The current interest in complex networks is a part of a broader movement towards research on complex systems. Motivation of this work raises the two challenging questions: (i) Are real networks fundamentally random preferential attached without any deterministic attachment for both un-weighted and weighted networks? (ii) Is there a coherent physical idea and model for unifying the study of the ...

The ongoing evolution from packet based networks to hybrid networks in Research & Education (R&E) networks, or what are the fundamental reasons behind this fundamental paradygm shift and the resulting growing gap between commercial and R&E Internet networks? As exemplified by the Internet2 HOPI initiative, the new GEANT2 backbone, the GLIF initiative and projects such as Dragon and Ultralight, ...

We introduce a Physically Unclonable Function (PUF) based on an ultra-fast chaotic network known as Hybrid Boolean Network (HBN) implemented field programmable gate array. The network, consisting of N coupled asynchronous logic gates displaying dynamics the sub-nanosecond time scale, acts `digital fingerprint' by amplifying small manufacturing variations during period transient chaos. In contra...

Future mobile networks are expected to involve systems that are based on different technologies, such as WiFi, WiMAX, 2G/3G/3G1, LTE, and satellite. To address this scenario, ITU has defined integrated and hybrid networks in the framework of Next-Generation Networks. The interest is to exploit the cooperation of different wireless communication systems (segments) to provide service to mobile us...

Three dimensional (3D) elastic hybrid networks built from interconnected nano- and microstructure building units, in the form of semiconducting-carbonaceous materials, are potential candidates for advanced technological applications. However, fabrication of these 3D hybrid networks by simple and versatile methods is a challenging task due to the involvement of complex and multiple synthesis pro...

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated with an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the constr...