Ahmed Atwan

Analysis of performance of Mobile Agents in distributed content based Image Retrieval

Mobile agents often have the task to collect data from several predefined servers. The factors that affect the performance of mobile agents in retrieving information from the network are the number of agents and the routing time taken by the participated agents to complete the assigned tasks. This paper presents an approach for distributed content based image retrieval using mobile agents. The proposed approach aims at reducing the network traffic of query passing in the network by clustering similar images inside each sever together. During query processing, the characteristic of the clusters used as the basis for selecting the source of the images; consequently, mobile agent is routed intelligently to all servers maintaining images belonging to the respective clusters. Experimental results show that the proposed approach reduces the network traffic and also improve the total time taken to retrieve the query results compared with other approaches.

Mobile agents' authentication using a proposed light Kerberos system

A mobile agent is a program that is issued either by a human user or an application to travel over a network autonomously to either gather information or perform some computation. However its many benefits, mobile agent technology resulted in new significant security threats from both malicious agents and hosts. This paper introduces novel efficient and light security framework for mobile agent environment based on Kerberos system. The proposed framework aims at reducing the usual overhead resulting inside the Kerberos system by using 2-layer software that accomplishes the work of the hardware components. Besides, it reduces the required hardware which means low cost and high speed communication compared to traditional Kerberos system.

Magnetic Resonance Brain Imaging Segmentation Based on Cascaded Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Clustering

A digital image can be partitioned into multiple segments, which is known as image segmentation. There are many challenging problems for making image segmentation. Therefore, medical image segmentation technique is required to develop an efficient, fast diagnosis system. In this paper, we proposed a segmentation framework that is based on Fractional-order Darwinian Particle Swarm Optimization (FODPSO) and Mean Shift (MS) techniques. In pre-processing phase, MRI image is filtered, and the skull stripping is removed. In segmentation phase, the output of FODPSO is used as input to MS. Finally, we make a validation to the segmented image. The proposed system is compared with some segmentation techniques by using three standard datasets of MRI brain. For the first dataset, proposed system was achieved 99.45 % accuracy, whereas the DPSO was achieved 97.08 % accuracy. For the second dataset, the accuracy of the proposed system is 99.67 %, whereas the accuracy of DPSO is 97.08 %. Proposed system improves the accuracy of image segmentation of brain MRI as shown in the experimental results.

Blind shot-based watermarking of RGB video stream in the wavelet domain

In this paper a new video watermarking scheme is proposed which depends on 2-level DWT decomposition of each component of RGB components in each frame. The scheme embeds independent watermarks into different shots. The matching between shots and watermarks is based on GA. The scheme chooses between HL1 of red or green or blue components of each frame based on a key and embeds error correcting code into one of them. The scheme is blind. Experimental results shows that the scheme is robust against attacks such as frame dropping, frame averaging, frame swapping, statistical analysis, and MPEG-2 and MPEG-4 compression. The proposed scheme uses two keys to increase the security.

Semantic Relations Extraction and Ontology Learning from Arabic Texts—A Survey

Semantic relations are the building blocks of the Ontologies and any modern knowledge representation system. Extracting semantic relations from the text is one of the most significant and challenging phases in the Ontology learning process. It is essential in all Ontology learning phases starting from building the Ontology from scratch, down to populating and enriching the existing Ontologies. It is challenging, on the other hand, as it requires dealing with natural language text, which represents various challenges especially for syntactically ambiguous languages such as Arabic. In this paper, we present a comprehensive survey of Arabic Semantic Relation Extraction and Arabic Ontology learning research areas. We study Arabic Ontology learning in general while focusing on Arabic Semantic Relation Extraction particularly, as being the most significant, yet challenging task in the Ontology learning process. To the best of our knowledge, this is the first work that addresses the process of Arabic Semantic Relation Extraction from the Ontology learning perspective. We review the conducted researches in both areas. For each research the used technique is illustrated, the limitations and the positive aspects are clarified.

Enhancing failure recovery in wireless sensor network based on Grade Diffusion

Wireless sensor networks (WSNs) are an important instrument for observing distributed remote environments. In most applications, failure is a serious problem. There are numerous algorithms of failure detection and failure recovery. However, most of them may reduce network lifetime or achieve poor accuracy. This paper proposes failure recovery algorithm based on Grade Diffusion with using Shortest Save Path. The sensor node may arrange the sensor nodes routing and save some backup nodes depending on the shortest path to reduce the energy consumption for the re-looking routing. In case the direction is determined based on the amount of power required, the sensor nodes routing is broken. In the simulation, the proposed algorithm is measured by power consumption, fault detection accuracy, number of hops, number of dead nodes, and time number of neighbour nodes.

A Proposed Peer Selection Algorithm for Transmission Scheduling in P2P-VOD Systems

Video transmission in peer-to-peer video-on- demand faces some challenges. These challenges include long transmission delay and poor quality of service. The peer selection plays an important role in enhancing transmission efficiency. For this reason, a proposed algorithm for peer selection is introduced to overcome these challenges. The proposed algorithm consists of four steps. First, the peers exchange their own buffer maps with other peers. Second, the requested segments are ordered according to their priorities. Third, neighbors of the receiver are evaluated by the efficiency estimation. Finally, the efficient sender list is applied to solve the overloading and bottleneck on the highest efficient sender. A simulation is introduced to evaluate the performance of the proposed algorithm compared to a peer selection algorithm with context-aware adaptive (CAA) data scheduling algorithm. The results show that, the proposed algorithm reduces initial buffering delay and achieves high throughput rather than CAA algorithm.