Since the advent of optical communications, a great technological effort has
been devoted to the exploitation of the huge bandwidth of optical fibers. Start-
ing from a few Mb/s single channel systems, a fast and constant technological
development has led to the actual 10 Gb/s per channel dense wavelength di-
vision multiplexing (DWDM) systems, with dozens of channels on a single
fiber. Transmitters and receivers are now ready for 40 Gb/s, whereas hundreds
of channels can be simultaneously amplified by optical amplifiers.
Short-range communications is one of the most relevant as well as diversified fields of en-
deavour in wireless communications. As such, it has been a subject of intense research and
development worldwide, particularly in the last decade. There is no reason to believe that this
trend will decline. On the contrary, the rapidly crystallizing vision of a hyper-connected world
will certainly strengthen the role of short-range communications in the future. Concepts such
as wireless social networks, Internet of things, car communications, home and office network-
ing, wireless grids and personal communications heavily rely on short-range communications
technology.
The Home Gateway Initiative (HGI) is a non-profit making organization which publishes guidelines,
requirements documents, white papers, vision papers, test plans and other documents concerning
broadband equipment and services which are deployed in the home.
In the first chapter, the topic of this book is classified into the area of pervasive com-
puting. Further, security and privacy in the scope of RFID technology is motivated
and the vision that guides the remainder of this book is introduced.
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
