The continuous progress in modern power device technology is increasingly
supported by power-specific modeling methodologies and dedicated simulation
tools. These enable the detailed analysis of operational principles on the the device
and on the system level; in particular, they allow the designer to perform trade-
off studies by investigating the operation of competing design variants in a very
early stage of the development process. Furthermore, using predictive computer
simulation makes it possible to analyze the device and system behavior not only
under regularoperatingconditions, but also at the rim of the safe-operatingarea and
beyond of it, where destructive processes occur that limit the lifetime of a power
system.
If one examines the current literature on GPS receiver design, most of it is quite a
bit above the level of the novice. It is taken for granted that the reader is already at a
fairly high level of understanding and proceeds from there. This text will be an
attempt to take the reader through the concepts and circuits needed to be able to
understand how a GPS receiver works from the antenna to the solution of user
position.
Thank you for purchasing the Earthshine Design
Arduino Starter Kit. You are now well on your way in
your journey into the wonderful world of the Arduino
and microcontroller electronics.
This book will guide you, step by step, through using
the Starter Kit to learn about the Arduino hardware,
software and general electronics theory. Through the
use of electronic projects we will take you from the
level of complete beginner through to having an
intermediate set of skills in using the Arduino.
I can remember buying my first electronic calculator. I was teaching a graduate level statistics course and I
had to have a calculator with a square root function. Back in the late 1960s, that was a pretty high-end
requirement for a calculator. I managed to purchase one at the “educational discount price” of $149.95!
Now, I look down at my desk at an ATmega2560 that is half the size for less than a quarter of the cost and
think of all the possibilities built into that piece of hardware. I am amazed by what has happened to
everything from toasters to car engines. Who-da-thunk-it 40 years ago?
Building a robot and enabling it to sense its environment is a wonderful way to
take your Arduino knowledge to the next level. In writing this book, I have brought
together my love for invention and my experience with electronics, robotics and
microcontrollers. I hope you have as much pleasure building and enhancing your
robot as I did developing the techniques contained in this book.
This book is an outgrowth of a course developed at Stanford University over
the past five years. It is suitable as a self-contained textbook for second-level
undergraduates or for first-level graduate students in almost every field that
employs quantitative methods. As prerequisites, it is assumed that the student
may have had a first course in differential equations and a first course in linear
algebra or matrix analysis. These two subjects, however, are reviewed in
Chapters 2 and 3, insofar as they are required for later developments.
Machine learning is a broad and fascinating field. Even
today, machine learning technology runs a substantial part of your
life, often without you knowing it. Any plausible approach to artifi-
cial intelligence must involve learning, at some level, if for no other
reason than it’s hard to call a system intelligent if it cannot learn.
Machine learning is also fascinating in its own right for the philo-
sophical questions it raises about what it means to learn and succeed
at tasks.
Artificial Intelligence (AI) has undoubtedly been one of the most important buz-
zwords over the past years. The goal in AI is to design algorithms that transform com-
puters into “intelligent” agents. By intelligence here we do not necessarily mean an
extraordinary level of smartness shown by superhuman; it rather often involves very
basic problems that humans solve very frequently in their day-to-day life. This can
be as simple as recognizing faces in an image, driving a car, playing a board game, or
reading (and understanding) an article in a newspaper. The intelligent behaviour ex-
hibited by humans when “reading” is one of the main goals for a subfield of AI called
Natural Language Processing (NLP). Natural language 1 is one of the most complex
tools used by humans for a wide range of reasons, for instance to communicate with
others, to express thoughts, feelings and ideas, to ask questions, or to give instruc-
tions. Therefore, it is crucial for computers to possess the ability to use the same tool
in order to effectively interact with humans.
General paradigm in solving a computer vision problem is to represent a raw image
using a more informative vector called feature vector and train a classifier on top of
feature vectors collected from training set. From classification perspective, there are
several off-the-shelf methods such as gradient boosting, random forest and support
vector machines that are able to accurately model nonlinear decision boundaries.
Hence, solving a computer vision problem mainly depends on the feature extraction
algorithm
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
