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Abdel-jaber H.,Arab Open University
Journal of King Saud University - Computer and Information Sciences | Year: 2015

Congestion control is one of the hot research topics that helps maintain the performance of computer networks. This paper compares three Active Queue Management (AQM) methods, namely, Adaptive Gentle Random Early Detection (Adaptive GRED), Random Early Dynamic Detection (REDD), and GRED Linear analytical model with respect to different performance measures. Adaptive GRED and REDD are implemented based on simulation, whereas GRED Linear is implemented as a discrete-time analytical model. Several performance measures are used to evaluate the effectiveness of the compared methods mainly mean queue length, throughput, average queueing delay, overflow packet loss probability, and packet dropping probability. The ultimate aim is to identify the method that offers the highest satisfactory performance in non-congestion or congestion scenarios. The first comparison results that are based on different packet arrival probability values show that GRED Linear provides better mean queue length; average queueing delay and packet overflow probability than Adaptive GRED and REDD methods in the presence of congestion. Further and using the same evaluation measures, Adaptive GRED offers a more satisfactory performance than REDD when heavy congestion is present. When the finite capacity of queue values varies the GRED Linear model provides the highest satisfactory performance with reference to mean queue length and average queueing delay and all the compared methods provide similar throughput performance. However, when the finite capacity value is large, the compared methods have similar results in regard to probabilities of both packet overflowing and packet dropping. © 2015 The Author. Source

Elayyan H.O.,Arab Open University
2012 International Conference on Information Technology and e-Services, ICITeS 2012 | Year: 2012

Arab Open University is a pioneer educational institution in middle east and North Africa that adopted the open learning concept and has been dedicated to form this concept and present it with various layers of perfect e-course coordination and e-monitoring and e-measurement systems that fits with the Virtual learning Environment (VLE) for quality of assurance applications. This paper argues the mutual impact of e-coordination system on students of multi sections as it presents a comparative analysis depending on AOU experience e-coordination system through the VLE and other running courses in regular education without any standardization level. © 2012 IEEE. Source

Jabeur N.,Arab Open University | Sahli N.,German University of Technology in Oman | Khan I.M.,Al-Buraimi University College
Procedia Computer Science | Year: 2013

We survey the sensor network holes from a cause-effect-solution perspective. We first propose a new taxonomy (PLMS) which classifies holes into type groups according to the cause of anomaly. We discuss the effects of holes on the sensor network. Finally, we survey the different curative approaches (prevention, detection, repairing, avoidance). © 2013 The Authors. Published by Elsevier B.V. Source

Tolba A.S.,Kuwait University | Khan H.A.,Kuwait University | Mutawa A.M.,Kuwait University | Alsaleem S.M.,Arab Open University
Textile Research Journal | Year: 2010

Defect detection of textiles is a necessary requirement for quality control and customer satisfaction. This paper presents a system for decision fusion in order to enhance the accuracy of defect detection in textiles. A multi-classifier decision fusion technique based on majority voting is presented to solve the problems of sensitivity to parameter variation and to make use of the advantages of the individual feature sets for accurate texture characterization. Features based on Gray Level Co-occurrence Matrix (GLCM), Laws Energy (LE) Filter, Local Binary Patterns (LBP), HU Moment invariants, Moment of Inertia (MOI) and Standard Deviation of Gray levels are used to train a set of Learning Vector Quantization (LVQ) classifiers. Detection accuracies of classifiers trained on single-feature sets are compared with those of the majority voting among the individual classifiers. The results obtained from majority voting indicate that the decision fusion technique improves the accuracy and reliability of the detection process. Empirical results indicate the high accuracy of the presented approach. The correct defect detection rate of the proposed approach is 98.64% with an average false acceptance rate of 0.0012. © The Author(S) 2010. Source

Amaneddine N.,Arab Open University | Condotta J.-F.,University of Lille Nord de France
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

We study in this paper the problem of global consistency for qualitative constraints networks (QCNs) of the Point Algebra (PA) and the Interval Algebra (IA). In particular, we consider the subclass corresponding to the set of relations of PA except the relations {<,=} and {>,=}, and the subclass corresponding to pointizable relations of IA one can express by means of relations of . We prove that path-consistency implies global consistency for QCNs defined on these subclasses. Moreover, we show that with the subclasses corresponding to convex relations, there are unique greatest subclasses of PA and IA containing singleton relations satisfying this property. © 2012 Springer-Verlag. Source

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