Notwithstanding its infancy, wireless mesh networking (WMN) is a hot and
growing field. Wireless mesh networkS began in the military, but have since
become of great interest for commercial use in the last decade, both in local
area networkS and metropolitan area networkS. The attractiveness of mesh
networkS comes from their ability to interconnect either mobile or fixed
devices with radio interfaces, to share information dynamically, or simply to
extend range through multi-hopping.
Wireless networking is undergoing a transformation from what has
been primarily a medium for supporting voice traffic between telephones,
into what is increasingly becoming a medium for supporting traffic among
a variety of digital devices transmitting media of many types (voice,
data, images, video. etc.) Wireline networking underwent a similar
transformation in the 1990s, which led to an enormous build-up in the
capacity of such networkS, primarily through the addition of new optical
fiber, switches and other infrastructure.
Resource allocation is an important issue in wireless communication networkS. In
recent decades, cognitive radio technology and cognitive radio-based networkS have
obtained more and more attention and have been well studied to improve spectrum
utilization and to overcomethe problem of spectrum scarcity in future wireless com-
munication systems. Many new challenges on resource allocation appear in cogni-
tive radio-based networkS. In this book, we focus on effective solutions to resource
allocation in several important cognitive radio-based networkS, including a cogni-
tive radio-basedopportunisticspectrum access network, a cognitiveradio-basedcen-
tralized network, a cognitive radio-based cellular network, a cognitive radio-based
high-speed vehicle network, and a cognitive radio-based smart grid.
This book is the result of works dedicated to specific applications of
metaheuristics in smart electrical grids. From electric transmission,
distribution networkS to electric microgrids, the notion of intelligence refers
to the ability to propose acceptable solutions in an increasingly more
restrictive environment. Most often, it refers to decision-making assisting
tools designed to support all human action.
Recent work has shown that convolutional networkS can
be substantially deeper, more accurate, and efficient to train
if they contain shorter connections between layers close to
the input and those close to the output. In this paper, we
embrace this observation and introduce the Dense Convo-
lutional Network (DenseNet), which connects each layer
to every other layer in a feed-forward fashion.
Abstract: It may sound trite, but it is definitely true: the smart grid has the potential to completely transform the energyindustry. However, smart meters and grid management alone will not ensure the success of the smart grid. Unliketraditional IT networkS, smart grids require consideration of energy measurement and security. To completely optimize thistechnology, smart grid designs must focus on energy measurement and security. This tutorial considers the benefits ofboth energy measurement and security and how they make machine-to-machine networkS different from traditional IT.
Single-Ended and Differential S-Parameters
Differential circuits have been important incommunication systems for many years. In the past,differential communication circuits operated at lowfrequencies, where they could be designed andanalyzed using lumped-element models andtechniques. With the frequency of operationincreasing beyond 1GHz, and above 1Gbps fordigital communications, this lumped-elementapproach is no longer valid, because the physicalsize of the circuit approaches the size of awavelength.Distributed models and analysis techniques are nowused instead of lumped-element techniques.Scattering parameters, or S-parameters, have beendeveloped for this purpose [1]. These S-parametersare defined for single-ended networkS. S-parameterscan be used to describe differential networkS, but astrict definition was not developed until Bockelmanand others addressed this issue [2]. Bockelman’swork also included a study on how to adapt single-ended S-parameters for use with differential circuits[2]. This adaptation, called “mixed-mode S-parameters,” addresses differential and common-mode operation, as well as the conversion betweenthe two modes of operation.This application note will explain the use of single-ended and mixed-mode S-parameters, and the basicconcepts of microwave measurement calibration.
