The “bottom-line” metrics of cash flow, demand, price, and return on investment
are driven by a second set of financial metrics represented by value to the
customer, cost, and the pace of innovation. Get them right relative to competition
and impressive bottom-line results should follow. Because of their importance, we
call value to the customer, variable cost, and the pace of innovation the
“fundamental metrics.”
Why did an electricity market emerge? How does it really work? What are the perfor-
mance measures that we can use to tell that the electricity market under consideration
is well functioning? These are the questions that will be explored in this book. The
main purpose of this book is to introduce the fundamental theories and concepts that
underpintheelectricitymarketswhicharebasedonthreemajordisciplines:electrical
power engineering, economics, and optimization methods.
The purpose of this book is to present detailed fundamental information on a
global positioning system (GPS) receiver. Although GPS receivers are popu-
larly used in every-day life, their operation principles cannot be easily found
in one book. Most other types of receivers process the input signals to obtain
the necessary information easily, such as in amplitude modulation (AM) and
frequency modulation (FM) radios. In a GPS receiver the signal is processed
to obtain the required information, which in turn is used to calculate the user
position. Therefore, at least two areas of discipline, receiver technology and
navigation scheme, are employed in a GPS receiver. This book covers both
areas.
This introductory chapter is devoted to reviewing the fundamental ideas of
control from a multivariable point of view. In some cases, the mathematics
and operations on systems (modelling, pole placement, etc.), as previously
treated in introductory courses and textbooks, convey to the readers an un-
realistic image of systems engineering. The simplifying assumptions, simple
examples and “perfect” model set-up usually used in these scenarios present
the control problem as a pure mathematical problem, sometimes losing the
physical meaning of the involved concepts and operations. We try to empha-
sise the engineering implication of some of these concepts and, before entering
into a detailed treatment of the different topics, a general qualitative overview
is provided in this chapter.
Computer science as an academic discipline began in the 1960’s. Emphasis was on
programming languages, compilers, operating systems, and the mathematical theory that
supported these areas. Courses in theoretical computer science covered finite automata,
regular expressions, context-free languages, and computability. In the 1970’s, the study
of algorithms was added as an important component of theory. The emphasis was on
making computers useful. Today, a fundamental change is taking place and the focus is
more on a wealth of applications. There are many reasons for this change. The merging
of computing and communications has played an important role. The enhanced ability
to observe, collect, and store data in the natural sciences, in commerce, and in other
fields calls for a change in our understanding of data and how to handle it in the modern
setting. The emergence of the web and social networks as central aspects of daily life
presents both opportunities and challenges for theory.
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.
This edition of Digital Image Processing is a major revision of the book. As in
the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992, 2002, and 2008
editions by Gonzalez and Woods, this sixth-generation edition was prepared
with students and instructors in mind. The principal objectives of the book
continue to be to provide an introduction to basic concepts and methodologies
applicable to digital image processing, and to develop a foundation that can
be used as the basis for further study and research in this field. To achieve
these objectives, we focused again on material that we believe is fundamental
and whose scope of application is not limited to the solution of specialized
problems. The mathematical complexity of the book remains at a level well
within the grasp of college seniors and first-year graduate students who have
introductory preparation in mathematical analysis, vectors, matrices, probability,
statistics, linear systems, and computer programming. The book website provides
tutorials to support readers needing a review of this background material
