Post on 11-Jul-2020
Behind Today’s Trends The Technologies Driving Change
Jim Tung
MathWorks Fellow
9 July 2014
Big Data
Cloud Computing
MOOC
Industry 4.0
Internet of Things
Wearable Tech
Smart Phones
Software as a Service
Social Computing
Executable Internet
Complex Event Processing
Trends from 2009
Big Data
MOOC
Software as a Service
Social Computing
2014 2009 2010 2011 2012 2013
Google Trends over Time
TRENDS
COME AND GO
R&D Expenditure
(% of GDP)
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
Germany
China
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
Engineers and Scientists in R&D
(per million people)
Long-Term Transformations Driving Changes in Markets
and Technical Applications
Spreading into every industry
Complexifying systems
Speeding up development cycles
Intensifying applied research
Challenging the education systems to produce those skillsets
Our strategy, based on what those transformations imply for the long term
Algorithms in everything
People computing anywhere
Hardware in specialized form factors
Connected chips, devices & systems
ALGORITHMS
IN EVERYTHING
Intel predicts
170 billion transistors
per person
in the world by 2015
1980 1985 1990 1995 2000 2005 2010 2015
200x IN 10 YEARS
Transistors Worldwide
TEXTUAL
&
GRAPHICAL
% Compute Kalman Gain:
W = P*M'*inv(M*P*M'+ R);
% Update estimate
xhat = xhat + W*residual;
% Update Covariance Matrix
P = (eye(4)-W*M)*P*(eye(4)-W*M)' + W*R*W';
Simulink
Stateflow MATLAB
% Predicted state and covariance
x_prd = A * x_est;
p_prd = A * p_est * A' + Q;
% Estimation
S = H * p_prd' * H' + R;
B = H * p_prd';
klm_gain = (S \ B)';
% Estimated state and covariance
x_est = x_prd + klm_gain * (z - H * x_prd);
p_est = p_prd - klm_gain * H * p_prd;
% Compute the estimated measurements
y(:,i) = H * x_est;
Large Collection of Function
Libraries
Mathematical
Modeling
Data Analysis
Algorithm
Development
Computer Vision and
Image Processing
Computational
Finance
Control Systems
Communications
Digital Signal
Processing
Physical Modeling
LTE System Toolbox NEW in Release 2014a
(Golden Reference)
Develop a portion of an
LTE system and verify their
design against the LTE
System Toolbox.
(End-to-end simulation)
See the effect of their
design on overall system
performance.
(Signal generation)
Create an LTE signal to
test a design in the lab and
make performance
measurements.
(Signal recovery) Bring a
recorded LTE signal into
MATLAB and decode it.
Transmitter
Test Waveform
Generation
End-to-end link-level simulation Golden reference
Signal generation and analysis
Signal information recovery
MATLAB System block
Incorporate MATLAB System objects into a Simulink model
Simplify the expression and implementation of algorithms for streaming data
Applications such as audio enhancement, wireless processing, object tracking and radar beamforming
NEW in Release 2013b
Signal processing algorithm
development
Basic math
Advanced digital
signal processing
Rapid iteration for development
and testing
Growing library of algorithms
DSP System Toolbox
APPS
Control System Tuner App
Tune fixed-structure
controllers in Simulink
Specify blocks to tune
Add tuning goals
Visualize tuning results
Update the tuned
Simulink blocks from app
NEW in Release 2014a
User-Created Apps
User-Created Apps
Unified textual & graphical programming
Portfolio of libraries
Apps and resources on MATLAB Central
ALGORITHMS
IN EVERYTHING
HARDWARE
IN SPECIALIZED
FORM FACTORS
RUN YOUR ALGORITHM
Power plants 100s Cars
1,000,000s
Planes
1,000s
HIL Simulator
Real-Time Test System USB Plug-In Device
Microcontroller
CPU-FPGA platforms
Custom ASIC
Microprocessor
Customer Talk
Modellbasierte Entwicklung eingebetteter
Systeme für AUTOSAR mit der
MathWorks-Toolkette
Dr. David Seider, Validas
ExploreToday
• Real-time simulation and
testing
• DSP and vision system
prototyping
• HIL simulation
• Designed for a turnkey
experience Simulink Real-Time Explorer
Build, run, and test real-time applications Simulink Real-Time NEW in Release 2014a
• Real-time simulation and
testing
• DSP and vision system
prototyping
• HIL simulation
• Designed for a turnkey
experience Simulink Real-Time Explorer
Build, run, and test real-time applications Simulink Real-Time NEW in Release 2014a
ExploreToday Track 2
Model-Based Design,
Verifikation, Validierung und Test
Run algorithms on
real-time hardware for
testing and verification
Design iteration and
testing in minutes
Simulink Real-Time Release 2014a
“…with Model-Based Design and rapid real-time prototyping, we
maintain the product development pace that our business demands.”
The Rise of Low-Cost
Hardware for the Masses
Arduino
300,000+ commercially produced
Prices ~$30
Raspberry Pi
2.5+ million shipped
$35
LEGO Mindstorms EV3
$350
TETHERED Write code and communicate
with the board
Low-Cost Hardware Same board … different approaches
EMBEDDED Develop a model and
program the board
Low-Cost Hardware Same board … different approaches
MathWorks.com/hardware
LEGO MINDSTORMS EV3
Samsung Galaxy S4
NEW in Release 2014a
Hardware support
packages
• Get connected and
running quickly
• 150 packages today,
for Arduino, RaspPi,
iPhone, webcams,
Kinect, and more
MathWorks.com/hardware
LEGO MINDSTORMS EV3
Samsung Galaxy S4
NEW in Release 2014a
Hardware support
packages
• Get connected and
running quickly
• 150 packages today,
for Arduino, RaspPi,
iPhone, webcams,
Kinect, and more
ExploreToday Track 3
MATLAB und Simulink in
Forschung und Lehre
Magnus Egerstedt
Khepera 3 MATLAB Simulation
HARDWARE
IN SPECIALIZED
FORM FACTORS Code generation for prototyping and embedded
Run your algorithms directly on real-time hardware
Connecting to low-cost hardware for the masses
CONNECTED
CHIPS, DEVICES,
& SYSTEMS
Internet of Things
1875 1900 1925 1950 1975 2000 2025
“Place” connectivity
PEOPLE 5 billion “People” connectivity via mobile
devices
THINGS 50+ billion
“Thing” connectivity
INFLECTION
POINTS
Growth in Global Connectivity
PLACES 1 billion
© 2010 Ericsson AB – from Joshipura, Arpit, “Infrastructure Innovation - Can the
Challenge be met?” Global Semiconductor Alliance, September 2010
Megabyte 1,000,000 bytes (million)
Gigabyte 1,000,000,000 bytes (billion)
Terabyte 1,000,000,000,000 bytes (trillion)
Petabyte 1,000,000,000,000,000 bytes (million billion)
Exabyte 1,000,000,000,000,000,000 bytes (billion billion)
Big Data in Energy Production
Source: General Electric
Turbine
(machine)
Wind farm
(plant)
Energy Producer
(enterprise)
Analytics Asset
optimization
Operations
optimization
Business optimization
Data
quantity
>100 tags
>6,000 tags
>1M+ tags
Data
frequency
40 milliseconds
1 second
1 second – 10 minutes
17 MB/day
6.3 GB/year
4.1 GB/day
1.5 TB/year
0.7 TB/day
0.25 PB/year
Big Data in Many Industries
ENERGY Smart grid
FINANCE Fraud detection
AUTO Fleet data will
influence vehicle design
AERO Maintenance, reliability
BIOTECH Instrumented humans
Exabyte 1,000,000,000,000,000,000 bytes (billion billion)
Big Data in MATLAB
Memory &
Disk Management
64-bit
Memory Mapped Variables
Disk Variables
Intrinsic Multicore Math
Image Block Processing
Processing Speed
GPU Computing
Parallel Computing
Cloud Computing
Distributed Arrays
Algorithms
Streaming Algorithms
Machine Learning
Big Data in MATLAB
Memory &
Disk Management
64-bit
Memory Mapped Variables
Disk Variables
Intrinsic Multicore Math
Image Block Processing
Processing Speed
GPU Computing
Parallel Computing
Cloud Computing
Distributed Arrays
Algorithms
Streaming Algorithms
Machine Learning
Reduce detail
in the
voiceover and
mention that
it’s also
visualization of
the big data
ExploreToday MasterClass
2^48 - keine Angst vor
großen Datensätzen mit
MATLAB
PROCESSING OPTIONS
• MATLAB RESTful interface to Cluster
• MATLAB Hadoop Streaming
• NoSQL connector (e.g. mongo)
• MATLAB / Java App accessing Cluster
• MATLAB Map-Reduce Components
CONNECTED
CHIPS, DEVICES,
& SYSTEMS Memory management
Computing power
Advanced algorithms
PEOPLE
COMPUTING
ANYWHERE
Cloud as a New Platform
1,000s of applications MILLIONS of users
Terminal - mainframe, mini
HUNDREDS OF MILLIONS
of users 10,000s of applications
PC - LAN, Internet
BILLIONS of users
Cloud – mobile, browser, social, big data
MILLIONS of apps
Source: IDC, 2013
MATLAB Mobile Support for iPhone, iPad & Android
, enhancing your
MATLAB Desktop MATLAB Distributed Computing Server on Amazon EC2
The cloud
The cloud, running
MATLAB, on demand, from anywhere
The cloud, running
MATLAB, on demand, from anywhere
MATLAB Production Server
Deploy MATLAB analytics into
enterprise IT frameworks
Integrate with databases, webservers
and application servers
Seamless transition from algorithm prototyping
to enterprise-scale analytics without recoding
“This product is a game changer, for sure.”
—Quantlabs
Running in Enterprise IT Environments
MATLAB Production Server
Deploy MATLAB analytics into
enterprise IT frameworks
Integrate with databases, webservers
and application servers
Seamless transition from algorithm prototyping
to enterprise-scale analytics without recoding
“This product is a game changer, for sure.”
—Quantlabs
Running in Enterprise IT Environments
ExploreToday Track 1
Mathematische Modellierung,
Datenerfassung,
Signalverarbeitung und –analyse
PEOPLE
COMPUTING
ANYWHERE MATLAB on mobile devices
MATLAB on the cloud
MATLAB in enterprise IT frameworks
Algorithms in everything
People computing anywhere
Hardware in specialized form factors
Connected chips, devices & systems
Algorithms in everything
People computing anywhere
Hardware in specialized form factors
Connected chips, devices & systems
MOOCs Massive Online Open Courses
Online course
Unlimited participation
Open access via the web
Keynote:
Neues Lernen - MOOC, MATLAB und Simulink
Prof. Dr.-Ing. Georg Fries
Professor der Ingenieurswissenschaften
Hochschule RheinMain
Algorithms in everything
People computing anywhere
Hardware in specialized form factors
Connected chips, devices & systems
Seize the opportunity to…
• Learn what’s new…
…. and how to apply it
• Engage your colleagues
• Benefit from the experience
ExploreToday
Enjoy the conference!