This paper studies the capacity of hybrid wireless networks with opportunistic routing (OR). We first extend the opportunistic routing algorithm to exploit high speed data transmissions in infrastructure network through base stations. We then develop linear programming models to calculate the end-to-end throughput bounds from multiple source nodes to single, as well as, multiple destination nod...

We combine two notable streams of neural networks research: Adaptive Resonance Theory (ART) and Growing Neural Gas (GNG) networks. In particular we modify the AppART neural network formulation by introducing GNG based training features. The resulting neural network outperforms its original version as well as other neural models while maintaining the functional approximation properties and hybri...

The main goal of this paper is to describe a method for exact inference in general hybrid Bayesian networks (BNs) (with a mixture of discrete and continuous chance variables). Our method consists of approximating general hybrid Bayesian networks by a mixture of Gaussians (MoG) BNs. There exists a fast algorithm by Lauritzen-Jensen (LJ) for making exact inferences in MoG Bayesian networks, and t...

With the advancement in Networking and Telecommunication technologies and the increase in mobility necessities, Wireless Networks and its related technologies are becoming more popular nowadays. Since, Wireless and Mobile Networks are still evolving, the technology requires much more optimizations. The traditional Base Station (BS) based Wireless networks and Ad-hoc Wireless networks, apart fro...

An approach for hybrid model parameter identification based on sensitivity equations approach is tested on a hybrid model for biosurfactant production process. The computer simulations were performed using software tools created in Matlab (The MathWorks, Inc.) environment. Possible difficulties and ways of practical implementation of the analyzed identification algorithms for the identification...

This paper presents a comparative study of the performance of three versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) hybrid model and two innovative hybrid models in the prediction of oil and gas reservoir properties. ANFIS is a hybrid learning algorithm that combines the rule-based inferencing of fuzzy logic and the back-propagation learning procedure of Artificial Neural Networks. Fu...

The capacity of large hybrid wireless networks is considered in this project. The hybrid network is based on an ad-hoc network with an embedded wired infrastructure. Both types of wireless transmissions are allowed in hybrid networks: peer-to-peer and through infrastructure. The report focuses on the case when the number of base stations in the hybrid network grows sub-linearly with the network...

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible tomodel both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybr...

Both industry and the academic world are becoming more and more interested in techniques to model, analyse, and control complex systems that contain both analog and logical components. Such systems are called hybrid systems. Examples of hybrid systems are telecommunication networks, flexible production systems, parallel processing systems, traffic networks, and so on. In this paper we present a...

The enhancement of QoS has emerged as a major research area in Real Time Multimedia Transmission in Hybrid Wireless networks. The network made up of Infrastructure and Adhoc Wireless Networks is sometimes referred to as Hybrid wireless network. We propose a scheme named PIW (PSNR Improvement in Wireless) to improve PSNR by using RTP/RTCP . RTCP provides control information that is used, by adju...