Umar Manzoor

A Fault Tolerant Infrastructure for Mobile Agen

Mobile agent technology is a promising paradigm for a myriad of real world applications. Owing to their tremendous capabilities, multiagent systems have been scoped in a large number of applications. However issues related to fault tolerance can hamper the suitability of mobile agents in these real world systems. In this paper we have proposed an infrastructure which provides agent fault tolerance. An algorithm similar to the sliding window model ensures a fault tolerant behavior. Different types of agents, work in collaboration to provide the desired system behavior by tolerating faults. The proposed infrastructure will be applicable in a variety of systems including, ecommerce, online banking etc. With the increasing market of electronic commerce it becomes an interesting aspect to use autonomous mobile agents for electronic business transactions.

An agent based system for activity monitoring on network - ABSAMN

Computer network as a force drags its customers to share more and more resources while on the other hand managing such network recourses is a challenging job for an IT professional and perhaps becoming difficult humanly. In this paper, we have proposed an agent based system for activity monitoring on network (ABSAMN) for the monitoring of resources over a network, suitable for network of networks; commonly known as CAN (campus area network). Multi-agent system is a system composed of several agents, collectively capable of achieving goals that are difficult to achieve by an individual agent or monolithic system. We propose the use of multi-agent system to ensure proper system operation by watching for inconsistencies in user activities, node level activity, internet monitoring, and system configuration. The system is fully autonomous and once initialized with the given rules and domain knowledge ABSAMN manages resources on its own with the help of mobile agents. We have evaluated this architecture on the university campus having seven labs equipped with 20-300 number of PCs in various labs. Results were very promising and support the implementation of the solution.

iDetect: Content Based Monitoring of Complex Networks using Mobile Agents

With the evolution in computer networks over the last decade, researchers are trying to come up with efficient approaches which can help network administrator in implementing the acceptable use policy for large complex networks. In this paper we have modified An Agent Based System for Activity Monitoring on Network - ABSAMN architecture and proposed iDetect: Content Based Monitoring of Complex Network using Mobile Agents which uses the content (i.e. text, image and video) of the application for categorization purpose. iDetect is implemented in Java using Java Agent Development (JADE) framework and supports platform independence; however, the framework has been tested only on Microsoft Windows (any version) environment. We have evaluated iDetect and ABSAMN on same configuration concurrently at the university campus having four labs equipped with 60-120 number of PCs in various labs; experimental results shows that iDetect efficiently detects known/unknown illegal activities (applications/websites) running on the network as compared to ABSAMN.

QUIET: A Methodology for Autonomous Software Deployment using Mobile Agents

Every software setup has an installation wizard that helps the user to install/un-install the software on PCs. Typically user interaction is required and the process cannot proceed without user input. Silent Unattended Installation Package Manager (SUIPM) automates the process of software installation/un-installation and can be used to deploy any software silently without user interaction. In this paper, we have proposed A Methodology for Autonomous Software Deployment using Mobile Agents, which deploys silent unattended installation/un-installation packages efficiently and smartly on networks without user interaction or intervention, suitable for network of networks, commonly known as CAN (campus area network). The system once initialized is fully autonomous and deployment of the software(s) is performed efficiently and autonomously with the help of mobile agents. We have evaluated this architecture on the university campus having 7 laboratories equipped with 20-300 PCs in various laboratories. Results are very promising and support the implementation of the solution.

Categorization of malicious behaviors using ontology-based cognitive agents

Every organization uses computer networks (consisting of networks of networks) for resource sharing (i.e. printer, files, etc.) and communication. Computer networks today are increasingly complex, and managing such networks requires specialized expertise. Monitoring systems help network administrators in monitoring and protecting their network by not allowing users to run illegal application or changing the configuration of network nodes. In this paper we have developed an agent based system for activity monitoring on networks (ABSAMN) and proposed Categorization of Malicious Behaviors using Cognitive Agents (CMBCA). This uses ontology to predict unknown illegal applications based on known illegal application behaviors. CMBCA is an intelligent multi agent system used to detect known and unknown malicious activities carried out users over the network. We have compared An Agent Based System for Activity Monitoring on Network (ABSAMN) and Categorization of Malicious Behaviors using Cognitive Agents (CMBCA) concurrently at the university campus having seven labs equipped with 20 to 300 PCs in various labs. Both systems were tested on the same configuration; results indicate that CMBCA outperforms ABSAMN in every aspect.

3D perception from binocular vision for a low cost humanoid robot NAO

Depth estimation is a classical problem in computer vision and after decades of research many methods have been developed for 3D perception like magnetic tracking, mechanical tracking, acoustic tracking, inertial tracking, optical tracking using markers and beacons. The vision system allows the 3D perception of the scene and the process involves: (1) camera calibration, (2) image correction, (3) feature extraction and stereo correspondence, (4) disparity estimation and reconstruction, and finally, (5) surface triangulation and texture mapping. The work presented in this paper is the implementation of a stereo vision system integrated in humanoid robot. The low cost of the vision system is one of the aims to avoid expensive investment in hardware when used in robotics for 3D perception. In our proposed solution, cameras are highly utilized as in our opinion they are easy to handle, cheap and very compatible when compared to the hardware used in other techniques. The software for the automated recognition of features and detection of the correspondence points has been programmed using the image processing library OpenCV (Open Source Computer Vision) and OpenGL (Open Graphic Library) is used to display the 3D models obtained from the reconstruction. Experimental results of the reconstruction and models of different scenes are shown. The results obtained from the program are evaluated comparing the size of the objects reconstructed with that calculated by the program. Implementation of a stereo vision system integrated in humanoid robot is proposed.Low cost robotics vision system for 3D perception avoids expensive hardware cost.Cameras are highly utilized as they are easy to handle, cheap and very compatible.

Agent Based Activity Monitoring System - ABAMS

Multi-agent system (MAS) is a system composed of several agents, collectively capable of achieving goals that are difficult to achieve by an individual agent or monolithic system. MAS is ideal for a network-like application for its flexibility, distributed nature, and modifiability, without the need for detailed rewriting of the application. In this paper, we have proposed agent based activity monitoring system (ABAMS) for the monitoring of resources over a network, suitable for network of networks; commonly known as CAN (campus area network). The system is fully autonomous and once initialized with the given rules and domain knowledge ABAMS manages resources on its own with the help of mobile agents.

Autonomous agents: Smart network installer and tester (SNIT)

With the evolution in the computer networks, many companies are trying to come up with efficient products which can make life easier for network administrators. Silent automated installation is one category among such products. In this paper, we have proposed silent network installer and tester (SNIT) which provides silent, automated and intelligent installation of software(s) over the network. SNIT will intelligently install any kind of software on request of the network administrator. SNIT not only provides installation services, it also enables network administrators to verify the installation of software over the network. SNIT uses an efficient algorithm to transfer the software installation setup on the network nodes. The system is fully autonomous and does not require any kind of user interaction or intervention. Network administrator is responsible for assigning the task(s) to SNIT and once assigned it performs the task(s) over the network autonomously with the help of intelligent mobile agents. SNIT has been deployed for evaluation purpose at the University Campus Lab equipped with 200 machines and some of the popular softwares were tested in the lab, results were very promising and support the implementation of the solution.

Semantic Image Retrieval: An Ontology Based Approach

Images / Videos are major source of content on the internet and the content is increasing rapidly due to the advancement in this area. Image analysis and retrieval is one of the active research field and researchers from the last decade have proposed many efficient approaches for the same. Semantic technologies like ontology offers promising approach to image retrieval as it tries to map the low level image features to high level ontology concepts. In this paper, we have proposed Semantic Image Retrieval: An Ontology based Approach which uses domain specific ontology for image retrieval relevant to the user query. The user can give concept / keyword as text input or can input the image itself. Semantic Image Retrieval is based on hybrid approach and uses shape, color and texture based approaches for classification purpose. Mammals domain is used as a test case and its ontology is developed. The proposed system is trained on Mammals dataset and tested on large number of test cases related to this domain. Experimental results show the efficiency / accuracy of the proposed system and support the implementation of the same.

V-NIP Ceaser: Video Stabilization System

In this paper, we propose a practical and robust approach of video stabilization that produces full-frame stabilized videos with good visual quality. While most previous methods end up with producing low resolution stabilized videos, our completion method produces full-frame videos by temporally filling in missing frame parts by locally aligning required data from neighboring frames. The proposed system has been evaluated on large number of real life videos; results were very promising and support the implementation of the solution.