the ability to face the challenge of keeping the structure and  texture information as the size of damaged region heighten. Experimental results  showed that, Kriging has a high PSNR value when recovering a variety of test images  from scratches and text as damaged regions. Keywords-image inpainting; image masking; Kriging; text removal; scratch removal. An Efficient Incremental Clustering Algorithm  Nidhi Gupta, USICT, GGSIPU, India. R.L.Ujjwal, USICT, GGSIPU, India. Abstract— Clustering is process of grouping data objects into distinct clusters so that  data in the same cluster are similar. The most popular clustering algorithm used is the  K-means algorithm, which is a partitioning algorithm. Unsupervised techniques like  clustering may be used for fault prediction in software modules.  This paper describes the standard k-means algorithm and analyzes the shortcomings  of standard k-means algorithm. This paper proposes an incremental clustering  algorithm. Experimental results show that the proposed algorithm produces clusters in  less computation time. Keywords - Clustering; Incremental Clustering; K-means; Unsupervised; Partitioning;  Data Objects. Adapting the Ant Colony Optimization Algorithm to the Printed Circuit  Board Drilling Problem  Taisir Eldos, Aws Kanan, Abdullah Aljumah Department Of Computer Engineering, College of Computer Engineering and  Sciences, Salman in Abdulaziz University, Saudi Arabia. Abstract— Printed Circuit Board (PCB) manufacturing depends on the holes drilling  time, which is a function of the number of holes and the order in which they are  drilled. A typical PCB may have hundreds of holes and optimizing the time to  complete the drilling plays a role in the production rate. At an early stage of the  manufacturing process, a numerically controlled drill has to move its bit over the  holes one by one and must complete the job in minimal time. The order by which the  holes are visited is of great significance in this case. Solving the TSP leads to  minimizing the time to drill the holes on a PCB. Finding an optimal solution to the TSP  may be prohibitively large as the number of possibilities to evaluate in an exact  search is (n-1)!/2 for n-hole PCB. There exist too many algorithms to solve the TSP in  an engineering sense; semi-optimal solution, with good quality and cost tradeoff.  Starting with Greedy Algorithm which delivers a fast solution at the risk of being low  in quality, to the evolutionary algorithms like Genetic algorithms, Simulated Annealing  Algorithms, Ant Colony, Swarm Particle Optimization, and others which promise better  solutions at the price of more search time. We propose an Ant Colony Optimization  (ACO) algorithm with problem-specific heuristics like making use of the dispersed  locales, to guide the search for the next move. Hence, making smarter balance  between the exploration and exploitation leading to better quality for the same cost  or less cost for the same quality. This will also offer a better way of problem  partitioning which leads to better parallelization when more processing power is to be  used to deliver the solution even faster. Keywords - Ant Colony; Optimization Algorithm; Printed Circuits Board  Drilling;Traveling Salesman.