Signal
processing does not necessarily require expensive computer
systems. The
Raspberry
Pi is a family of small but remarkably capable single-board
computers
that range in cost between $10 and $80. Most models are about
the size
of a deck of cards. Most have several USB ports,
general-purpose
input-output pins, HDMI port, Ethernet port, audio jack and
composite video, interfaces
for a video camera, micro-SD card slot for mass storage, a
graphics core,
Wireless LAN, and Bluetooth. You can get them with a bunch of
installed
software, including a version of the Linux operating system, a Web
browser, the
complete LibreOffice suite, Wolfram's
Mathematica (screenshot), several
programming languages and
various utilities. All of these are installed by default on
the
Raspberry Pi's operating system installer.
(The smallest and
cheapest models are ideal in situations where they might be
damaged or lost, as
in rocket or balloon-borne experiments). There are many starter
packs available that bundle all the required bits and
pieces.
You can easily
build a complete Windows 10 computer from a Raspberry Pi version
4 ($70) which comes built into a full keyboard; you need only a
USB-C 5 volt power
supply, a TV/monitor with an HDMI input, and a mouse (all of which
you might
find at a second-hand shop), and a mini SD card (8 to 16 Gbytes)
for mass
storage (which you can buy with all the software already
installed, or use a
blank one to which you can download the free software yourself).
For
educational purposes, the Pi has been used as a low-cost
alternative for school
computer labs, using its included software for both Office-type
applications (Writer word processor, Calc spreadsheet,
etc.),
and for programming instruction (Python (page 434),
C, C++, Java, Scratch, Ruby, etc). Single-board versions are ideal for “headless” applications
(meaning without a
monitor, keyboard, or mouse) where, after being set up, is only
accessed
remotely via WiFi or Bluetooth, using Putty
(for command-line UNIX-style access) or
using a graphical desktop sharing system such as RealVNC (free
for
Windows, Mac, IOS, and Android), which reproduces the entire
graphical
desktop on your local device, complete with a pop-up virtual
keyboard. Typical
applications are as a network file server, weather
station, media center or as a
networked security camera. It can
also share files with Windows.
There
are many laboratory and field applications,
especially
in combination with an Arduino micro-controller. But
if none of the
available Raspberry Pi models are sufficiently fast for your
signal-processing
needs, then you use the Pi only for data acquisition and transfer
the data to a
faster computer. (For MATLAB users, there is a MATLAB
Support
Package for Raspberry Pi Hardwarethat
supports this. Alternatively, you
could simply have the Raspberry Pi save data or results in a shared folder that is
accessed via Wi-Fi
from another computer).
Python
is the primary programming language that comes with the Raspberry
Pi. This
language is unlike the older languages traditionally used by
scientists, such
as Fortran or Pascal. Matlab programmers can use ChatGPT to
convert their code
to Python (page 442).
As a comparison, here is a real-time example of data acquisition
and plotting on a Raspberry
Pi, measuring its own temperature as a function of
time as it warms
up, using the commercially available add-on Sense Hat board with
a program written in Python
(click for real-time animation).If you do not have a Sense Hat, here's a modification
of
the same Python program that plots the running average of a
random
number, using the same autoscaling graphic technique, showing a
result that
gradually settles down closer and closer to the average the longer
you let it
run. This Matlab/Octave
script does the same thing at the same speed, but in
this particular
case the Matlab/Octave script length is substantially shorter.
(For a more
extensive comparison of Python to Matlab for several different
signal
processing tasks, see page 434).
This page is part
of "A Pragmatic Introduction to Signal
Processing", created and maintained by Prof. Tom O'Haver ,
Department of Chemistry and Biochemistry, The University of Maryland
at College Park. Comments, suggestions and questions should be
directed to Prof. O'Haver at toh@umd.edu. Updated July, 2024.
