Signal Processing - November 2017 - 6
about the name change proposal are
given next. I'm pleasantly surprised by
the number and depth of these comments. It shows that this topic is timely
and of great interest to our community.
Signal processing is "present in nearly
all the trendy mobile devices," according
to one respondent, yet it's not well understood by our peers in science, industry,
and the general public "is oblivious to
the concept." It's a fitting paradox for
signal processing, which is described
in the book Essentials of Digital Signal
Processing [1] as the "phantom technology because it is so pervasive and yet
not well understood."
Another participant agreed, writing,
"Signal processing is still a mystery to
many of our peers, and it does not adequately reflect the current activities."
Others pointed out that this dilemma hinders our ability to attract good students to
the field, negotiate promotions at universities and corporations, and "build a visible ecosystem" upon which individuals
could envision a career.
The diversity in theories and applications within our field can be viewed
as both a benefit and a hurdle. One person wrote that our branding challenges
"will get worse with signal processing
getting more diverse and intangible" as
the emphasis shifts from boards and circuits to software applications. Yet how
do we strive to be both more inclusive
and more succinct with our branding?
Some participants suggested adding
various qualifiers, the most popular of
which were data, data science, science,
signal science, engineering, and information processing.
The term data received a few favorables. With the increased emphasis and
publicity on data in recent years, I wonder whether more respondents would
have favored this term had the blog been
posted a couple of years later. Others
felt that while it's currently trendy and
may have increasing funding opportunities, it may have a short shelf life and it's
too broad and generic and too specific
to computer science, data processing
and "big data"-implying all are poorly
understood by the general public. There
are also educational differences to consider. One respondent pointed out that,
6
while signal processing necessitates
an advanced scientific education and
carefully conducted scientific protocols, it only takes a few courses in computer science and web programming to
become a "data scientist."
The addition of the term science also
received mixed reviews. As one respondent pointed out, "We are not scientists-we are engineers, and I for one am
damn proud to call myself an engineer.
Scientists take things apart; engineers
put things together. Not only are these
fields different, they are polar opposites." Someone else wrote, "Engineers
is what we are, and signal processing is
what we do." Several people opposed the
addition of the term engineering to our
name. One respondent pointed out that
engineering is also misunderstood by
the public as dealing with work that signal processors do not do, such as work
related to engines.
Adding the term information processing was the most popular alternative among the respondents, primarily
because it best conveyed the diversity
of our field and our goal to "strive to be
inclusive of all its members."
Yet other members called information processing redundant, saying that
you cannot process information if it does
not induce a signal; only signals that contain information can be processed and
"signal processing" allows for information in a signal to be available in a "convenient format." As one respondent put
it, "the word signal already indicates an
information-bearing phenomenon, and
signal processing already encompasses
the decoding/encoding of any kind of
information." However, I wish to add
here that besides "processing," much
of our work involves understanding and
learning about the systems we study.
Among the various proponents of
maintaining our current name, some
voiced concern that a name change would
dilute the brand name. The term signal
processing is well established, featured
in many journal titles, conference names,
and the majority of academic programs.
University electrical engineering departments teach subjects with signal processing in the title, and these courses are
often first-year courses offered to underIEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
|
graduates in electrical and computer
engineering, which piques the interest of
young students, and sets them on the path
to become signal processors.
Other supporters pointed out that the
SPS is already a well-respected brand
in the science and engineering community. "Let's keep the name and improve
our outreach and publicity efforts,"
wrote one respondent. Another member
agreed, writing, "Better outreach and
publicity will fix this issue." However
instead of "marketing the subtleties of
a denoising algorithm that optimizes
some supercool theoretical function,"
we should showcase the latest cuttingedge technologies. Another member
agreed, writing, "When people ask
'What is SP?' I say it is 'everything that
goes on inside a smartphone' and their
eyes suddenly light up."
"Whatever the new name of the
Society, I will still say that I am a signal
processing guy," wrote one commentator. Another wrote, the term "signal
must be kept since it represents the
human instinct to communicate since
the prehistoric age."
Reading the comments on this blog
has given me, a proponent of a name
change, much to think about. On one
hand, a name change, e.g., by adding
the term data science, would, at present, help us increase our visibility and
capture the interests of students, friends,
and other Societies, as well as the corporations and industries that provide
employment and help fund our research
and development. Our field has definitely evolved much further than processing signals measured by electronic
devices and grew to processing, understanding, and learning from data, irrespective to whether or not it is obtained
from physical or physiological processes.
Also, many of the concepts and theories
we have advanced have been abstracted
for use in a large number of applications.
On the other hand, data science has
much overlap with signal processing,
mainly nonparametric, high-dimensional statistical signal processing (which
involves big data and does not model the
process). Thus it could be strongly argued
that data science falls with the realm of
signal processing.
Table of Contents for the Digital Edition of Signal Processing - November 2017
Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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