This introduction takes a visionary look at ideal cognitive radios (CRs) that inte-
grate advanced software-defined radios (SDR) with CR techniques to arrive at
radios that learn to help their user using computer vision, high-performance
speech understanding, global positioning system (GPS) navigation, sophisticated
adaptive networking, adaptive physical layer radio waveforms, and a wide range
of machine learning processes.
The telecommunications industry is undoubtedly in a period of radical change with
the advent of mobile broadband radio access and the rapid convergence of Internet
and mobile services. Some of these changes have been enabled by a fundamental
shift in the underlying technologies; mobile networks are now increasingly based
on a pure Internet Protocol (IP) network architecture. Since the first edition of this
book was published in 2009, a multitude of connected devices from eBook readers
to smartphones and even Machine-to-Machine (M2M) technologies have all started
to benefit from mobile broadband. The sea change over the last few years is only the
beginning of a wave of new services that will fundamentally change our economy, our
society, and even our environment. The evolution towards mobile broadband is one of
the core underlying parts of this revolution and is the focus of this book.
Optical wireless communication is an emerging and dynamic research and development
area that has generated a vast number of interesting solutions to very complicated
communication challenges. For example, high data rate, high capacity and minimum
interference links for short-range communication for inter-building communication,
computer-to-computer communication, or sensor networks. At the opposite extreme is
a long-range link in the order of millions of kilometers in the new mission to Mars
and other solar system planets.
The first edition of this book was published in 1992. Nine years later it had become
clear that a second edition was required because of the rapidly changing nature of
telecommunication. In 1992, the Internet was in existence but it was not the
household word that it is in the year 2001. Cellular telephones were also in use
but they had not yet achieved the popularity that they enjoy today. In the current
edition, Chapter 1 has been revised to include a section on the Internet. Chapter 10 is
new and it covers the facsimile machine; I had overlooked this important tele-
communication device in the first edition. Chapter 11 is also new and it describes the
pager, the cordless telephone and the cellular telephone system. These are examples
of a growing trend in telecommunications to go ‘‘wireless’’.
The planarization technology of Chemical-Mechanical-Polishing (CMP), used for the manufacturing of multi-
level metal interconnects for high-density Integrated Circuits (IC), is also readily adaptable as an enabling technology
in MicroElectroMechanical Systems (MEMS) fabrication, particularly polysilicon surface micromachining. CMP not
only eases the design and manufacturability of MEMS devices by eliminating several photolithographic and film
issues generated by severe topography, but also enables far greater flexibility with process complexity and associated
designs. T
Electricity has been chosen as the most convenient and useful form of energy, due
to its ease of transportation over large distances and easy conversion to other
energy forms. The biggest inconvenience with electricity is that it cannot be stored
and must be utilized at the moment of generation. The storage of a large amount of
electrical energy is usually connected with its conversion to other types of energy,
which significantly reduces the efficiency of such processes. The aim of the power
system, often treated as the biggest and the most complex machine ever built, is to
deliver uninterruptible electric energy of demanded quality parameters to
consumers.
RFID networks are currently recognized as one a research area of priority. Research
activities related to RFID technology have been booming recently. A number of ongoing
projects are being funded in Europe, Asia, and North America. According to leading
market analysts, the development of the RFID market is projected to increase from
approximately $3 billion in 2005 to $25 billion in 2015. Several countries have dedicated
innovation programs to support and develop RFID systems and related technologies: the
RFID initiative in Taiwan, Ubiquitous Japan and the NSF SBIR program in the USA.
The EU has recently advertised its Strategic Research Roadmap concerning the Internet of
Things, which first of all refers to the RFID technology before being extended to commu-
nicating devices as in M2M (Machine to Machine). In this roadmap, several application
domains have been identified:
Have you ever looked at some gadget and wondered
how it really worked? Maybe it was a remote control
boat, the system that controls an elevator, a vending
machine, or an electronic toy? Or have you wanted
to create your own robot or electronic signals for a model railroad, or per-
haps you’d like to capture and analyze weather data over time? Where and
how do you start?
Although state of the art in many typical machine learning tasks, deep learning
algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount
of required computations and huge model sizes. Because of this, deep learning
applications on battery-constrained wearables have only been possible through
wireless connections with a resourceful cloud. This setup has several drawbacks.
First, there are privacy concerns. Cloud computing requires users to share their raw
data—images, video, locations, speech—with a remote system. Most users are not
willing to do this. Second, the cloud-setup requires users to be connected all the
time, which is unfeasible given current cellular coverage. Furthermore, real-time
applications require low latency connections, which cannot be guaranteed using
the current communication infrastructure. Finally, wireless connections are very
inefficient—requiringtoo much energyper transferredbit for real-time data transfer
on energy-constrained platforms.
The Industrial Revolution, which started in England around 1760, has replaced
human muscle power with the machine. Artificial intelligence (AI) aims at replacing
human intelligence with the machine. The work on artificial intelligence started in
the early 1950s, and the term itself was coined in 1956.
