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Subjects / keywords: MARTIN, JAMES; PRENTICE-HALL; 004.65 M381c

Subjects / keywords: DAVIES, D. W; JOHN WILEY & SONS; 004.65 C737c

The widespread development of using networks and relying on it and the emergence of many challenges faced by so many institutes like ministry of defense, intelligence agencies , banks, universitie...etc which depend heav...

The book is intended to be used in an introductory and intermediate level course on computer networks, both whitin Computer Science and Electric Engineering scopes. One of its characteristics is that the entire research ...

Laboratory practice is a fundamental aspect of computer network learning. Experiments tend to be very specific, frequently demanding changes in the local network topology and privileged access to the operating system con...

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly represen...

This paper analyses the parallel implementation using networks of transputers of a neural structure belonging to a particular class of neural architectures known as GSN neural networks. These architectures, belonging to ...

The book of practices of the subject of Fundamentals of Telematics offers students the first approach to computer networks in the degrees of Telematics Engineering and Telecommunication Systems Engineering. For this, thi...

"The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availabil...

The book presents the treatment of advanced topics in computer networks - mainly in those of high speed - which the designer or the network manager necessarily needs to know. These topics relate to multimedia traffic sup...

Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and char...

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffi...

Abstract Inequalities in social networks arise from linking mechanisms, such as preferential attachment (connecting to popular nodes), homophily (connecting to similar others), and triadic closure (connecting through mut...

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be com...

We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they ...

We compute the influence of a vertex on the connectivity structure of a directed network by using Shapley value theory. In general, the computation of such ratings is highly inefficient. We show how the computation can b...

Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network ...

Matrix multiplication is taken as a test bed for parallel processing on heterogeneous networks of workstations (local area networks) used as parallel machines. Two algorithms are proposed taking into account the specific...

The Internet has a very complex connectivity recently modeled by the class of scale-free networks. This feature, which appears to be very efficient for a communications network, favors at the same time the spreading of c...

Many networks display community structure--groups of vertices within which connections are dense but between which they are sparser--and sensitive computer algorithms have in recent years been developed for detecting thi...

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains ...

Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and compute...

Fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropiate tools for Intelligent Computer Systems. But on the other hand, learning alg...

Parallel computing on networks of workstations are intensively used in some application areas such as linear algebra operations. Topics such as processing as well as communication hardware heterogeneity are considered so...