Recent advances in wireless communication technologies have had a transforma-
tive impact on society and have directly contributed to several economic and social
aspects of daily life. Increasingly, the untethered exchange of information between
devices is becoming a prime requirement for further progress, which is placing an
ever greater demand on wireless bandwidth. The ultra wideband (UWB) system
marks a major milestone in this progress. Since 2002, when the FCC allowed the
unlicensed use of low-power, UWB radio signals in the 3.1–10.6GHz frequency
band, there has been significant synergistic advance in this technology at the cir-
cuits, architectural and communication systems levels. This technology allows for
devices to communicate wirelessly, while coexisting with other users by ensuring
that its power density is sufficiently low so that it is perceived as noise to other
users.
An acronym for Multiple-In, Multiple-Out, MIMO communication sends the same data as several signals
simultaneously through multiple antennas, while still utilizing a single radio channel. This is a form of
antenna diversity, which uses multiple antennas to improve signal quality and strength of an RF link. The
data is split into multiple data streams at the transmission point and recombined on the receive side by
another MIMO radio configured with the same number of antennas. The receiver is designed to take
into account the slight time difference between receptions of each signal, any additional noise or
interference, and even lost signals.
Visible light communications (VLC) is the name given to an optical wireless
communication system that carries information by modulating light in the visible spectrum
(400–700 nm) that is principally used for illumination [1–3]. The communications signal
is encoded on top of the illumination light. Interest in VLC has grown rapidly with the
growth of high power light emitting diodes (LEDs) in the visible spectrum. The
motivation to use the illumination light for communication is to save energy by exploiting
the illumination to carry information and, at the same time, to use technology that is
“green” in comparison to radio frequency (RF) technology, while using the existing
infrastructure of the lighting system.
The explosion in demand for wireless services experienced over the past 20 years
has put significant pressure on system designers to increase the capacity of the
systems being deployed. While the spectral resource is very scarce and practically
exhausted, the biggest possibilities are predicted to be in the areas of spectral reuse
by unlicensed users or in exploiting the spatial dimension of the wireless channels.
The former approach is now under intense development and is known as the cogni-
tive radio approach (Haykin 2005).
In this age of science and technology, the global economy has developed so much that our
lifestyles are now extremely modernized and developed. In some ways, modern society
seems to have reached the utmost state of advancement in various areas, including eco-
nomic development, science and technology pursuit, and the utilization of the given nat-
ural environment. However, it is important to consider approaches that may allow human
beings to stay longer on the Earth while enjoying fulfilling and peaceful daily lives.
This book is written for engineers involved in the operation, control, and
planning of electric power systems. In addition, the book provides information and
tools for researchers working in the fields of power system security and stability. The
book consists of two volumes. The first volume provides traditional techniques for the
stability analysis of large scale power systems. In addition, an overview of the main
drivers and requirements for modernization of the traditional methods for online
applications are discussed. The second volume provides techniques for online security
assessment and corrective action studies. In addition, the impact of variable generation
on the security of power systems is considered in the second volume. The first volume
may be considered as a background builder while the second volume is intended for
the coverage of edge techniques and methods for online dynamic security studies.
Striking developments have taken place since 1980 in feedback control theory. The subject has be-
come both more rigorous and more applicable. The rigor is not for its own sake, but rather that even
in an engineering discipline rigor can lead to clarity and to methodical solutions to problems. The
applicability is a consequence both of new problem formulations and new mathematical solutions
to these problems. Moreover, computers and software have changed the way engineering design is
done. These developments suggest a fresh presentation of the subject, one that exploits these new
developments while emphasizing their connection with classical control.
Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propa-
gation. Similarly, new models based on kernels have had significant impact on both
algorithms and applications.
This book is a general introduction to machine learning that can serve as a reference
book for researchers and a textbook for students. It covers fundamental modern
topics in machine learning while providing the theoretical basis and conceptual tools
needed for the discussion and justification of algorithms. It also describes several
key aspects of the application of these algorithms.
Machine learning is about designing algorithms that automatically extract
valuable information from data. The emphasis here is on “automatic”, i.e.,
machine learning is concerned about general-purpose methodologies that
can be applied to many datasets, while producing something that is mean-
ingful. There are three concepts that are at the core of machine learning:
data, a model, and learning.
