behind today’s trends - mathworks€¦ · cloud as a new platform millions of users 1,000s of...
TRANSCRIPT
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Behind Today’s TrendsThe Technologies Driving Change
Sameer M. Prabhu, Ph. D.
Industry Marketing Director
MathWorks
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Big Data
Cloud Computing
MOOC
Industry 4.0
Internet of Things
Wearable Tech
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Smart Phones
Software as a Service
Social Computing
Executable Internet
Complex Event Processing
Trends from 2009
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Big Data
MOOC
Software as a Service
Social Computing
20142009 2010 2011 2012 2013
Google Trends over Time
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TRENDS
COME AND GO
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Research & Development
Source: OECD Main Science and Technology Indicators Database, 2014/1.
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Research & Development
R&D investment by the world’s top 2000 companies
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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
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Algorithms in everything
People computing anywhere
Hardwarein specializedform factors
Connected chips, devices & systems
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ALGORITHMS
IN EVERYTHING
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Intel predicts
170 billiontransistors
per person
in the world by 2015
1980 1985 1990 1995 2000 2005 2010 2015
200xIN 10 YEARS
Transistors Worldwide
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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';
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Simulink
Stateflow MATLAB
Simulink
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MATLAB System block
Incorporate MATLAB System objects into a Simulink model
– System objects simplify the implementation of stream processing
– Optimized to process large streams of data
NEW in Release 2013b
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Signal processing
algorithm development
Basic math
Advanced digital
signal processing
Rapid iteration for
development and testing
Growing library of
algorithms
DSP System Toolbox
メディカルトラック @13:00から
http://www.mathworks.com/medical-devices/
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Phased Array System Toolbox
Algorithm
development
• Antenna array design
and analysis
• Radar signal
generation
• Detection and signal
processing algorithms
Radar system
modeling
• Performance testing
• Regulatory
compliance
• What-if analysis
NEW in Release 2014bBlock library for phased
array system design in
Simulink
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Large
Collectionof Function
Libraries
Mathematical
Modeling
Data Analysis
Computer Vision and
Image Processing
Computational
Finance
Control Systems
Communications
Digital Signal
Processing
Physical Modeling
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Advanced Driver Assistance Systems
From Advance Driver Assistance Systems Market,
Drivers, Functions , Continental AG, KSAE 2011
Adaptive cruise
control + Stop&Go
Forward collision
warning
Emergency brake
assist
Advanced
emergency braking
system
Traffic signal
recognition
Intelligent headlamp
control
Lane change assist
Back-up aid
Lane departure
warning
Lane keeping
system
Blind spot detection
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Design, Analyze, and Implement Radar Sensors' Alignment Algorithm with MATLAB
Ling Ma, Delphi, MathWorks Automotive Conference, May 2014, Michigan, USA.
Advanced Driver Assistance Systems
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Traffic sign recognition in driver assistance systems - MATLAB at Continental
Dr Alexander Behrens, Continental, MATLAB Expo, July 2014, Munich, Germany.
Advanced Driver Assistance Systems
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APPS
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Control System Tuner App
Tune fixed-structure
controllers in Simulink
Specify blocks to tune
Add tuning goals
Visualize tuning results
Update tuned Simulink
blocks from app
NEW in Release 2014a
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User-Created Apps
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Unified textual & graphical programming
Portfolio of libraries
Apps and resources on MATLAB Central
ALGORITHMS
IN EVERYTHING
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HARDWARE
IN SPECIALIZED
FORM FACTORS
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RUN YOUR ALGORITHM
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Power plants 100sCars
1,000,000s
Planes
1,000s
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HIL Simulator
Real-Time Test System USB Plug-In Device
Microcontroller
Programmable SOC
Custom ASIC
MicroprocessorXilinx Zynq Intelligent Drives Kit
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Simulink Real-Time Explorer
Build, run, and test real-time applicationsSimulink Real-TimeNEW in Release 2014a
"Simulinkのバーチャル環境を現実の世界へ" @ 17:30
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Run algorithms on
real-time hardware
– Real-time testing
and verification
Design iteration and
testing in minutes
Higher software
quality
Simulink Real-TimeRelease 2014a
“…with Model-Based Design and rapid real-time
prototyping, we maintain the product development
pace that our business demands.”
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The Rise of Low-Cost
Hardware for the Masses
Arduino
300,000+ commercially produced
$30 (UNO), $55 (Mega 2560), $55 (Due)
Raspberry Pi
2.5+ million shipped
$35 (Model B)
LEGO Mindstorms EV3
3rd generation from
LEGO Mindstorms
$350 (for base set)
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NEW in Release 2014b
Hardware
support
packages
• Get connected
and running
quickly
• 150 packages
today,
for ARM, Texas
Instruments,
iPhone, Android,
Kinect, and more
MathWorks.com/hardware
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Magnus Egerstedt
Khepera 3
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HARDWARE
IN SPECIALIZED
FORM FACTORS MATLAB algorithms in production systems
Code generation for prototyping and embedded
Connecting to low-cost hardware for the masses
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CONNECTED
CHIPS, DEVICES,
& SYSTEMS
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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
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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)
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 Energy Production
"インテージ株式会社: 価格弾力性分析におけるMATLAB活用事例の紹介" @17:10
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Big Data in Many Industries
Exabyte 1,000,000,000,000,000,000 bytes (billion billion) ENERGYSmart grid
FINANCEFraud detection
AUTOFleet data will
influence vehicle design
AEROMaintenance, reliability
BIOTECHInstrumented humans
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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
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PROCESSING OPTIONS
• MATLAB RESTful interface to Cluster
• MATLAB Hadoop Streaming
• NoSQL connector (e.g. mongo)
• MATLAB / Java App accessing Cluster
• MATLAB Map-Reduce Components
"エンタープライズ向けアプリケーション開発の例"@15:50
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CONNECTED
CHIPS, DEVICES,
& SYSTEMS Memory management
Computing power
Advanced algorithms
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PEOPLE
COMPUTING
ANYWHERE
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Cloud as a New Platform
1,000s of applicationsMILLIONS of users
Terminal - mainframe, mini
HUNDREDS OF MILLIONS
of users10,000s of applications
PC - LAN, Internet
BILLIONS of users
Cloud – mobile, browser, social, big data
MILLIONS of apps
Source: IDC, 2013
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MATLAB MobileSupport for iPhone, iPad & Android
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The cloud, enhancing your
MATLAB DesktopMATLAB Distributed Computing Server on Amazon EC2
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The cloud, running
MATLAB, on demand, from anywhere
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The cloud, running
MATLAB, on demand, from anywhere
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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
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PEOPLE
COMPUTING
ANYWHERE MATLAB on mobile devices
MATLAB on the cloud
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Algorithms in everything
People computing anywhere
Hardwarein specializedform factors
Connected chips, devices & systems
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MOOCs
MOOC = Massive Online Open Course
Online course
Unlimited participation
Open access via the web
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Magnus Egerstedt
Khepera 3
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Algorithms in everything
People computing anywhere
Hardwarein specializedform factors
Connected chips, devices & systems
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