Distributed multimedia applications are often sensitive to the Quality of Service (QoS) provided by the communication network. They usually require guaranteed QoS service, so that real-time communication is possible. However, searching a route with multiple QoS constraints is known to be a NP-complete problem. In this paper, we propose a new simple and efficient distributed QoS routing algorithm, called "DQoSR," for supporting real-time communication in high-speed networks. It searches a route that could guarantee bandwidth, delay, and delay jitter requirements. Routing decision is based only on the modified cost, hop and delay vectors stored in the routing table at each node and its directly connected neighbors. Moreover, DQoSR is proved to construct loop-free routes. Its worst case message complexity is O(|V|2), where |V| is the number of nodes in the network. Thus DQoSR is fast and scales well to large networks. Finally, extensive simulations show that average rate of establishing successful connection of DQoSR is very near to optimum (the difference is less than 0.4%).

Feature subset selection basically depends on the design of a criterion function to measure the effectiveness of a particular feature or a feature subset and the selection of a search strategy to find out the best feature subset. Lots of techniques have been developed so far which are mainly categorized into classifier independent filter approaches and classifier dependant wrapper approaches. Wrapper approaches produce good results but are computationally unattractive specially when nonlinear neural classifiers with complex learning algorithms are used. The present work proposes a hybrid two step approach for finding out the best feature subset from a large feature set in which a fuzzy set theoretic measure for assessing the goodness of a feature is used in conjunction with a multilayer perceptron (MLP) or fractal neural network (FNN) classifier to take advantage of both the approaches. Though the process does not guarantee absolute optimality, the selected feature subset produces near optimal results for practical purposes. The process is less time consuming and computationally light compared to any neural network classifier based sequential feature subset selection technique. The proposed algorithm has been simulated with two different data sets to justify its effectiveness.

In fully connected Multilayer perceptron (MLP), all the hidden units are activated by samples from the whole input space. For complex problems, due to interference and cross coupling of hidden units' activations, the network needs many hidden units to represent the problem and the error surface becomes highly non-linear. Searching for the minimum is then complex and computationally expensive, and simple gradient descent algorithms usually fail. We propose a network, where the input space is partitioned into local sub-regions. Subsequently, a number of smaller networks are simultaneously trained by overlapping subsets of the input samples. Remarkable improvement of training efficiency as well as generalization performance of this combined network are observed through various simulations.

The concept of flexible system is long being used by many researchers, aiming to solve some particular problem of adaptation. The problem is viewed differently in different situations. In this paper, we first give a set of definitions and specifications to generalize this concept applicable to any system and in particular to communication networks. Through these definitions we will formalize, what are the conditions a system should satisfy to be called as a Flexible Communication System. The rest of the paper we formalize the concepts of flexible information network, and propose an agent oriented architecture that can realize it.