Goals: Using Intelligent Techniques for routing in Mobile Ad-hoc Networks (MANETs) to increase robustness under dynamic conditions.

Goals: An ad-hoc network is the cooperative engagement of a collection of mobile nodes without the required intervention of any centralized access point or existing infrastructure. In this project we implement Ad-hoc On Demand Distance Vector Routing (AODV), a novel algorithm for the operation of such ad-hoc networks. Each Mobile Host operates as a specialized router, and routes are obtained as needed (i.e., on-demand) with little or no reliance on periodic advertisements. Our new routing algorithm is quite suitable for a dynamic self starting network, as required by users wishing to utilize ad-hoc networks. AODV provides loop-free routes even while repairing broken links. Because the protocol does not require global periodic routing advertisements, the demand on the overall bandwidth available to the mobile nodes is substantially less than in those protocols that do necessitate such advertisements. Nevertheless we can still maintain most of the advantages of basic distance-vector routing mechanisms. We show that our algorithm scales to large populations of mobile nodes wishing to form ad-hoc networks.

Goals: In this project after investigating the multiresolution filters, with the aid of nonlinear robust filters theory, a framework for robust multiresolution filters will be presented. A new algorithm will be proposed for the removal of gaussian and impulsive noise from the images

Goals: Due to the increasing investment on producing multimedia content and using it in diverse distributed areas on the Internet, searching image databases has turned into a hot research topic. In traditional methods, statistical characteristics of an image such as color, texture and shape are immensely used to search in the databases. These methods do not present suitable results in many cases because they do not utilize the semantic relations. Therefore, some researchers focus on semantic methods. Many of these methods are trying to extract the semantic relations through the statistical information of the images. Indeed, there is a gap between two levels of statistical features and comprehensible semantic relations which is known as semantic gap. Building a semantic framework, classifying the images and ultimately, putting them into the mentioned framework can be used to bridge this gap. In this project the Bayesian network approach is utilized for classification and content-based search of images in a semantic framework.

Goals: Scientific research in the different section of an object-based video encoder especially in providing the object-based input of the encoder and object post-processing.

Goals: 1- To find the minimal achievable transmission rate for a multimedia application with admissible distortion 2- To organize an algorithm for such a minimal rate over admissible distortion

Goals: The aim of this thesis is about the detecting real and fake facial expression by thermal images.

Goals: Presenting a new approach using network coding for multi-path layered video streaming over ad-hoc networks

Goals: Presenting a new approach in overlay multicasting for ad hoc networks for multimedia applications

Goals: To make an Internet kiosk for using in public places

Goals: 1. Define new and efficient MP feature vectors based on local image texture; 2. Using MP for better very low bit rate video coding, based on improving some parts of Hybrid-DCT video coding with this regards.

Goals: 1. Making one of multicast protocol scalable; 2. Working on clustering and examine  its effect on performance of multicast protocol