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MathWorks News&Notes
The Magazine for the MATLAB® and Simulink® Community
Designing Radio
Astronomy Instruments
for the MeerKAT Array
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6
10
fe at u r e s
6
Accelerating the Design of Radio Astronomy Instruments
A DSP system developed by SKA South Africa can process 5 terabits of data per second
from 64 radio antennas in the MeerKAT array.
10
Deploying Standalone and Web-Based MATLAB Applications
to Improve the Steel Manufacturing Process
Using MATLAB data analysis and visualization applications, Ruukki Metals plant
operators complete analyses that used to take days in less than a minute.
14
Inverting the Robotics Classroom with a Massive Open
Online Course
Georgia Institute of Technology students learn control theory from MOOC videos and
spend class time developing controllers for Khepera III robots.
18
Adding Fun to First-Year Computer Programming Classes with
MATLAB, Arduino Microcontrollers, and Model Trains
By developing controllers for N scale trains, Ohio State University students experience
the joy of engineering while learning basic programming skills.
22
Using Modeling and Simulation to Test Designs
and Requirements
This workflow for building a component model from requirements and running
system-level simulations helps you catch and correct design errors before hardware is
available.
26
Systematic Fraud Detection Through Automated Data
Analytics in MATLAB
A hedge fund data example demonstrates an automated process for importing
heterogeneous data, constructing models, and training machine learning techniques.
22
26
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4
30
MATLAB and Simulink in the World: Medical devices
Cleve’s Corner: Variants of the QR algorithm
30
36
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about the cover
The cover shows the first of the 64
antennas that will make up the
MeerKAT radio telescope in South
Africa’s Karoo region. As a forerunner
to the Square Kilometre Array (SKA),
slated for completion in 2024, MeerKAT
will be used by scientists worldwide on
a range of research projects, including
observing neutral hydrogen gas and molecular hydrogen
levels in the early universe, the physics of enigmatic neutron
stars, and the nature and behavior of pulsars, dark matter,
and the cosmic web.
Production Staff
M. Arnold, D. Audette, K. Camerlin, L. Goodman,
L. Heske, A. Hutker, K. Kevorkian, L. Macdonald,
A. Pollack, M. Ruotolo, G. Quilop
Editorial Board
T. Andraczek, S. Gage, C. Hayhurst, M. Hirsch, S. Lehman,
D. Lluch, M. Maher, A. May, C. Moler, M. Mulligan,
L. Shure, J. Tung
Contributors and Reviewers
G. Bourdon, G. Campa, M. Carone, K. Cohan,
S. Cruickshank, J. Deinzer, M. DeMaio, A. Deoras,
S. deSouza, S. Eddins, M. Egerstedt, J. Eggers,
N. Fernandes, J. Friedman, W. Fullam, T. Gaudette,
S. Hiremath, M. Judin, F. Kapp, R. Kause, S. Kodial,
S. Kozola, T. Kush, T. Lennon, N. Levine, D. Mathis,
M. McBroom, I. McKenna, M. Michanek, A. Mishra,
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R. Shenoy, M. Stakenborg, S. Tandon, S. Tiwari,
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MATLAB AND SIMULINK IN THE WORLD
Medical Devices
In nearly 2000 medical device companies worldwide, engineers use MATLAB® and Simulink® to analyze image and
signal data, design controls and signal processing algorithms, implement their designs in software after thorough
verification and validation, and automatically generate reports for standards compliance.
enables rehabilitation engineers to change an FES controller in the
clinic and build FES control software. The Simulink based Universal
External Control Unit enables clinicians to develop and refine their
own FES applications up to 10 times faster than before.
mathworks.com/fes
VIRGINIA COMMONWEALTH UNIVERSITY
Simulating cardiovascular conditions using a fully
automated mock circulatory loop
Mock circulatory loops (MCLs) simulate the human circulatory
system to enable testing of cardiac assist devices. When conducting tests using an MCL, investigators must adjust the settings on
each component in the loop. VCU researchers modeled a fully automated MCL in which the mechanical pump, compliance chamber,
and peripheral resistance valve settings are dynamically adjusted via
microprocessors. The complete Simulink model of the system enables the user to apply parameter estimation routines to patient data
to determine the settings needed to replicate a wide range of cardiac
conditions and dynamics.
COCHLEAR LIMITED
mathworks.com/virginia-commonwealth
Streamlining development of cochlear implant sound
processing algorithms
CLEVELAND FES CENTER AND CASE WESTERN
RESERVE
Restoring movement to paralyzed limbs with functional
electrical stimulation
For individuals with neurological impairments, functional electrical
stimulation (FES) can help make real what was once only imagined:
the restoration of movement to paralyzed arms and legs. FES devices send electrical impulses to electrodes—implanted in the body,
worn on the skin, or operating through the skin—to produce and
control movement. Researchers at Case Western Reserve University
developed a flexible, configurable system technology platform that
4
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Noise reduction algorithms help cochlear implant recipients perceive speech in challenging acoustic settings. Engineers at Cochlear
use Simulink to model and simulate candidate algorithms. After
identifying a promising algorithm, the engineers generate C code
from the model using Simulink Coder™. The code is compiled and
deployed to a Speedgoat turnkey real-time system with Simulink
Real-Time™. In clinical validations, the Simulink Real-Time system
is linked via custom hardware to a recipient’s implant, enabling the
clinician to measure the performance of the algorithm using simulated real-life acoustic conditions, including live speech in quiet and
noisy environments.
mathworks.com/cochlear
developed a production BLDC motor controller that complies with
the IEC 62304 medical device software standard. They modeled
the motor, including its electrical and mechanical components, in
Simulink. They developed a Simulink controller model, and used
Stateflow® to model startup, shutdown, and error modes, as well as
user-selectable operating modes. After running closed-loop simulations of the plant and the controller, they generated more than 5000
lines of C code from their controller model, and compiled the code
for an ARM® Cortex®-M3 processor.
mathworks.com/itk
INFRAREDX
Accelerating FPGA development of an intravascular
imaging system
A patient with lipid core plaques (LCP) is vulnerable to coronary artery disease, the number one killer in developed countries.
Infraredx developed the only FDA-approved medical device for
LCP detection. The TVC Imaging System™ combines near-infrared
spectroscopy with intravascular ultrasound (IVUS) in a single coronary catheter to provide information about vessel composition and
structure. Infraredx engineers modeled and simulated the IVUS
signal and image processing algorithms in Simulink, and implemented the design on the Altera® Cyclone FPGA using VHDL® code
generated from the Simulink model with HDL Coder™. The generated HDL used the same number of multipliers as their handwritten
HDL while using 9% less logic and 3% less memory.
mathworks.com/infraredx
Sinoatrial
(SA) node
Coronary
Sinus (CS)
His Bundle
(HB)
High Right
Atrium (HRA)
Atrioventricular (AV) node
Right Ventricular
Apex (RVA)
High Right Atrium (HRA)
Right Ventricular Apex (RVA)
His Bundle (HB)
Coronary Sinus (CS)
UNIVERSITY OF PENNSYLVANIA
Closed-loop real-time testing of pacemakers with virtual
heart models
One-third of the more than 600,000 cardiac medical device recalls
that occurred between 1990 and 2000 were due to software problems. To enable early verification of pacemaker software, University
of Pennsylvania engineers developed a first-of-its-kind heart-ona-chip that can be configured to match a patient’s specific electrophysiological characteristics. The team built an electrophysiological
model of the heart in Simulink and Stateflow, and deployed multiple
versions of the heart model on an Altera FPGA using automatically
generated HDL code. Using the virtual heart model running on the
FPGA in real time they simulated several closed-loop scenarios, including pacemaker-mediated tachycardia and atrial flutter, as well as
failure conditions, such as a displaced pacemaker lead.
mathworks.com/upenn
ITK ENGINEERING
Learn More
Developing IEC 62304–compliant controller software for
a dental drill motor
Sensorless brushless DC (BLDC) motors cause less abrasion than
brushed motors, and are more reliable, quieter, and easier to maintain, but they require complex control algorithms. ITK Engineering
User Stories
mathworks.com/user-stories
Technical Articles
mathworks.com/technical-articles
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
5
Accelerating the Design of Radio Astronomy
Instruments in South Africa and Worldwide
with Simulink
By Francois Kapp, SKA South Africa
When it is complete, the MeerKAT array in South Africa’s Karoo
region will include 64 antenna dishes, making it the largest and most sensitive radio telescope in the southern hemisphere. As a forerunner to the
Square Kilometre Array (SKA), slated for completion in 2024, MeerKAT will
be used by scientists worldwide on a range of research projects, including
observing neutral hydrogen gas and molecular hydrogen levels in the early
universe; the physics of enigmatic neutron stars; and the nature and behavior
of pulsars, dark matter, and the cosmic web.
E
ach of these research initiatives depends
on the ability to process the massive amounts
of data collected by the radio antennas 24
hours a day. To meet this need, the Digital
Back-End (DBE) team at SKA South Africa is
developing a digital signal processing (DSP)
system capable of handling 5 terabits of data
per second. This system is designed with a
Simulink® library that enables our team and
fellow researchers to rapidly design and deploy radio astronomy instrumentation on
modular, reusable hardware using ModelBased Design.
Radio Astronomy Instrument
Design Challenges
Early radio astronomy relied on large, singledish antennas up to 300 meters in diameter
that focused on relatively small areas of the
6
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
sky. Modern antenna arrays, which are constructed using multiple small dishes, offer
a wider field of view and greater flexibility.
Moving the dishes farther apart increases the
telescope’s resolution and enables it to detect
smaller objects. Moving the dishes closer together decreases the resolution but expands
the field of view.
The challenge is to correlate data received
from all the antennas in the array. To determine the position of an object in the sky, the
DSP system must calculate the phase difference between radio signals arriving at each
pair of antennas in the array. Because data
from all the antennas must be cross-correlated (that is, each antenna’s signal must be
correlated with every other antenna’s signal),
the processing task is an N2 problem: When
we double the number of antennas, we have
to quadruple the processing power of the
DSP.
Compounding the challenge is the continuous nature of the data stream. Optical
astronomy data can be collected at night,
stored, and then processed the following day.
Radio astronomy data, by contrast, is collected 24 hours a day, and must be processed
as it is received.
Moving from Specialized
Hardware to Reusable Libraries
and Hardware Components
Specialized hardware based on ASICs or
FPGAs is needed to process the raw data received from the antenna array in real time. In
the past, developing this hardware required a
team of experienced hardware engineers and
years of effort. Today, scientists with little or
Artist’s impression of the MeerKAT array in the Karoo region of South
Africa. The SKA South Africa team had to develop a DSP system capable of correlating data as it is received from all the antennas in the
array. Because data from all the antennas must be cross-correlated,
when we double the number of antennas we have to quadruple the
processing power of the DSP.
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
7
Figur e 1. A 4-input wideband FFT Simulink library block.
no hardware design experience can develop
their own instrumentation in months using
Simulink, a library of drag-and-drop components, and standardized Reconfigurable
Open Architecture Computing Hardware
(ROACH) boards.
The Simulink library and ROACH boards
form the basis of an open-source design environment for radio astronomy instrumentation maintained by the Collaboration for
Astronomy Signal Processing and Electronics Research (CASPER). SKA South Africa
has been a key contributor to the CASPER
library during the development of the DSP
systems for MeerKAT.
The CASPER library consists of components commonly used in radio astronomy,
including mixers, oscillators, down-converters, filters, matrix transforms, accumulators,
adders, and wideband fast Fourier transform
(FFT) blocks (Figure 1).
The blocks are parameterized and configured automatically by a MATLAB® script
based on settings specified by the user (Figure 2). For example, to create a five-input
parallel adder block, the script automatically
inserts the required number of basic adders
and delays, connects them, and packages
them as a self-contained block for use in a
larger design.
Scientists assemble signal processing systems from CASPER library blocks and simulate their design in Simulink, using recorded
radio signal data as input. After verifying
8
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
their design via simulation, they use HDL
Coder™ to generate HDL or Verilog® code
from the Simulink model for deployment on
the ROACH board’s Virtex® FPGA. In addition to the FPGA, the ROACH board includes analog-to-digital converters (ADCs),
a 10-gigabit Ethernet interface, and other
hardware components frequently required
in radio astronomy instruments (Figure 3).
Initially developed with blocks from the
Xilinx® System Generator for DSP™ blockset,
the CASPER library made it easy for scientists to generate HDL code for their designs
and target Xilinx FPGAs. At SKA South Africa, a project is under way to migrate the
library to native Simulink blocks, which
will enable the use of HDL Coder to generate the HDL code, and partition designs so
that functional blocks can be targeted not
only to FPGAs but also to ASICs or ARM®
processors. HDL Coder also opens the possibility of expanding CASPER beyond radio
astronomy and into other disciplines that
require similar real-time signal processing
capabilities.
From KAT-7 to MeerKAT
The seven-dish KAT-7 array is an engineering prototype for the MeerKAT array. KAT-7
is the world’s first radio telescope array with
fiberglass dishes. It is located in the Karoo
region of South Africa, a sparsely populated
area with low levels of radio interference from
human activity, where MeerKAT and parts of
Figur e 2. CASPER Library adder tree configuration interface. Users can specify the number
of inputs, latency, and other implementation
settings.
the Square Kilometre Array will also be built
(Figure 4).
We developed the KAT-7 DSP system in
Simulink using the CASPER library and
deployed it to 16 ROACH boards. While its
primary purpose was to serve as a proof-ofconcept for MeerKAT, KAT-7 is a valuable
telescope in its own right, and has already
produced images of Centaurus A, a galaxy
14 million light years away.
We are currently using Simulink and the
CASPER library to design the signal processing systems for the MeerKAT array,
which is slated for completion in 2016. With
64 dishes, MeerKAT has almost 10 times as
many antennas as KAT-7. This means that
it will require roughly 100 times the signal
processing to handle the data, which will be
coming in at more than 5 terabits per second.
A key advantage of using Simulink and
Model-Based Design for this effort is that
designs will be easily retargeted to the next
generation of ROACH boards when they
become available. This retargeting enables
us to take advantage of the more powerful
library is not only accelerating the development of radio astronomy instruments for
MeerKAT and SKA; it is also helping researchers across the globe. Prior to CASPER,
it was commonplace for the development of
new instruments to take five years or more;
with CASPER we have seen groups develop
eight instruments in less than two years.
A principal benefit of using Simulink and
CASPER is that beginners can become productive designers very quickly. We run workshops on the use of Simulink and CASPER.
Attendees are typically hardware design novices, but after a week, they know enough to
design their own instruments by dragging
and dropping blocks in Simulink and deploying them to ROACH hardware with a single
click. In fact, each year some of the trainees
develop a new, working instrument during
the weeklong training course. That kind of
productivity is driving interest and enthusiasm in our field, and it is accelerating advances in radio astronomy not only in South
Africa, but worldwide. ■
Figur e 3. A ROACH-II board.
Figur e 4. Aerial view of the KAT-7 array.
FPGAs that we expect to be available in the
years to come. We anticipate using about
200 to 300 next-generation ROACH boards
for MeerKAT.
The Square Kilometre Array and
Beyond
The Square Kilometre Array will have between 3000 and 4000 antennas, each about 15
meters in diameter. Combined, the dishes will
have a square kilometer of surface area, making SKA the largest and most powerful radio
astronomy telescope in the world—about 100
times more powerful than its closest competitor. SKA dishes will produce data at a rate
equivalent to roughly 10 times the world’s
current Internet traffic, and the system will
be so sensitive that it will be able to detect
signals equivalent in strength to airport radar, on a planet 50 light-years away. Although
construction will not begin until 2017, early
signal processing design work has already begun, and the team has submitted a proposal
to design the SKA’s central processing system
using Simulink and the CASPER library.
Our work with Simulink and the CASPER
Learn More
Square Kilometre Array
ska.ac.za
Developing Radio Astronomy Instruments with
Simulink Libraries
mathworks.com/radio-astronomy
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
9
Deploying Standalone and Web-Based
MATLAB Applications to Improve the
Steel Manufacturing Process
By Mika Judin, Ruukki
The mass production of high-quality steel products requires a
combination of time-tested manufacturing practices and modern technology.
As steel coils pass through the rolling, galvanizing, color coating, and other
lines in a steel manufacturing plant, operators must set oven temperatures,
line speeds, and alignments based on the characteristics of each coil and the
desired thickness and flatness profile. Failure to properly set up a line results
in excess scrap. It can also damage the ovens and cause the line to be shut
down for several days.
A
t Ruukki Metals, we built and deployed a web application with MATLAB®
that enables operators to select and apply the
proper settings throughout the steel manufacturing process. We built a second, standalone
MATLAB application that our engineers use
to aggregate and analyze production metrics
from multiple databases, track individual
coils, and refine our process.
Since deploying these applications, we have
had a much more consistent and efficient
process, with less scrap, improved flatness,
and a shorter off-gauge length. For example,
in the temper mill, the off-gauge length—the
amount of a coil that does not achieve the target thickness—has been reduced from several
meters to 50 centimeters or less. Key to these
improvements were regular optimizations of
setup calculation parameters, and data visu-
10
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
alization in MATLAB. The power of these
MATLAB visualizations for detecting deviant
coils cannot be exaggerated.
Identifying Potential Process
Improvements
Line speeds and temperatures must be adjusted to keep the thickness and flatness of
each coil within required tolerances. Before
MATLAB based web applications were available, our operators relied on their own experience, personal notes, and judgment for this
work. With multiple shifts running each day,
this practice led to inconsistent results.
Once coils had been processed, it was difficult and time-consuming for engineers to
determine what settings had been used for
any particular coil or set of coils. For example,
to check the thickness at the cold-rolling mill,
the output after galvanizing, and the transverse thickness profile, they typically spent
days collecting the necessary data, processing
it, and producing the plots needed to understand the results.
Analyzing Big Data with MATLAB
and Neural Networks
At the heart of our new process is a set of
data warehouses that we use to store and
access information about the coils as they
pass through the plant. A Microsoft® SQL
based data warehouse stores thickness tolerances, dimensions, the raw material class for
each coil, and the coil’s intended customer.
A Wonderware® Historian data warehouse
stores time-series data for the coil’s thickness
and flatness and other process measurements.
An Oracle® based data warehouse stores
Cold-rolled steel coils. Ruukki Metals processes 60,000 coils annually.
To ensure quality, engineers use a MATLAB application to aggregate
and analyze up to 4000 different measurements for each coil.
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
11
defects or anomalies detected and test results
in a coil. All together, up to 4000 different
measurements may be stored for each of the
60,000 coils processed annually.
With MATLAB and Database Toolbox™,
we developed an application that retrieves
data from each database, merges it in a separate Microsoft Access database, and creates
documentation as needed. When a new coil
is about to be processed in the line, this application analyzes the merged and stored data
to calculate oven temperatures and other parameters used to set up equipment. In the galvanneal process, for example, the application
uses a neural network created with Neural
Network Toolbox™ to calculate setup values.
We relied on Neural Network Toolbox to
implement a number of other key application features. We used self-organizing maps
to classify coils by zinc mass, iron percentage,
and flatness (Figure 1).
We also used Neural Network Toolbox to
create a neural model. The model generates
roll gap predictions based on finite element
methods (FEM) (Figure 2).
In total, we created more than 30 different
MATLAB applications for analyzing, visualizing, and exporting data for coils in the production chain. This data includes thickness,
flatness, transverse profiles, raw material
quality, and other characteristics (Figure 3).
Deploying Applications
We used MATLAB Compiler™ to create a
desktop application that provides easy access
to the data analysis and visualization applications in MATLAB that we had developed.
Thanks to our use of JDBC drivers, no ODBC
database connections were needed, and we
did not have to use database wizards to create a database connection for the applications.
Engineers can install and run this application from any PC without having to install
MATLAB. Currently, more than 20 engineers
are using it to gain insights into how our production chain is working today and how it
might be improved in the future. Using this
12
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Figur e 1. A self-organizing map created with Neural Network Toolbox. Cluster 83 in the upper left
shows that there were 469 coils with similar flatness characteristics.
Figur e 2. Plots showing roll gap profile predictions.
application, analyses that took days to complete manually can be completed in less than
a minute.
We also created a web-based application,
accessible via any web browser on our net-
work, that line operators can use to view the
data they need to monitor and set up the line
(Figure 4). To build this .NET application, we
packaged our MATLAB code as a DLL using
MATLAB Builder™ NE. The DLL retrieves
information from our database and creates
plots that are sent as bitstreams to the main
application, hosted on a Microsoft Internet
Information Services (IIS) web server.
Using this application, our operators are
prepared to make the necessary adjustments
before each new coil enters a line. Since deploying this application and the standalone
executable that we created with MATLAB
and MATLAB Compiler, we have seen fewer
misalignments, less scrap, and significant increases in efficiency and consistency at the
plant.
Recently, we used MATLAB to model the
bending process during cold rolling in the
temper and tandem mill, and we performed
simulations that have enabled us to improve
the flatness of coils coming out of this process.
MATLAB provides a powerful tool for accessing FEM models like COMSOL Multiphysics®.
We have created several applications that use
a COMSOL-MATLAB link that enables us to
input values to an FEM model and visualize
results from COMSOL. ■
Figur e 3. Zinc mass visualization, created in MATLAB.
Learn More
HKM Optimizes Just-in-Time Steel Manufacturing
Schedule
mathworks.com/hkm
Example: Thickness Control for a Steel Beam
mathworks.com/thickness-control
Figur e 4. The web application, built with components created in MATLAB Builder NE, running in
a browser.
Webinar: Application Deployment with MATLAB
22:58
mathworks.com/wbnr-81715
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
13
Inverting the Robotics Classroom with a
Massive Open Online Course
By Magnus Egerstedt, Georgia Institute of Technology
Electrical and computer engineering students at Georgia
Institute of Technology learn embedded control design in ECE 4555: Embedded and Hybrid Control Systems. While the course covers embedded control
systems of all types, it places a heavy emphasis on mobile robots. Robotics
helps bridge the gap between theory and practice in a way that engages and
motivates students like little else.
I
n the most recent version of the course,
students had more time to work with robots
in class, thanks to an inverted (or flipped)
classroom and a massive open online course
(MOOC). More than 40,000 students participated in the MOOC worldwide, including
the 30 third- and fourth-year students in ECE
4555 at Georgia Tech. The videotaped lectures and online assignments in the MOOC
enabled me to flip the on-campus classroom:
Students learned theory from MOOC videos,
and spent classroom time developing controllers in MATLAB® and working with actual
robots.
The results were outstanding. In all my
years of teaching, I’ve never seen such a high
level of energy and enthusiasm—even though
the flipped classroom demanded more time
than a typical three-credit class. In class sur-
14
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
veys, students gave the course an average of
4.9 points out of 5, and although the MOOC
was far more advanced than most online
courses, more than 5000 students completed
it, far exceeding the average retention rate.
Using a MOOC to Invert the
Classroom
When I started teaching ECE 4555 I focused
on general embedded control, but soon
moved into robotics as the primary application domain. The first time that we taught the
course with actual robots, I was so pleased
with the results that I decided to add more robotics—and a much richer set of problems. I
found, however, that as the assignments grew
more complex, the students stopped being
systematic in their design approach. I was
faced with a dilemma. I wanted lots of robot-
ics in the classroom, but I still needed time to
teach the theory and make it both meaningful
and accessible.
I flipped the classroom so that the students could learn the background material
and theory outside class and spend class time
working with me and my teaching assistant
to develop control systems and test them on
Khepera III robots (Figure 1).
Once I had decided to use an inverted classroom, the next question was, “Where will the
material come from?” At that point, I opted to
jump in with both feet and create a MOOC,
Control of Mobile Robots. Since I would be
depending on the MOOC to teach my oncampus students, I could not water down the
material. I developed seven weekly modules,
each with eight sublectures and a homework
assignment covering topics such as linear
Students in ECE 455: Embedded and
Hybrid Control Systems put theory into
practice in their final control design
projects. Among the most impressive
designs this year was a simultaneous
localization and mapping (SLAM) robot
that automatically builds a map of its
environment as it explores.
Figur e 1. Khepera robots, programmed by students in ECE 4555.
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
15
systems, control design, hybrid systems, and
navigation. To make the MOOC broadly accessible, I used MATLAB to present control
design examples in the lectures and as extra
programming assignments, but did not require students to use MATLAB to complete
assignments.
Introducing the Sim.I.am Simulator
In ECE 4555, I faced a challenge common to
many similar undergraduate courses: I had far
more students than robots. Even working in
groups, students had to spend much of their
limited time with their robot debugging the
control program instead of optimizing and
enhancing it. The solution was to build a robot simulator in MATLAB. Developed by a
teaching assistant, the Sim.I.am simulator
enables students to write control programs in
MATLAB and debug them in a virtual world
(Figure 2). The same MATLAB code can then
be used without modification to control a
Khepera robot in the lab. The connection between simulation and actual robots is made
possible by a thin TCP/IP server on the robots
that enables the simulator to send the control
signals from the controllers to the Khepera
robots instead of their simulated counterparts.
Sim.I.am was instrumental to the success
of the MOOC, as well. Online students who
had MATLAB used the simulator to complete
optional programming exercises. Students
without access to MATLAB could download
a standalone version of the simulator created
with MATLAB Compiler™. While the students
could not develop their own control programs
using this version, they could experiment by
setting controller gains to tune the system and
see how their choices affected the behavior of
the simulated robot.
Sim.I.am is designed to be adaptable to a variety of use cases. It can easily be modified to
support other types of differential drive robots.
It has a Simulink® interface that can be used as
an alternative to programming in MATLAB. In
addition to relying on the simulator as a teaching tool, I use it in my own research.
16
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Welcome to Sim.I.am, a robot simulator.
This is Sim the Second, your companion for control theory and robotics.
The simulator is maintained by the GRITSLab at
http://gritslab.gatech.edu/projects/robot-simulator
1. Start the demo by clicking the play button.
2. Use the mouse to pan and zoom.
3. Double click anywhere on the grid to send the robot to that location.
4. Select the robot to follow it.
5. If your robot crashes, press the rewind button.
Figur e 2. The Sim.I.am robot simulator.
Programming Robots in MATLAB
After two weeks of in-class lessons on embedded controls, my on-campus students
switched to the MOOC videos to learn new
concepts. They came the first class session
each week prepared to apply those concepts
by developing MATLAB programs to run in
Sim.I.am. In the second session, they tested
and refined the programs on the Khepera robots. I did not have to spend class time teaching MATLAB because they had all learned
programming with MATLAB in CS 1371:
Computing for Engineers, a required course
for all engineering majors at Georgia Tech.
For their first in-class assignment, students used MATLAB to program the robot to
steer from a starting point to a goal using the
movement of the robot’s actuators to estimate
its change in position. They created a single
input, single output proportional integral derivative (PID) design, tuning it to ensure that
the robot did not oscillate as it drove, and that
the actuators did not saturate. While actuator
saturation is simulated by Sim.I.am, effects
such as skidding and slippage are not. As a result, students learned to cope with real-world
effects as they worked to improve algorithms
that performed well in the simulator but less
so on the robot.
In the second assignment, they had to
make the robot move and turn away from any
obstacle that it encountered. This assignment
required them to write a MATLAB program
that controlled the robot based on data acquired from the robot’s sensors.
Next, the students had to combine what
they had learned in the first two assignments
to design a controller that could get a robot to
its goal while avoiding obstacles. For this objective, classic control design techniques are
no longer sufficient; the system must switch
between different controllers. In the following weeks, we added cul-de-sacs and multiple
objects to the robots’ environment. To enable
the robots to navigate these more complex
environments, students implemented sliding
mode controls. They used Control System
Toolbox™ to explore and analyze design ideas
Raw Data
Image with Threshold
X Coordinate (m)
Hough Transform
Lines Detected
Figur e 3. A map generated by a student-developed
SLAM algorithm.
before implementing them in code.
When the seven-week MOOC ended, I had
planned to spend the four weeks remaining in
the Georgia Tech semester lecturing on supervisory control topics. However, the students
were so engaged and excited to work with the
robots that I opted instead to use that time
to expand their final design projects. Each
student group came up with their own project idea (subject to instructor approval), and
implemented it using MATLAB, Sim.I.am,
and the Khepera robots. Among the most
impressive were a program that moved multiple robots in formation and a simultaneous
localization and mapping (SLAM) design that
automatically built a map of its environment
as it explored (Figure 3).
Gaps Bridged and Lessons Learned
Practicing engineers apply theory to make
something happen in the real world. In ECE
4555, the students learned to do the same.
They also learned several widely applicable
engineering skills that transcend embedded controller design. Like practicing engineers, they argued about control designs and
learned to identify the best ones.
In many group efforts, one student does
all the work. This was not the case in ECE
4555. On many teams every student took the
initiative to develop a solution, and then the
team picked one to develop and optimize.
Some teams were initially inclined to hack
together a solution. They started fast, but
soon discovered the value of slowing down
to think through a design before rushing to
implement it. They also learned how to assess
and manage real-world effects. Such effects
are easily dismissed when you are only doing simulations, and not actually smelling the
burnt capacitor.
I’m now a firm believer in flipped classrooms, and the MOOC was invaluable to
making ours a huge success. Other institu-
tions have recognized the advantages as well;
next year the University of Hawaii will flip an
upper-level design course using Control of
Mobile Robots.
On campus, I plan to incorporate the
Simulink interface to Sim.I.am. Further improvements may include adapting it to work
on a less expensive robotics platform. ■
Learn More
Download: Sim.I.am app
mathworks.com/fx-40860
Sim.I.am Simulator
mathworks.com/blogs/sim-i-am
Motivating First-Year UC Berkeley Students
to Learn Programming with a Virtual Robot
Tournament
mathworks.com/learn-with-robot
Applied Project-Based Learning: Building
Applications for Low-Cost Hardware
mathworks.com/building-applications
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
17
Adding Fun to First-Year Computer
Programming Classes with MATLAB,
Arduino Microcontrollers, and Model Trains
By Lowell Toms and Dustin West, Ohio State University
Ohio State University’s First-Year Engineering course sequence is
designed to give incoming students insight into most of the engineering disciplines offered at the university. The challenge is to keep the students interested and prevent that first year from turning into a painful slog through the
core science and math courses. How do we solve that challenge? We let the
students play with train sets while teaching them basic programming skills
using MATLAB®.
T
he 1700 students who take ENGR 1181:
Fundamentals of Engineering I each year not
only learn engineering concepts and the basics
of computer programming in MATLAB; they
also complete a final project in which they develop a controller for an N scale train set using
MATLAB and an Arduino® microcontroller.
This project enables students to demonstrate
what they’ve learned. It also gives them their
first opportunity to experience the joy of engineering by seeing the results of their efforts
come to life in the lab. We frequently see students jumping and cheering as they watch
their control algorithms work for the first time.
Learning Programming with MATLAB
Before they tackle the train project, students
learn measurement, graphing, data analysis, and other core engineering concepts in
parallel with the basics of programming.
18
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Lab assignments in MATLAB reinforce the
programming lessons, which cover arrays,
strings, loops, logical expressions, conditionals, and other constructs.
Most students come to the course with little
or no programming experience. We have found
that MATLAB is an excellent first language to
learn. MATLAB is more interactive than C and
similar languages, there is no need to compile,
and there are far fewer low-level details for the
students to master. Once they understand basic programming concepts in MATLAB, students learn to apply them in other languages
quite rapidly. MATLAB is also ideal for ENGR
1181 because it is used across all engineering
disciplines at Ohio State.
Controlling Trains with MATLAB
and Arduino Hardware
Students begin the train project during the
10th week of the course. Their goal is to develop a MATLAB controller that works with an
Arduino microcontroller to interact with
a train as it travels around a circular track.
Break-beam sensors separate the track into a
“city” section and a “country” section (Figure
1). As the train enters the city, the MATLAB
code must reduce the train speed, start the
road crossing flashers, and then drop the crossing gate after a one-second delay. When the
train leaves the city and enters the country, the
MATLAB code must increase the train speed,
turn off the flashers, and raise the gate.
Working in groups of four, the students use
MATLAB and the MATLAB Support Package for Arduino Hardware to write a program
that communicates with an Arduino Uno via
a serial port. The program checks the status
of the break-beam sensors via the Arduino’s
digital input, and uses the Arduino’s digital
Working in groups of four, students
write a MATLAB program that
reduces the train speed, starts the road
crossing flashers, and drops or raises
the crossing gate.
Figur e 1. N scale train setup in the lab.
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
19
%Start a timer
tic;
%You know you are inside the approach gate so now you will
%be checking the departure gate to see if you have passed
dep = a.analogRead(departure);
dep = a.analogRead(departure);
dep = a.analogRead(departure);
%This will run until you pass the gate.
while dep < 300
%Turn the crossing gate lights on to alternate
%*****NOTE****
%Extend this through about 6 or 7 seconds. If it takes
%longer than 4.5 seconds for your train to pass between the
%approach and departure gates, your lights won’t blink after
%4.5 seconds.
if ((toc-floor(toc)) < 0.5)
a.digitalWrite(rLED,1)
a.digitalWrite(lLED,0)
else
a.digitalWrite(rLED,0)
a.digitalWrite(lLED,1)
end
%Lower the crossing gate to horizontal 1.5 seconds
%after the train goes through the approach sensor
if (toc>1.5)
a.servoWrite(1,170)
end
Figur e 2. Student code using tic() and toc() to implement a non-blocking delay.
output to flash the LEDs and operate a servo
motor for raising and lowering the gate.
Over the course of three lab sessions, the
students implement their MATLAB control
algorithms. These typically consist of an endless polling loop with conditionals to check
the status of sensors and logic to manage the
servos and LEDs.
For many students, the most challenging aspect of the project is implementing a
nonblocking delay, needed to flash the LEDs
and lower the gate after one second. Students
discover that they cannot use the MATLAB
20
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
pause() function because the algorithm
may miss sensor input while the pause is executing. Instead, they learn to use MATLAB
tic() and toc() functions to time the delay
without interrupting the polling loop (Figure 2).
Building a Simulator for Debugging
and Independent Work
Scheduling lab time for 1700 students was a
significant challenge. Our lab has room for
18 tables, each capable of holding one train
setup. With four-member teams, we could
Figur e 3. The MATLAB train set simulator.
accommodate just 72 students at a time. Long
waits for lab time were made even longer
because students had to do all their debugging on an actual train set in the lab. Another
concern was that one member of each team—
usually someone with prior programming
experience—tended to do most of the work
while the other three watched.
To address these challenges, two graduate students developed a train simulator in
MATLAB that emulates the train setups
in the lab (Figure 3). The simulator uses a
MATLAB figure window to draw the track,
train, break-beam sensors, and gate. Students
can use the simulator to debug their control
algorithm code and then use the same code
on the actual train set. Teams then spend lab
time optimizing their algorithms instead of
debugging, greatly reducing the amount of
time each team needs with the actual trains.
Further, we can now give assignments for
students to complete independently using the
simulator, ensuring that no single programmer does all the work.
MATLAB in the Second Semester
and Beyond
Engineering students continue to use MATLAB
in ENGR 1182: Fundamentals of Engineering 2.
In this course, the students must design and
build an advanced energy vehicle that uses
as little power as possible as it travels around
a monorail track. Students analyze power
consumption in MATLAB using voltage and
Figur e 5. DAQ setup for measuring the velocity of a dropped ball using MATLAB and Data
Acquisition Toolbox.
Figur e 4. A monorail vehicle and MATLAB interface used to plot power consumption, distance
traveled, velocity, kinetic energy, and energy efficiency as a function of time.
current measurements recorded by an Arduino Nano installed on the vehicles (Figure 4).
In upper-level courses and in their finalyear capstone projects, students build upon
the skills they’ve developed in MATLAB
throughout the First-Year Engineering program. For example, in ENGR 2167: Data
Acquisition with MATLAB, students explore
the use of data acquisition (DAQ) devices,
including the USB-6009 and myDAQ from
National Instruments. During one exercise, a
ball is dropped through a vertical tube lined
with break-beam sensors (Figure 5). Using
a DAQ device in session mode with Data
Acquisition Toolbox™, we collect data from
the sensors at 20,000 samples per second. We
use MATLAB to compute the ball’s velocity as
it falls, and compare the result to the velocity of a ball dropping in a vacuum, calculated
from Newton’s laws.
High-Fives and Other Positive
Feedback
By the end of their first year, the students
have a solid grasp of data analysis and programming with MATLAB and understand
how vectors, indexing, loops, and conditionals work. The train project has led not only to
high-fives in the lab, but also to very favorable
course reviews from our students.
Since the adoption of the First-Year Engineering course sequence with MATLAB, a
higher percentage of students are opting to
continue in the engineering program. We believe that the increased retention rate is partly
due to the students having experienced the
satisfaction of facing and overcoming a genuine engineering challenge in their first year. ■
Learn More
Teaching Computer Programming to First-Year
Undergraduates with a MATLAB Based Robot
Simulator
mathworks.com/robot-simulator
Classroom Resources: Programming and
Computer Science
mathworks.com/programming-computer-science
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
21
Using Modeling and Simulation to
Test Designs and Requirements
By Michael Carone, MathWorks
Modeling is an efficient and cost-effective way to represent a
real-world system. A model can represent key aspects of the system, including
the underlying requirements, the components, and how those components
communicate with one another. The model can be simulated, enabling designers to test designs before hardware is available, or to test conditions that
are either difficult or expensive to replicate in the real world. Iterating between modeling and simulation can improve the quality of the system design
early, reducing the number of errors found later in the design process.
D
espite these advantages, designers who
heavily rely on hand coding do not always
take full advantage of modeling and simulation. Setting up tests can be difficult and
time-consuming, and when separate tools are
used for each domain, it can be challenging to
obtain a system-level view of the design. As
a result, defects that could have been found
in the modeling and simulation phase are often found during the implementation phase,
when defects are more expensive to fix.
These issues are addressed in Simulink®,
a platform for modeling and simulation.
Simulink supports not only multidomain
modeling but also simulation, with its own
set of ordinary differential equation (ODE)
solvers. A fundamental advantage of using
Simulink is that you can represent different
domains, including control systems, state
22
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
machines, and environmental models, in
one model, and then run simulations within
Simulink to verify that the model is built correctly. As the simulation runs, you have access
to simulation analysis capabilities, such as
data displays, state animation, and conditional breakpoints. After the simulation is completed, you can analyze any logged data with
MATLAB® scripts and visualization tools.
In this article, we describe a workflow for
building a component model from requirements, simulating and testing that component model, and then connecting it to a
system-level model for further simulation
and testing. To illustrate this workflow we
will build and test the fault detection, isolation, and recovery (FDIR) component of the
HL-20, a re-entry vehicle designed by NASA
to complement the Space Shuttle orbiter. We
will connect our component to a system-level
model that includes environmental models,
flight controls, and guidance, navigation, and
controls (GN&C) systems, and then simulate
the system-level model to validate its behavior.
The model used in this example is available
in Aerospace Blockset™.
Building the Component Model
from Requirements
The first step is to model the fault management logic of the actuator system. The requirements document specifies five possible
modes for the actuator: passive, standby, active, isolated, and off. For simplicity, we will
consider the first four modes only. We represent these modes by adding four states to a
Stateflow® state diagram (Figure 1).
Figur e 1. Stateflow diagram showing actuator modes represented by states.
Figur e 2. Requirements linked to specific parts of the state diagram.
Next, we need to determine how the
system will transition from one state (or
mode) to another. Using the information
provided in the requirements document,
we add transitions connecting the states,
and specify which conditions need to be
satisfied for the system to switch states. We
also group the Passive, Active, and Standby
states together in a superstate, since they
all transition to the Isolated state under the
same condition. This hierarchical modeling
technique helps us to model complex logic
in a simple visual form.
We continue to build the model, connecting each element to a specific system requirement (Figure 2). Later we will be able
to trace our model back to the requirements
document to explain why a design choice
was made.
Once we have built up the logic for the left
inner actuator, we can reuse this design for
the right inner actuator, since the structure is
exactly the same. The only elements that need
to be changed are the conditions that guard
each transition, as described in the requirements document.
Testing the Component Through
Simulation
Now that the component is partially built, we
are ready to run simulations to verify that it
is behaving correctly. To do this, we set up a
simple test harness that feeds input signals
into the component using a combination of
switch and constant blocks.
With Simulink and Stateflow we can start
the simulation without having to manually
define variables. When we press the Play button, a dialog box appears showing which variables need to be defined before the simulation
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
23
Figur e 3. State animation showing a defect in the design.
can run. When we press OK, those variables
are automatically created.
As the simulation runs, the state diagram
animates, letting us know which state is active
at any given time and how the system is transitioning from state to state. Ad-hoc testing by
switching input signals on or off reveals a flaw
in the design (Figure 3). When the left inner
actuator is activated, the right inner actuator
should be activated, as well. The fact that we
were able to set the input conditions so that
this did not happen indicates that there is a
flaw in our design.
It turns out that the condition on the transition from Active to Standby contains a defect. Since we have linked each condition to
a requirement, we can trace that condition to
the underlying requirement and verify that
the defect originated from the requirements
document and not from the design (Figure 4).
The last line should read “or the left inner
actuator is in the Active mode.”
We fix the language in the requirements
document, revise the condition, simulate the
model again, and verify that the system now
behaves correctly in response to the input
signals.
Testing the System with the New
Component
Now that the FDIR component has been
tested on its own, we are ready to test it in
24
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Figur e 4.
Design defect traced
back to requirements
document.
Figur e 5. System-level model showing three referenced component models: flight control system,
FDIR logic, and guidance system.
the system-level model. We bring the component into the model as a Model block
named FDIR_application. Once the block
is integrated into the system model, we can
continue to work on it independently from
the rest of the system using the model referencing capability in Simulink (Figure 5).
We simulate the system-level model and
visualize the behavior of the component in
the state diagram, as well as the behavior
Figur e 6. Simulation of the multidomain system-level model.
of the overall system, using FlightGear, an
open-source visualization tool. To test the
system, we set up a harness that injects faults
into the actuator system so that we can verify
that both the component and overall system
respond correctly (Figure 6).
Next Steps
So far, we have built up a component from
requirements, simulated and tested that
component, and then connected it to a system-level model for additional simulation
and testing. There are a number of additional
steps that we can take to enhance the modeling and simulation workflow. For example,
we can:
• Speed up simulation performance using
the Performance Advisor in Simulink
• Implement a formal testing and verification process with design proofs, coverage
analysis, and test-case generation
• Replace blocks with connections to hardware as hardware becomes available
Whichever step you choose next, the key
is to model, simulate, and test the system as
frequently and as early as possible to find and
fix defects early to reduce overall system development cost. ■
Learn More
Webinar: Simulink and Stateflow for Simulation
Engineers 48:04
mathworks.com/wbnr-81993
Modeling and Simulation
mathworks.com/modeling-simulation
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
25
Systematic Fraud Detection Through
Automated Data Analytics in MATLAB
By Jan Eggers, MathWorks
As the Madoff Ponzi scheme and recent high-profile raterigging scandals have shown, fraud is a significant threat to financial
organizations, government institutions, and individual investors.
Financial services and other organizations have responded by stepping
up their efforts to detect fraud.
S
ystematic fraud detection presents several
challenges. First, fraud detection methods require complex investigations that involve the
processing of large amounts of heterogeneous
data. The data is derived from multiple sources
and crosses multiple knowledge domains, including finance, economics, business, and law.
Gathering and processing this data manually is prohibitively time-consuming as well
as error-prone. Second, fraud is “a needle in a
haystack” problem because only a very small
fraction of the data is likely to be coming from
a fraudulent case. The vast quantity of regular
data—that is, data produced from nonfraudulent sources—tends to blend out the cases of
fraud. Third, fraudsters are continually changing their methods, which means that detection
strategies are frequently several steps behind.
Using hedge fund data as an example, this
article demonstrates how MATLAB® can be
26
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
used to automate the process of acquiring and
analyzing fraud detection data. It shows how
to import and aggregate heterogeneous data,
construct and test models to identify indicators
for potential fraud, and train machine learning
techniques to the calculated indicators to classify a fund as fraudulent or nonfraudulent.
The statistical techniques and workflow
described are applicable to any area requiring
detailed analysis of large amounts of heterogeneous data from multiple sources, including
data mining and operational research tasks in
retail and logistic analysis, defense intelligence,
and medical informatics.
The Hedge Fund Case Study
The number of hedge funds has grown exponentially in recent years: The Eurekahedge
database indicates a total of approximately
20,000 active funds worldwide.1 Hedge funds
are minimally regulated investment vehicles
and, therefore, prime targets of fraud. For example, hedge fund managers may fake return
data to create the illusion of high profits and
attract more investors.
We will use monthly returns data from January 1991 to October 2008 from three hedge
funds:
• Gateway Fund
• Growth Fund of America
• Fairfield Sentry Fund
The Fairfield Sentry Fund is a Madoff fund
known to have reported fake data. As such, it
offers a benchmark for verifying the efficacy
of fraud detection mechanisms.
Gathering Heterogeneous Data
Data for the Gateway Fund can be downloaded from the Natixis web site as a Microsoft®
Excel® file containing the net asset value (NAV)
Code Excerpt 1
% Calculate monthly returns
gatewayReturns = tick2ret(gatewayNAV);
Code Excerpt 2
% Connect to yahoo and fetch data
c = yahoo;
data = fetch(c, 'AGTHX', 'Adj Close', startDate, endDate);
Code Excerpt 3
%Convert to monthly returns
tsobj = fints(dates, agthxClose);
tsobj = tomonthly(tsobj);
Figur e 1. The MATLAB Data Import Tool for
interactively importing data from files.
of the fund on a monthly basis. Using the
MATLAB Data Import Tool, we define how
the data is to be imported (Figure 1). The
Data Import Tool can automatically generate
the MATLAB code to reproduce the defined
import style.
After importing the NAV for the Gateway
Fund, we calculate the monthly returns (code
excerpt 1).
For the Growth Fund of America, we use
Datafeed Toolbox™ to obtain data from
Yahoo! Finance, specifying the ticker symbol
for the fund (AGTHX), the name of the relevant field (adjusted close price), and the time
period of interest (code excerpt 2).
Unfortunately, Yahoo does not provide
data for the period from January 1991 to February 1993. For this time period, we have to
collect the data manually.
Using the financial time series object in
Financial Toolbox™, we convert the imported
daily data to the desired monthly frequency
(code excerpt 3).
Finally, we import reported data from the
Fairfield Sentry fund. We use two freely available Java™ classes, PDFBox and FontBox, to
Code Excerpt 4
% Instantiate necessary classes
pdfdoc = org.apache.pdfbox.pdmodel.PDDocument;
reader = org.apache.pdfbox.util.PDFTextStripper;
% Read data
pdfdoc = pdfdoc.load(FilePath);
pdfstr = reader.getText(pdfdoc);
read the text from the pdf version of the
Fairfield Sentry fund fact sheet (code excerpt 4).
Having imported the text, we extract the
parts containing the data of interest—that is,
a table of monthly returns.
Some tests for fraudulent data require
comparison of the funds’ returns data to
standard market data. We import the benchmark data for each fund using the techniques
described above.
Once the data is imported and available,
we can assess its consistency—for example,
by comparing the normalized performance of
all three funds (Figure 2).
Simply viewing the plot allows for a qualitative assessment. For example, the Madoff
fund exhibits an unusually smooth growth,
yielding a high profit. Furthermore, there are
no obvious indications of inconsistency in the
underlying data. This means that we will be
able to use formal methods to detect fraudulent activities.
Analyzing the Returns Data
Since misbehavior or fraud in hedge funds
manifests itself mainly in misreported data,
academic researchers have focused on devising methods to analyze and flag potentially
manipulated fund returns. We compute metrics introduced by Bollen and Pool2 and use
them as potential indicators for fraud on the
reported hedge fund returns. For example:
• Discontinuity at zero in the fund’s returns
distribution
• Low correlation with other assets, contradicting market trends
• Unconditional and conditional serial
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
27
Comparison of Hedge Fund Performance
Gateway Fund
9
8
Gateway Fund
Growth Fund of America
Fairfield Sentry (Madoff)
Normalized Performance
-0.08-0.06-0.04-0.02
0 0.020.040.06 0.08
Monthly Returns
7
6
Growth Fund of America
5
4
-0.15
-0.1
-0.050
0.05
0.01
0.15
Monthly Returns
3
Fairfield Sentry (Madoff)
2
1
1990199219951997 20002002200520072010
-0.03
-0.02
Figur e 2. Plot comparing the performance of the funds under
consideration.
correlation, indicating smoother than
expected trends
• Number of returns equal to zero
• Number of negative, unique, and consecutive identical returns
• Distribution of the first digit (Does it follow Benford’s law?) and the last digit (Is it
uniform?) of reported returns
To illustrate the techniques, we will focus on
discontinuity at zero.
Testing for Discontinuity at Zero
Since funds with a higher number of positive
returns attract more capital, fund managers
have an incentive to misreport results to avoid
negative returns. This means that a discontinuity at zero can be a potential indicator for
fraud.
One test for such a discontinuity is counting the number of return observations that
fall in three adjacent bins, two to the left of
zero and one to the right. The number of observations in the middle bin should approximately equal the average of the surrounding
two bins. A significant shortfall in the middle
bin observations must be flagged.
Figure 3 shows the histograms of the funds’
returns, with the two bins around zero high-
28
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
-0.010
0.01
0.02
0.03
Monthly Returns
Time
Figur e 3. Histograms of monthly returns for funds under consideration.
lighted. Green bars indicate no flag, and red
bars indicate potential fraud. Only the Madoff
fund did not pass this test.
Results for Funds Under
Consideration
Applying all the tests described above to the
present data yields a table of indicators for
each fund (Figure 4).
The Madoff fund raised a flag in nine out of
ten tests, but the other two funds also raised
flags. Positive test results do not prove that a
given hedge fund was involved in fraudulent
activities. However, a table like the one shown
in Figure 4 indicates funds that merit further
investigation.
Classifying Analysis Results with
Machine Learning
We now have a set of flags that can be used as
indicators for fraud. Automating the analytics enables us to review larger data sets and
to use the computed flags to categorize funds
as fraudulent or nonfraudulent. This classification problem can be addressed using
machine learning methods—for example,
bagged decision trees, using the TreeBagger
algorithm in Statistics Toolbox™. The Tree-
Bagger algorithm will require data for supervised learning to train the models. Note that
our example uses data for only three funds.
Applying bagged decision trees or other machine learning methods to an actual problem
would require considerably more data than
this small, illustrative set.
We want to build a model to classify funds
as fraudulent or nonfraudulent, applying the
indicators described in the section “Analyzing the Returns Data” as predictor variables.
To create the model, we need a training set of
data. Let us consider M hedge funds that are
known as fraudulent or nonfraudulent. We
store this information in the M-by-1-vector yTrain and compute the corresponding
MxN-matrix xTrain of indicators. We can
then create a bagged decision tree model
(code excerpt 5), where nTrees is the number of decision trees created based on bootstrapped samples of the training data. The
output of the nTrees decision trees is aggregated into a single classification.
Now, for a new fund, the classification can
be performed (code excerpt 6).
We can use the fraud detection model to
classify hedge funds based purely on their
returns data. Since the model is automated,
Gateway Fund
Growth Fund of
America
Fairfield Sentry
(Madoff)
Discontinuity
at zero
Low correlation
with other assets
Unconditional
serial correlation
Conditional
serial correlation
Number of returns
equal to zero
Number of
negative returns
Number of
unique returns
Number of consecutive
identical returns
Uniform distribution
of the last digit
Benford’s law
on the first digit
the module to investigate funds under consideration for future investments. Regulatory
authorities could integrate a fraud detection
scheme into their production systems, where
it would periodically perform the analysis on
new data, summarizing results in an automatically generated report.
We used advanced statistics to compute individual fraud indicators, and machine learning to create the classification model. In addition to the bagged decision trees discussed
here, many other machine learning techniques
are available in MATLAB, Statistics Toolbox,
and Neural Network Toolbox™, enabling you
to extend or alter the proposed solution according to the requirements of your project. ■
1
Eurekahedge. www.eurekahedge.com
2
Bollen, Nicolas P. B., and Pool, Veronika K. “Suspi-
cious Patterns in Hedge Fund Returns and the Risk of
Fraud”(November 2011).
www2.owen.vanderbilt.edu/nick.bollen
Figur e 4. Test results for funds under consideration. Red boxes indicate results that raised a flag.
Code Excerpt 5
% Create fraud detection model based on training data
fraudModel = TreeBagger(nTrees,xTrain,yTrain);
Code Excerpt 6
% Apply fraud detection model to new data
isFraud = predict(fraudModel, xNew);
it can be scaled to a large number of funds.
The Bigger Picture
This article outlines the process of developing
a fully automated algorithm for fraud detection based on hedge fund returns. The approach can be applied to a much larger data
set using large-scale data processing solutions
such as MATLAB Distributed Computing
Server™ and Apache™ Hadoop®. Both technologies enable you to cope with data that
exceeds the amount of memory available on
a single machine.
The context in which the algorithm is deployed depends largely on the application
use cases. Fund-of-funds managers working
mostly with Excel might prefer to deploy the
algorithm as an Excel add-in. They could use
Learn More
Webinar: Machine Learning with MATLAB 53:39
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Example: Credit Rating by Bagged Decision Trees
mathworks.com/credit-rating
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
29
CLEVE’S CORNER
Variants of the QR Algorithm
1
By Cleve Moler, MathWorks
T
he QR algorithm is one of the most successful and powerful tools
we have in mathematical software.
The MATLAB® core library includes several variants of the QR algorithm. These variants compute the eigenvalues of real symmetric matrices, real nonsymmetric matrices, pairs of real matrices, complex matrices, pairs of complex matrices, and singular values of various types
of matrices. These core library functions are used in various MATLAB
toolboxes to find eigenvalues and singular values of sparse matrices
and linear operators, find zeros of polynomials, solve special linear systems, assess numerical stability, and perform many other tasks.
Dozens of people have contributed to the development of the QR
algorithm variants. The first papers on the subject came from J.G.F.
Francis in 1961 and 1962 and Vera N. Kublanovskaya in 1963. But it
was J.H. Wilkinson who developed the first complete implementation of the QR algorithm. Wilkinson also developed an important
convergence analysis. Wilkinson’s book The Algebraic Eigenvalue
Problem and two of his papers were published in 1965. This means
we’ll be able to celebrate 2015 as the golden anniversary of the practical QR algorithm.
The variant of the QR algorithm used for the singular value decomposition (SVD) was published in 1965 by Gene Golub and Velvel Kahan and perfected in 1969 by Golub and Christian Reinsch.
The Name “QR”
The name “QR” is derived from the letter Q, used to denote orthogonal
matrices, and the letter R, used to denote right triangular matrices.
There is a qr function in MATLAB, but it computes the QR factorization, not the QR algorithm. Any matrix, whether real or complex,
square or rectangular, can be factored into the product of a matrix Q
with orthonormal columns and matrix R that is nonzero only in its upper, or right, triangle. You might remember the Gram Schmidt process,
which does pretty much the same thing, although in its original form
it is numerically unstable.
A One-Liner
Using the qr function, a simple variant of the QR algorithm can be expressed in one line of MATLAB code. Let A be a square, n-by-n matrix,
and let I = eye(n,n). Then one step of the QR iteration is given by
30
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
s = A(n,n); [Q,R] = qr(A - s*I); A = R*Q + s*I
The quantity s is the shift; it accelerates convergence. As A approaches
an upper triangular matrix, s approaches an eigenvalue. If you enter
these three statements on a single line, you can use the up-arrow key
to iterate.
The QR factorization produces an upper triangular R.
A-sI = QR
Then the reverse order multiplication, RQ, restores the eigenvalues
because
RQ + sI = Q’(A – sI)Q + sI = Q’AQ
So the new A is similar to the original A. Each iteration effectively
transfers some “mass” from the lower to the upper triangle while preserving the eigenvalues. As the iterations proceed, the matrix begins
to approach an upper triangular matrix with the eigenvalues conveniently displayed on the diagonal.
An Example
To illustrate this process we’ll use a matrix from the MATLAB
Gallery collection.
A = gallery(3)
A =
-149
-50
-154
537
180
546
-27
-9
-25
It is not at all obvious, but this matrix has been constructed to have
eigenvalues 1, 2, and 3. The first iteration of our one-line QR code produces
A =
28.8263
-259.8671
773.9292
1.0353
-8.6686
33.1759
-0.5973
5.5786
-14.1578
The matrix is now much nearer to being upper triangular, but the eigenvalues are still not evident. However, after five more iterations we
have
A =
3.0321
-8.0851
804.6651
0.0017
0.9931
145.5046
-0.0001
0.0005
1.9749
We begin to see the eigenvalues 3, 1, and 2 emerging on the diagonal. Eight more iterations give
A =
3.0716
-7.6952
802.1201
0.0193
0.9284
158.9556
0
0
2.0000
The eigenvalue 2.0 has been computed to the displayed accuracy,
and the below-diagonal element adjacent to it has become zero. At this
point it is necessary to continue the iteration on the 2-by-2 upper left
submatrix.
The QR algorithm is never carried out in this simple form. It is
always preceded by a reduction to a compact form in which all the
elements below the subdiagonal are zero. The iteration preserves this
reduced form, and the factorizations can be done much more quickly.
The shift strategy is more sophisticated, and is different for various
forms of the algorithm. Additionally, the reduced form is of utmost
importance for the convergence properties of the iteration.
Symmetric Matrices
Figures 1–3 illustrate three of the most important variants of the QR
algorithm. The figures are snapshots taken from the output generated by the program eigsvdgui.m from Numerical Computing with
MATLAB.
The simplest variant involves real, symmetric matrices. An n-by-n
real, symmetric matrix can be reduced to tridiagonal form by means
of n-2 Householder reflections, which are a sequence of similarity
transformations preserving the eigenvalues. The QR iteration applies
to the tridiagonal form. Wilkinson provided a shift strategy that allowed him to prove both global convergence and a local cubic convergence rate. Even in the presence of roundoff error, this algorithm
is guaranteed to succeed.
Figure 1 shows an initial symmetric matrix, the situation halfway
through the reduction to tridiagonal, the tridiagonal, the situation
partway through the QR iteration, and finally, the eigenvalues. Actually, because the matrix is symmetric, the computation is only performed on one half of the array, but our figure reflects the results to
show an entire matrix.
Nonsymmetric Matrices
The situation for real, nonsymmetric matrices is much more complicated. The initial reduction uses n-2 Householder similarity transformations to create a Hessenberg matrix, which is upper triangular plus an “extra” subdiagonal. A QR iteration with a double shift
strategy is then used. This preserves the Hessenberg form while attempting to create a real Schur form, which is upper triangular except for 2-by-2 blocks corresponding to pairs of complex conjugate
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
31
Figur e 1. Eigenvalues of a symmetric matrix. Left to right: The input—a random symmetric 10-by-10 matrix, halfway through the orthogonal reduction
to tridiagonal form, symmetric tridiagonal form, partway through the symmetric tridiagonal QR iteration, and the final diagonal matrix of eigenvalues.
Figur e 2. Eigenvalues of a nonsymmetric matrix. Left to right: The input—a random 10-by-10 matrix; halfway through the orthogonal reduction to
Hessenberg form; Hessenberg form, upper triangular plus one subdiagonal; partway through the nonsymmetric QR iteration; and the final real Schur form
with real eigenvalues and 2-by-2 blocks with complex pairs of eigenvalues on the diagonal.
Figur e 3. Singular values of a rectangular matrix. Left to right: The input—a random rectangular 12-by-10 matrix, halfway through the orthogonal
reduction to bidiagonal form, bidiagonal form, partway through the bidiagonal singular value QR iteration, and the final diagonal matrix of singular values.
eigenvalues on the diagonal.
The nonsymmetric Hessenberg QR algorithm is not infallible. It is
an iterative process that is not always guaranteed to converge. Even
30 years ago, counterexamples to the basic iteration were known.
Wilkinson introduced an additional “ad hoc” shift to handle them,
but no one has been able to prove a complete convergence theorem.
So, on rare occasions, MATLAB users might see this message:
A=
0
1
0
0
2
0
1
0
0
0
0
1
-1
0
0
0
This is the companion matrix of the polynomial p(x)=x4-2x2+1, and
the statement
Error using ==> eig, Solution will not converge
roots([1 0 -2 0 1])
Years ago, recipients of this message might have accepted it as unavoidable. But today, most people would be surprised or annoyed; they
have come to expect infallibility.
We now know of a 4-by-4 example that may cause the real, nonsymmetric QR algorithm to fail on certain computers, even with
Wilkinson’s ad hoc shift. The matrix is
32
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
calls for the computation of eig(A). The values λ= 1 and λ= -1 are
both eigenvalues, or polynomial roots, with multiplicity two. For real
x, the polynomial p(x) is never negative. These double roots slow
down the iteration so much that, on some computers, the vagaries
of roundoff error interfere before convergence is detected. The itera-
tion can wander forever, trying to converge but veering off when it
gets close.
Similar behavior is shown by examples of the form
0
1
0
0
1
0
δ
0
0
-δ
0
1
0
0
1
0
where δ is small but not small enough to be neglected—say, δ = 10-8.
The exact eigenvalues are close to a pair of double roots. The Wilkinson
double shift iteration uses one eigenvalue from each pair. This iteration
does change the matrix, but not enough to get rapid convergence. So
we have to use a different double shift based on repeating one of the
eigenvalues of the lower 2-by-2 blocks.
These different ad hoc shift strategies have been incorporated into
the latest versions of LAPACK, and consequently, into MATLAB. We
are now in the situation where we do not know of any matrices that
cause eig or roots to display the “will not converge” message,
but we have no proof that the nonsymmetric code with these various
embellishments is infallible.
Figure 2 shows an initial nonsymmetric matrix, the situation halfway through the reduction to Hessenberg form, the Hessenberg form,
the situation partway through the QR iteration, and the final real Schur
form. For this particular matrix, it happens there are four real eigenvalues and three complex conjugate pairs, for a total of ten eigenvalues.
Singular Values
The singular values of a possibly rectangular matrix A are the square
roots of the eigenvalues of the symmetric matrix A’A. This fact can be
used to motivate and analyze an algorithm, but it should not be the basis for actual computation with finite precision arithmetic. The initial
phase of the Golub-Kahan-Reinsch algorithm involves Householder
reflections operating on both the left and the right to reduce a matrix to a bidiagonal form. This phase is followed by an SVD variant
of the QR algorithm operating on the bidiagonal. Wilkinson’s analysis
of symmetric tridiagonal QR applies to this algorithm as well, so the
process is guaranteed to be globally convergent.
Figure 3 shows an initial rectangular matrix, the situation halfway
through the reduction to bidiagonal form, the bidiagonal form, the situation partway through the QR iteration, and the final diagonal form
containing the singular values.
QR Algorithm Applications
While the QR algorithms for computing eigenvalues and singular values are closely related, the applications of the results are usually very
different. Eigenvalues are often employed to analyze systems of ordi-
nary differential equations where behavior as a function of time is important. Singular values, on the other hand, are useful for analyzing
static systems of simultaneous linear equations, where the number of
equations is often not the same as the number of unknowns.
Control theory and control design automation make heavy use of
eigenvalues. The classical state-space system of differential equations
studied in control theory is
ẋ=Ax+Bu
y=Cx+Du
Using the QR algorithm to compute the eigenvalues of A is essential
to the investigation of issues like stability and controllability.
In statistics, the SVD is a numerically reliable way to obtain the principal components. Principal component analysis (PCA) is a technique
for analyzing an overdetermined system of simultaneous linear equations
Ax=b
where A has more rows than columns. Using the QR algorithm to
compute the singular values and vectors of A produces the principal
components. ■
For Further Reading and Viewing
Golub, Gene H., and Charles F. Van Loan, Matrix Computations, 4th Edition, Johns Hopkins University Press, 1996, 697 pp.
mathworks.com/book-95399.
Moler, Cleve, Numerical Computing with MATLAB, Chapter 10, “Eigenvalues and Singular Values”, mathworks.com/moler/eigs.pdf.
Moler, Cleve, 1976 Matrix Singular Value Decomposition Film
youtube.com/watch?v=R9UoFyqJca8.
Wilkinson, J.H., The Algebraic Eigenvalue Problem, Oxford
University Press, 1965, 662 pp., amazon.com/Algebraic-EigenvalueMonographs-Numerical-Analysis/dp/0198534183.
A portion of this article is based on “The QR Algorithm,” MATLAB
News and Notes, Summer 1995. mathworks.com/qr-algorithm.
1
Learn More
Cleve’s Corner Blog
blogs.mathworks.com/cleve
Cleve’s Corner Collection
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MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
33
TIPS AND TRICKS
Solving a Maze with the Watershed Transform
The watershed transform finds “catchment basins” and “watershed ridge lines” in an image by treating the image as a
surface where light pixels are high and dark pixels are low. It is a powerful tool for solving segmentation problems in
densely packed objects and other difficult images, such as an image of biological tissue or a container full of objects.
Figur es 1–4. Original maze, part of maze corresponding to a catchment basin, path (green) extracted by shrinking the catchment basin, and solution
path overlaid on maze.
Using a watershed transform to solve a
maze—that is, to find a path between the entry and exit points1—is a straightforward process, but it involves several steps. All the steps
must be completed to get the best results.
To illustrate this process, we will use the
watershed function in Image Processing
Toolbox™ to “solve” the maze (Figure 1).
The landscape (surface) of the maze image has two catchment basins. The solution
path is the watershed between those catchment basins. Let’s compute the catchment
basins produced by watershed.
L = watershed(I);
I1 = L1.*I;
P = imoverlay(I, path, [1 0 0]);
imshow(I1)
imshow(P)
Next we use watershed again to get
catchment basins for this new image.
L2 = watershed(I1);
Using imshowpair we compare the new
image to the original. We see in Figure 3
that the catchment basin in the new image
has shrunk by roughly half the width of the
maze paths (shown in green) compared with
the same catchment basin in the original
image.
The watershed transform is also useful
for image segmentation. It can be applied to
many images where traditional techniques
fall short—for example, images where objects are densely packed, such as cells in a
tissue sample. Any image where you can
visually identify high or low intensity areas
between objects will be a good candidate for
watershed segmentation. ■
1
2
We can visualize the catchment basins using imshow (figure not shown here). The interface (boundary) between the two basins is
the solution (path) to the maze.
At this point we have already solved the
bulk of the problem. All we need to do now is
extract the interface between the two basins.
First we create a new image, retaining only
the original image region from one of the
catchment basins (Figure 2).
34
imshowpair(L,L2)
Finally, we extract the path shown in
green, which is the region where the two
catchment basins differ.
img1 = L == 2;
img2 = L2 == 2;
path = img1 - img2;
Using the imoverlay function we can
visualize the path on the maze (Figure 4).
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Available on the File Exchange:
mathworks.com/fx-10502
2
L1 = L == 2;
We are considering only a “standard” or “perfect”
maze that has one path from entry to exit.
Learn More
Steve on Image Processing
blogs.mathworks.com/steve
Example: Marker-Controlled Watershed
Segmentation
mathworks.com/marker
The Watershed Transform: Strategies for Image
Segmentation
mathworks.com/watershed-transform
TEACHING AND LEARNING RESOURCES
Hands-On Learning with MATLAB
MATLAB® supports a variety of hardware platforms for classroom laboratory use. With MATLAB and these platforms,
your students can bring theory to life with hands-on projects in controls, mechatronics, robotics, signal processing, and
circuit design.
Analog Discovery
Digilent Analog Discovery Design Kit is
a hardware development platform that
enables students to design their own analog
circuits. Teaching materials, reference
designs, and lab projects are available for
download.
The Data Acquisition Toolbox™ Support
Package for Digilent Analog Discovery
hardware lets you perform the following
tasks in MATLAB:
• Read data from the two oscilloscope
channels (analog input)
• Control and generate data from the two
waveform generators (analog output)
• Characterize ICs and measure behavior
of the circuit and IC components
• Find and display Digilent Analog
Discovery device settings
MATLAB
• Interactively develop programs to acquire
analog and digital data
• Control DC, servo, and stepper motors
• Run control loops at up to 25 Hz (not real
time)
mathworks.com/digilent-analog-discovery
Arduino Support from MATLAB
Hands-On Learning with MATLAB and
mathworks.com/arduino-support
let you acquire data from sensors and
imaging devices, including the Raspberry
Pi Camera Board, I2C, SPI and serial
interfaces, and GPIO pins
• Access to prebuilt audio and video
algorithms in DSP System Toolbox™ and
Computer Vision System Toolbox™
Analog Discovery 35:00
Learning Basic Mechatronics Concepts Using
Raspberry Pi Support from MATLAB
mathworks.com/matlab-analog-discovery
the Arduino Board and MATLAB
mathworks.com/raspberry-pi-matlab
EDN Network: Student Engineering Labs
mathworks.com/mechatronics-concepts
Enabling Project-Based Learning with MATLAB,
Revived with Hands-on Electronic Kits
Arduino IO Package: Slides and Examples
Simulink, and Target Hardware 45:19
edn.com/electronics-blogs/anablog/4427939/
mathworks.com/fx-27843
mathworks.com/wbnr-81847
Digilent Analog Discovery Support from
Working with the Raspberry Pi Camera Board
Student-engineering-labs-revived-with-handson-electronic-kits
Arduino
Arduino® is a microcontroller board for
exploring concepts in electrical engineering,
motor control, and mechatronics.
MATLAB support for Arduino enables
you to use MATLAB to communicate with
the Arduino board over a USB cable. With
this package your students can:
Raspberry Pi
Raspberry Pi™ is a tiny, low-cost, singleboard computer specifically designed for
teaching. It features a Broadcom® system-ona-chip that includes an ARM11 processor
running at 700 MHz, 256 or 512 MB RAM,
and a VideoCore IV GPU.
MATLAB support for Raspberry Pi
includes:
• A library of MATLAB functions that
mathworks.com/raspberry-pi-camera-board
Learn More
Hardware for Project-Based Learning
mathworks.com/project-hardware
MakerZone
makerzone.mathworks.com
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
35
THIRD-PARTY PRODUCTS
Connecting Low-Cost Hardware Platforms to
MATLAB and Simulink
Low-cost hardware platforms from Arduino® to Zynq® have quickly developed vibrant user communities, and are now
used in a range of applications, including sensing and control, computer vision, and robotics. With these platforms and
their user communities, makers explore new ideas, students engage in hands-on projects, and developers deliver commercial products. Hardware support packages enable users to stream sensor data into MATLAB® and automatically
generate code to run their Simulink® models on real hardware.
Arduino, LEGO, and Raspberry Pi
Development Platforms
Widely adopted by the maker and student
communities, Arduino offers an open hardware architecture with a range of boards and
accessories. The LEGO® MINDSTORMS®
platform offers easy-to-assemble robots,
and serves as a powerful teaching tool in
many schools. Both platforms are frequently
used in robotics and controls applications.
Launched in 2012, Raspberry Pi™ is a pocketsized, single-board computer designed to
make computer science accessible for high
school students. Kits and projects from the
user community have helped broaden the
range of applications to computer vision and
communications. Support packages enable
users to program any of these platforms with
Simulink for standalone execution or configure them to communicate with a MATLAB
session.
Texas Instruments: C2000
LaunchPad
The Texas Instruments C2000™ MCU family
offers hardware for a variety of applications,
from low-cost USB form factor controlSTICKs
to full-featured developer platforms for solar,
motor control, lighting, and digital power
applications. Low-cost LaunchPad™ Evaluation Kits and BoosterPack™ plug-in modules
make it easy to explore different applications
enabled by the C2000 microcontroller. With
Embedded Coder®, engineers can generate
optimized C and C++ code directly from
their algorithms in Simulink and compile
it with TI’s Code Composer Studio™ (CCS)
IDE. Target I/O blocks for C2000 peripherals
are provided with the C2000 hardware support package.
ti.com/c2000
arduino.cc
mindstorms.lego.com
raspberrypi.org
36
MathWorks News&Notes | 2 0 1 4 – 2 0 1 5
Xilinx: Zynq and ZedBoard
The Xilinx® Zynq-7000 All-Programmable
SoC combines a dual-core ARM® Cortex®-A9
with Xilinx 7-series FPGA logic on a single
chip. ZedBoard™ provides a low-cost, rapidprototyping and development platform for a
wide variety of Zynq applications, including
motor control, software-defined radio, and
video and image processing. MathWorks
Zynq support enables you to use MATLAB,
Simulink, HDL Coder™, and Embedded
Coder to target both the ARM cores and the
FPGA logic on Zynq.
xilinx.com/zynq
Learn More
Third-Party Products and Services
mathworks.com/connections
MakerZone
makerzone.mathworks.com
Hardware Support
mathworks.com/hardware
Explore MATLAB and Simulink Webinars
Join a live session or
browse the library of
commercial and
academic recordings.
mathworks.com/recordedwebinars
Technical experts present practical tips and examples. Topics include:
■■ Code generation and verification
■■ Image processing and computer vision
■■ Real-time simulation and testing
■■ Test and measurement
■■ Signal processing and communications
■■ Math, statistics, and optimization
■■ Physical modeling
■■ Machine learning
■■ Robotics
■■ GPU and parallel computing
■■ Verification, validation, and test
■■ Control system design and analysis
POWERED WITH ELECTRICITY, GAS,
AND AUTOMATICALLY-GENERATED CODE.
THAT’S MODEL-BASED DESIGN.
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powertrain, engineers at GM
used models to continuously
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and automatically generate the
embedded code.
The result: a breakthrough HEV,
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To learn more, visit
mathworks.com/mbd
©2012 The MathWorks, Inc.
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