more innovations arise from borrowing and combining than

Shoumen DattaShoumen Datta 11

More innovations arise from borrowing and combining than from simple invention (Fortune, 2004)

Research Scientist, Engineering Systems Division, School of Engineering, MIT and Executive Director, MIT Forum for Supply Chain Innovation

Shoumen Datta, MIT Forum for Supply Chain Innovation, School of Engineering <[email protected]>2

I think there is a world market for maybe five computers. Thomas WatsonChairman, IBM

1943

There is no reason anyone would want a computer in their home. Kenneth OlsonFounder, DEC

1970

Prediction is very difficult, especially about the future.Prediction is very difficult, especially about the future.NielsNiels BohrBohr

19201920


17711771 18251825 18861886 19391939 19771977 ~1997~1997

Industrial Revolution

18001800

Knowledge Economy

18531853 19131913 19691969 20052005 20252025

Ado

ptio

nA

dopt

ion

TechnologyIntroduced

18531853 19131913 19691969 20252025 20612061 20812081

Conceptual Advances (add to the Wealth of Nations but add to the Wealth of Nations but ““Adam Smith was wrong!Adam Smith was wrong!””))

TextileTextile RailwayRailway AutoAuto ComputerComputer AgentsGrid, SL

NanotechHydrogen

Fusion

Economic History from Norman Poire

1959AI

Atoms BitsPhysical World Model

PROCESS

DECISION


Barriers to Adaptability and Death by “Clockspeed”

Graphics: Forrester

PLM SCM CRM

XSCMAdaptable Business Network

“Clockspeed” by Charles Fine, MITAdaptable Business Network popularized by Bob Betts, Founder, Mainstreet Applications and co-author of “Adapt or Die”


Source: Forrester

Data vs Noise

~6 terabytesper second

Estimate excludes real-time data



2005

2003

2004


65% of SKUsare inaccurate

Absolute Error (units)

% o

f SK

Us (

n=36

9,59

2)35%

22%

10%

6%4%

3% 3%2% 2% 1% 1%

9%

1%0%

5%

10%

15%

20%

25%

30%

35%

40%

0 1 2 3 4 5 6 7 8 9 10 11-50 51-100 101-200 201-400

Inventory Record Inaccuracy

Source: Nicole DeHoratius, University of Chicago and Ananth Raman, Harvard Business School


Too many wrong products !!

1970 1980 1990 1995

Markdowns (% of sales)

31%

26%

21%

16%

11%

6%

Fewer right products“A third of customers entering a store leave without buying. They can’t find what they came to buy.”

Push Pull Adaptive PredictiveConvergence Convergence Convergence

Source: Nicole DeHoratius, University of Chicago and Ananth Raman, Harvard Business School


Retain 87.5% of the information

Data vs Information: Systems introduce Artifacts and Inaccuracies

Size = Length + Breadth

Retain 62.5% of the information

http://obelia.jde.aca.mmu.ac.uk/multivar/pca_graf.htm


Retailer’s DCMANUFACTURERRaw material

SUPPLIERS

Store

Store

Store

Store

Store

Store

Store

Store

DC

DC

DC

Paper

Medical

Cotton

Store

Plant DC

Plant DC

EPC (RFID), UWB (UID)

Replenishmentneeds

Confirmation

Loads

Store Orders

Status

Futureshipping needs

Replenishmentneeds determinedfrom RFID Tag info“Intelligent Signal”

Transport

Customer Info Center

ManufacturerHQ

real timeRFID data

Personal Care

Consumer Tissue

Health Care

InformationAgent

Inventory AgentInventory Agent

TLB Agent

Cross-Docking Agent

ConsumptionInventory

SEMANTIC PORTAL

Open Grid Services ArchitectureOpen Grid Services Architecture

ImmediateReplenishment

needs

Right-Time Data in Agents-integrated Adaptable Demand Network ?

(This illustration is a modified composite from various sources including P&G, Forrester, Kimberly-Clark)


Real Time DataStreaming Data, Continuous Queries

D2B / RFID / UWBObject Oriented Hardware

Service (Value) Supply Chain

Semantic GridSemantic GridWeb PortalWeb Portal

dERPGRID

Internet 0 Internet 0 Internet 1 Internet 1 Internet 2Internet 2

AGENTS

S E C U R I T Y

Internet 0 Ubiquitous Infrastructure: Real-Time Data ON/OFF Control

MEMS / NEMSIntel Motes, Crossbow

Service (Value) Supply Chain

Froman officein Shinzen, China, you logon a SDR reader in a warehouse in USA, to check if your products arrived on-time. They did. You also get to know thatyour distributor in Santiago, Chile and retailer in Espoo,Finland also checked the delivery status, moments before you logged on.

Bits, Atoms, DecisionsBits, Atoms, Decisions

Right-TimeAnalytics

Data Interrogators as Ubiquitous Internet Appliances

IPv6


CONVERGENCE CONVERGENCE

Real-Time Adaptive Model

DECISIONDECISION

PROCESSPROCESS

OBJECTOBJECT

OPTIMIZE

INFODATA

AGENTSAGENTS


CONFLUENCE CONFLUENCE

Near Real-Time Predictive Model

DECISIONDECISION

PROCESSPROCESS

OBJECTOBJECT

OPTIMIZE

INFODATA

AGENTSAGENTSDemand “ Pull ”Forecast “ Predict ”


RRadio adio FFrequencyrequency IDIDentificationentification

Enabling RealEnabling Real--time Data at the Righttime Data at the Right--time ?time ?


1977RCA develops "Electronic identification system"

What is ‘new’ about RFID ? Evolution of RFID

1940 1950 1960 1970 1980 1990 2000

RFID born out of Radar effort (WWII)

RFID crawls out Theory of RFID, field trials planned

Early adopters implement RFID

Commercial RFID endeavors sprout

Many RFID standards emerge

1948Harry Stockman invents RFID. Publishes paper,“Communication by Means of Reflected Power”

1952F.L. Vernon“Application of the microwave homodyne”

1950D.B. Harris patents RFID.“Radio transmission systems withmodulatablepassive responder”

1963-1964R.F. Harrington advances theory with “Field measurements using active scatterers” and“Theory of loaded scatterers” 1975

Los Alamos National Lab (LANL) releases RFIDresearch to public sector, publishes “Short-range radio-telemeteryfor electronic identification using modulated backscatter”

1973Raytheon's "Raytag"

1966Commercialization of EAS, 1-bit Electronic ArticleSurveillance

1976-1977LANL RFID spin-offs Indentronixand Amtech

1975-1978Raytheon, Fairchild & RCA develop RFID

1982Mikron founded;bought by Philips

Partial Source: Shrouds of Time – The History of RFID

1991TI creates TIRIS to develop and market RFID

Vast number of RFID companies and

‘short-sight’ enters the market.

1987First RFID road toll collection implementedin Norway

1992-1995Multi-protocol traffic control and toll collection implemented in Texas, Oklahoma, and Georgia (USA)

1959Identification of Friend or Foe (IFF) long-range transpondersystem reaches breadboard demonstration stage.

RFID hype, peaks

Modified from: Han Pang Huang, National Taiwan University

2003UPC and EANforced by US retailers topromote EPC

1998David Brock and Sanjay Sarma of MIT publishes an idea: ‘Internet of Things’

1999Auto ID Center created at MIT. Retailers drive to standardize EPC

2005Wal-Mart andUS DoD fuelsthe hype curveby demandingsuppliers usepassive RFIDand EPC.


3 0 0 1 2 3 4 5 6 7 8 9 0 6

- UPC/EAN

- Code 128

- Ilv 2 of 5

- Code 39

Point-of-Scanningis potentially theweakest link in the chain.

CHAR. PATTERN CHAR. PATTERN1 M2 N3 O4 P5 Q6 R7 S8 T9 U0 VA WB XC YD ZE -F

G SPACE

H *I $J /K +L %

- 2D Stacked


FROM TO

SSCC

CARRIER

Good Time Supplier1155 Battery StSan Francisco,94111

CustomerDC14785241 San Antonio Dr NEAlbuquerque NM 87109

SHIP TO POSTBest FreightB/L: 853930

PO: 345-896779-0DEPT: 092

(00) 000521775138957172

(420) 87109

SSCC

Application Identifier

ExtensionDigit

EAN.UCCCompany

Prefix

Check Digit

Serial Reference

6 14141 12345 9


Value Chain Management

Sony S

NC / Tra

nsmeta

Solectr

onMfg

Pen

ang

Sony e

-log

Merise

l

Comp U

SA

Primar

yCon

sumer

Gift

Servic

e, W

arra

nty

Spare

Par

ts

Aware Goods? Object Identification ?Aware Goods? Object Identification ?

Inven

tory

Purch

asing

Manufacturing

Goods Receipt / Picking

Warehouse Management

Tracking

Delivery

Goods Receipt

Recall Control

Track Customer / Product History

Service Support

BUSINESS PROCESS

VALUE CREATION

Inventory / Procurement

Focus: Customer Relationship Management

Supplier Relationship ManagementDistributor Retailer Management


Source:

The Economist, April 24th, 2004

“Likewise, in the past few decades most ofthe companies that have created trulyextraordinary amounts of wealth have doneso by inventing great processes, not greatproducts (technology). Dell, Toyota andWal*Mart, for example, have risen to the top of their respective industries by comingup with amazingly efficient ways of gettingquite ordinary products into the hands of consumers more cheaply than their rivals.”


• Radio Frequency Identification•• Electronic Product Code (EPC)Electronic Product Code (EPC)

RF waves transfer data (object to reader)Re-writable secure dataIdentify individual itemsLine of sight not requiredStable in variety of conditionsRead through most non-metalsRFID transponders 5 cents ? ($0.25 - $150)RFID readers: $2000 to $10 (SDR?)Infrastructure: Profit over Physics?RFID Interface (Real-time data) to ERP (?)Can current RDBMS handle data flow?Auto ID standard Global EPC at UCC.EANLimited spatial capacity of 1 kbpsm2

Item or PalletSKU Reader

Server

Internet

Why use RFID ? EmperorWhy use RFID ? Emperor’’s New Clothes?s New Clothes?

RFID Tag (Active UWB)

ERP


• CPU and Memory• Antenna• Frequencies• Active (battery) • Passive• Read only (WORM) tags • Re-writable tags• Low sophistication = Low Cost.

• One or more RF tags

• Two or more antennas

• One or more interrogators

• One or more host computers

• Appropriate software

• Tag memory: factory or field programmed, partitionable(option: permanent lock)

• Bytes left unlocked can berewritten >100,000 times

• Critical information database

What is RFID ?What is RFID ?System

Tags


Active RFID - Longer rangeContinuously powered tag Low-level RF to the tag High-level RF back to the reader (transmits radio signal)Longer read ranges (>100 metres)Multi-KB data capacity

Passive RFID - Shorter range Tags reflect radio signal from readerTag receives/stores energy to respond Needs stronger RF signal from readerLow RF strength from tagShorter range (~5 cm to ~5 metres)May require link to database

Semi-Passive RFIDSimilar to passiveInternal power (battery) for tag circuitry Range may be extended

Types of RFID


AM Radio SW Radio Garage TV 2-6 FM Radio TV 7-13 TV 14-69 Cordless Ph

0.5 1.7 30 40 54 88 108 174 216 470 806 902

GPS Cell Ph BluTh, b/g 802.11a Satel TV

1.61.2 1.8 2.1 2.4

IC

5.0 5.8 10.7 12.5

MHz GHz

125 KHz 433 MHz 860-930 MHz 2.45 GHz13.56 MHz

U L T R A W I D E B A N D

RFID FrequenciesRFID Frequencies

EPCEPC


More susceptible to noiseShared with other technologies

Small tag/antenna sizeGood rangeVery high data rate

2.45GHz and 5.8GHz

Poor water/tissue penetrationUHF spectrum crowded in USRegulatory issues outside US

Longer rangeHigher data rate

303.8MHz, 418MHz, 433MHz, 868MHz and 915MHz

Government RegulationsHard to get around metal

Water/Tissue penetrationSmall, thinner antenna

13.56MHz

Very large antennaSlow with short range

Free from regulationRelatively inexpensive

125KHz and 135KHz

DisadvantagesAdvantageFrequency



Frequency Regulation Range Data Speed Comments

125-150 kHz Unregulated ≈ 10 cm Low Animal identification and factory data collection systems

13.56 MHz ISM band, differing power levels and duty cycle

< 1 m Low to moderate

Popular frequency for Smart Cards

433 MHz

Non-specific Short Range Devices (SRD), Location Systems

1 – 100 m Moderate US DoD (Active)

860-960 MHz

ISM band (Increasing use in other regions, differing power levels and duty cycle

2 – 5 m Moderate to high

EAN.UCC GTAG, MH10.8.4 (RTI), AIAG B-11 (Tires), EPC (18000-6’)

2450 MHz ISM band (differing power levels and duty cycle)

1 – 2 m High IEEE 802.11b, Bluetooth, CT, AIAG B-11



Frequencies to be Studied

5850 MHz315 MHz

2450 MHz13.56 MHz

862 - 928 MHz7.4 - 8.8 MHz

433.92 MHz50 - 140 kHz

United StatesIndia

United KingdomHong Kong

SingaporeGermany

Russian FederationFrance

Korea (South)China

JapanAustralia

Second Focus

YugoslaviaSouth AfricaNorwayEgypt

UkraineSlovak RepublicNew ZealandDenmark

TurkeySaudi ArabiaNetherlandsCzech Republic

ThailandRomaniaMexicoColombia

TaiwanPortugalMalaysiaCanada

SwitzerlandPolandItalyBrazil

SwedenPhilippinesIsraelBelgium

SpainPeruFinlandAustria

VenezuelaOmanHungaryChile

United Arab EmiratesMaltaCyprusBulgaria

SloveniaKuwait Czech RepublicBahrain

QatarIndonesiaCroatiaArgentinaThird Focus

First Focus

Identify the primary user Identify the availabilityIdentify the maximum possible output powerIdentify the maximum antenna gain Identify the max effective isotropic radiated power (ERIP) Identify the required duty cycle Identify the bandwidthIdentify the channel spacingIdentify the licensing requirementsIdentify restrictions and future plans

Elements of the Study (each frequency, each country)

for RFID, RFDC (RFID data collection) & RFID LAN (RLAN)


RFID Hardware Types

• Acousto-Magnetic – Very Short Range• Inductive – Very Short Range• Modulated Backscatter – Short to Medium Range/Directional• Long Range Active – Long Range• Real Time Location – Long Range


Acousto-Magnetic

• Theft Prevention and Access Control• Easily deactivated• Very low frequency (50-60KHz)• Inexpensive.

EAS Label

Transmitter Receiver

PulsesResonating

Signal


Inductive

• CPU with ferrite/air core• Short range (inches)• Low cost• <150KHz and 13.56MHz • Passive

TagTag

Reader/WriterReader/Writer

Movement of Tag


Inductive Passive 13.56 MHz and <135 KHz

Near FieldASK, PSK


Inductive – How it works

RFID TagRFID Tag

SecondarySecondaryCoilCoil

PowerPowerSourceSource

TraditionalTraditionalTransformerTransformer

PrimaryPrimaryCoilCoil

I

LoadLoad

I

ReaderReader

ToTo““SystemSystem””

I

EM FieldEM Field

< 60 cm< 60 cm

EM FieldEM Field

I


EM Field UHF RFID


Modulated Backscatter

• 915MHz and 2.4GHz• Range up to 90 feet

RF Field

ToSystem

Eye

Light

Radio RFIDTag

Light

ReflectiveObject


Microwave RFID

Passive: ASK, PSKActive: FSK

0.5-1.0 m (far field) 4WActive: 15-20 meters


Long-Range Active Tags

• UHF or 2.4GHz• Range up to 600 feet• Requires Battery


Real Time Location System

t4t4

t1t1

t2t2

t3t3

TagTagReaderReader

• Time Differential or Signal Strength• Range up to 1000 feet• Requires Battery

Shoumen Datta, MIT Forum for Supply Chain Innovation, School of Engineering <[email protected]>

RFID Operation

Reader

RF ModuleTag

Antenna

Host ComputerHost Computer


RFID Tag : Wireless Information System & DB

Interrogation UnitTx/RxMicro

Computer

Computer Network

Antenna Tag

Radio Tx/Rx

RAM ROM

CPU I/O

Pwr Supply

Radio Tx/Rx

RAM ROM

CPU I/O

Pwr Supply


TagInsert

Antenna Reader

Firmware

Customer’sMIS

Host

ApplicationSoftware API

TCP/IP

Power

~

Asset

Asset/Tag

RFID System Components


RESPONSES

COMMANDS

Tag Physical Memory

APPLICATIONRESPONSES

APPLICATION INTERROGATOR RF TAG

APPLICATIONCOMMANDS Command /

ResponseUnit

PHYSICALINTERROGATOR

DATA PROTOCOL PROCESSOR

Encoder

Logical Memory

AIR

INTERFACELogical Memory

Map

The Logical Memory Map in the Tag Physical Memory is given by the tag architecture and mapping rules

in the Tag Driver. All the information in the Logical Memory is represented in the Logical Memory Map.

Decoder

Tag Driverand

Mapping Rules

Application Program

InterfaceA

pplication Program Interface

DEVICECOMMANDS

DEVICERESPONSES

RFID System Architecture


Microwave

UHF (400-1000 MHz)

5.5 metres30 WattUS site license

2.0 metres4.0 WattUS & Canada

0.7 metres0.5 WattEU

DistanceApproved EIRP*Radiated Power

from Reader

Where

RFID Technology vs Business Process

Data: AIM

Radiated Power ≈ Energy Field » Read Range

* EIRP * EIRP -- effective isotropic radiated power


Field StrengthTransmitted Signal Inversely Proportional to Exponent of Distance

13.56 MHz 1/d 6

UHF 1/d 2, 1/d 3, 1/d 4 (orientation dependency)


Sequence of CommunicationSequence of Communication

• Host manages reader(s) and issues commands• Reader and tag communicate via RF signal• Carrier signal generated by reader (request from host application)• Carrier signal transmitted through antennas• Carrier signal reaches tag(s)• Tag receives and modifies carrier signal• Tag ‘sends back’ modulated signal (passive backscatter)• Antennas receive modulated signal and transmits to reader• Reader decodes data• Results returned to host application

RFID Operation RFID Operation -- PASSIVEPASSIVE


RFID Operation RFID Operation -- ACTIVEACTIVE

Sequence of Communication Sequence of Communication

• Host manages reader(s) and issues commands• Reader and tag communicate via RF signal• Inquiry (upon request from the host application)• ‘Wake-up’ signal transmitted by interrogator to all tags within

communication range• Tags enter ‘ready state’ awaiting command from interrogator • Interrogator initiates communications; listens for response from tags• Tag communicates with interrogator based on command received• Antennas receive modulated signal and transmit to reader• Reader decodes data• Results returned to host application


STEPStowardPRIVACYPROTECTION


Portal Applications

Bill of LadingMaterial Tracking

Number items at forklift speeds8’ X 10’ doorwaysElectronic receipt & dispatchWrong destination alertElectronic markingPallet/container item tracking


Conveyor / Assembly Line

Up to 450 fpm60+ items per containerInexpensive tunnelsLonger tunnel more itemsElectronic receiptSortingElectronic marking


Handheld ApplicationsHandheld Applications

Material HandlingInspecting / Maintaining

Wireless / BatchInventory Management

Where is it? What is it?What is inside the box?

Where is it going? Where has it been?Should it be here?

What have I assembled or disassembled?How many do I have? Do I have enough?

Has this been repaired?Is this under warranty?Has this been inspected?Is this complete? What is the asset’s status?

ASN Verification


Pet/Animal Tracking

ACCESS SECURITY


DoD In-Transit Visibility (ITV)

• Over 350 Nodes World-Wide• Tag is Interrogated as it Passes a Node• TRANSCOM Kits for Contingency Operation.

ITV Nodes ITV Server

433MHz Interrogation

433MHz Response with ID and/or Data (TAV Format) Source: US DoD


ITV Process

Container contains bar coded equipment

Manifest is written to tag and uploaded to server (TAV Format)

In TransitShipment ArrivesTag Manifest is

Read

Nodespick-up tag id

Information uploaded to ITV

server

Access to ITV Data via WebITV Server

Tag Location is Reported to Central

ITV Server

Source: US DoD


Bosnian ITV Capability

CROATIA

ITALY

HUNGARY

CZECHREPUBLIC

GERMANY

AUSTRIA NSE

ASG

xxxx

xxx

- JTAV/LAD

- RF Interrogators Installed

- RF Interrogators to be Installed

AIRTRUCK

RAIL

QUALCOMM Provides Visibility ofTruck Convoys & Rail MovementsData Passed to Paris Hub via SatelliteDispatch Stations Access Paris Hubvia Modem/Phone Line

NSE

Frankfurt International

Kaiserslautern

Tuzla

Prague

Tuzla APOD123rd FSB)

KaspovarTazar AirfieldInterrogators also installed at:

• Miesau• Germersheim• ERF• Baumholder• Bad Kreuznach• Baumholder Railhead• Weillerbach Railhead• Coleman Barracks Railhead

Data Passed via Phone Line to LOGSAWithin 15 Minutes of Reading Tag

All ALOC Shipments From NewCumberland & All Containers ShippedFrom USAREUR Are Tagged

Ramstein

Source: US DoD


Asset Tracking: Healthcare Example (Hospital)

Browser access to location data from anywhere in hospital

SiteServer

Hospital LANTagged Asset

Tag Readers(433MHz Receive)

Sign Post (132KHz Transmit)

Sign Post ID

Tag ID and Last Sign Post ID

(Up to 250 feet)

Range to 12 feet

802.11b WLANAccess Points

FIPS 140-2Firewall

Hand Held Device & ComputersWith Security Client


Asset Tracking: Ground Services Example (Airports)


Tracking

HUMANS

OBJECTSOBJECTS


Short, simple, extensible code to uniquely identify

products and reference networked information.

(MIT) Auto ID Center - EPC Objective 1998


RFID Evolves

Store Data on the Tag

Reader

Database

DATABASE

Object Name Server

Proprietary RFID MIT Auto ID

Reader

EPC (unique ID)


Open StandardsProprietaryTechnology

InexpensiveExpensiveCost

SCM-ERP system-wideClosed loopApplications

On the networkOn the tagData

Present/FutureAuto-ID

Past/Present Commercial RFID

RFID Evolution


ONS / PML

Reader

DSS Software

Antenna Antenna

EPC EPCUCC.EAN Alphanumeric string

Standards-based Auto-ID

PML – Physical Markup Language

ONS – Object Name Service

EPC – Electronic Product Code


EPC Objectives

Unique EPC should be a unique numbering scheme

Reference EPC should be used primarily as an information reference

Simple EPC should be a simple as possible and minimize information content

Internet EPC should be integrally coupled to Internet systems and protocols

Standards EPC should accommodate were possible legacy standards, systems and codes


Electronic Product CodeNaming Scheme for Physical Objects

Grains of rice13,000,000,000,000,000Razor blades20,000,000,000Televisions1,000,000,000Computers560,000,000Cars per year6,000,000

SubSub--componentscomponentsSpare PartsSpare PartsAssembliesAssembliesContainersContainers


128 bit EPC structure can incorporate IPv6 numbering scheme


EPC Vision ? RFID Reality Check ?

•More than 12 separate RFID EPC Trials in US•UK Home Office RFID Trial (2000-2003)•Germany, Japan, Singapore, UK RFID Trials•Gillette buys 500 million EPC RFID Alien tags•Wal*Mart suppliers to use EPC RFID tags by 2005•EPC Global to be managed by UCC.EAN•US DoD urges suppliers to use RFID tags by 2005 •How much of this is marketing hype ?

P


01.0203D2A.916E8B.0719BAE03C

Electronic Product CodeElectronic Product Code (EPC) 96 bits(EPC) 96 bits

Header Object ClassePC Manager Serial Number

no longer behind bars…Beyond Barcode

Header: 8 bits = 256

ePC Mgr: 28 bits = 268, 435,456

Object Class: 24 bits = 16,777,216

Serial Number: 36 bits = 68,719,476,736

268 million companies can each categorize 16 million different p268 million companies can each categorize 16 million different products roducts and each product category may contain over 68 billion individualand each product category may contain over 68 billion individual items !! items !!


Repository

HEINZHEINZKETCHUPKETCHUP

(98) 0614141999999

Repository

HEINZHEINZKETCHUPKETCHUP

01.0203D2A.916E8B.0719BAE03C

PastPast

NowNow


One who says it can’t be done is often interrupted by someone doing it ! Elbert Hubbard, 1856-1915

RFID tagged Gillette razors at TESCO Store (Cambridge, UK)

Source: Colin Cobain, CIO, TESCO (www.tesco.co.uk)


• GIAI – Global Individual Asset Identifier

• GLN – Global Location Number

• SSCC – Serialized Shipping Container Code

• GTIN – Global Trade Item Number

• and now

• Global EPC – Global Electronic Product Code

EAN.UCC Keys to Data


96 bit EPC

Can uniquely number …

79,228,162,514,264,337,593,543,950,336

or about 8 x 1028 individual objects

or more than 1 million times all the grains of sand on earth!!


EPC Design

012

.

.

.

79,228,162,514,264,337,593,543,950,33479,228,162,514,264,337,593,543,950,33579,228,162,514,264,337,593,543,950,336

If only one number is used once …

One Big Database !!


Two numbers …

X.Y

Still somewhat large …

… 281,474,976,710,656

EPC Design


Three numbers

X.Y.Z

4,294,967,296 …

… about right ?

EPC Design


Version.X.Y.ZVersion.X.Y.Z

What if we add a version number ?

EPC Design


Version.X.Y.Z

Header

Domain Class

InstanceVersion

Manager Number Object Class

Serial Number

EPC Design


01.0203D2A.916E8B.0719BAE03C

Header 8 bits256

Domain 28 bits268,435,456

Class 24 bits16,777,216

Instance 36 bits68,719,476,736

96 bit EPC


Universal Identifier (UI)

Domain Identifiers (DI)

EPC can embed other standards


Header (Company) Prefix Item Reference Serial Number8 37-20 7-24 36

Partition4

Object Type4

96 bit EPC

Application Identifier

ExtensionDigit

EAN.UCCCompany

Prefix

Check Digit

Serial Reference


Unassigned6,…, 15

Other5

Location4

Load/Pallet3

Case/Shipping Unit2

Inner pack1

Item/Customer Unit0

NameObject Type

96 bit EPC Object Type Codes

Object type 4 bits x Partition 4 bits = 16 types


16 Million7241 Million6206

1 Million62016 Million7245

131,072517128 Million8274

16,3844141 Billion9303

102431016 Billion10342

12827128 Billion11371

AddressDigitsBitsAddressDigitsBits

ClassManagerPartition

EPC Design


Header EPC Manager Item Reference Serial Number2 14 20 24

ObjectType4

4

16

16,384 1,048,576 16,777,216

Item Reference is identicalto the GTIN Item Reference

64 bit EPC Design


Source: AIDC


Supply (?) Chain

Regional DC

Local DC

Manufacturer

Regional DC

Local DC

Local DC

Local DC

Retailer

Retailer

Retailer

Retailer

Retailer

Retailer

Retailer

Retailer


Information Flow

•Manual

•Slow

•Error prone

•Friction

•No value add

Data Entry

Manual CheckBar Codes

Material Flow

Supply Chain: Material vs Information Flow

DataData DataData

Manual CheckBar Codes


RFIDReader

RFIDReader

•Automated

•High integrity

•Fast

•Frictionless

Information Flow

Material Flow

Supply Chain Optimization: Real-Time Data

Illustration: Mark Dinning, DELL Corporation

ePCePC


Procter&

GambleBOUNTY CASE PALLET MFG

FACTORY

MFGDISTRIBUTION

CENTER

Gillette Mach III CASE PALLET MFGFACTORY

MFG DCChicago IL

SAM'S CLUBTULSA

UnileverLIQUID ALL

&DOVE SOAP

CASE PALLET MFGFACTORY

MFG DCSAM'S CLUBKANSAS CITY

DC

KraftsFoods

CHEESE SLICES &

MACARONI & CHEESE

CASE PALLET MFGFACTORY

MFG DC WAL-MARTDC

BENTONVILLE

Johnson&

Johnson

FEMININEHYGENE CASE PALLET MFG

FACTORYMFG DC WAL-MART

TULSA

MFG SKUs CASE PALLETSKUs

MFGFACTORY

MFGDC

RETAILERDC

RETAILSTORE

MIT Auto ID Center “Town Test”

Source: AIDC


PALLET

Procter&

GambleBOUNTY CASE PALLET

FACTORYCape

Girardeau Mo

MFG

DC

SAM'S CLUBKANSAS CITY

DC

SAM'S CLUBTULSA

MFG SKUs CASE PALLETSKUs

MFGFACTORY

MFGDC

RETAILERDC

RETAILER

1 1 0 1 1 0 0 1

High volume products in

pallets

Products with high volume at Sam's

Estimated 150 pallets per month

At manufacturer:Tag palletsWire 3 doorsWire 1 PML serverApplication software

Shipped directly from factory to retailer

At retailer wire:6 incoming doors2 transition doors1 DSD door2 shelves on retail floor1 PML serverApplication software

CHEP attaches tags for P&G pallets Pallet back to pool

VMI Note: P&G ships high vol products direct to WAL*MART

Tag and read pallets at mfg factory and/or DC:

Read pallets at Sam's Club through:

Incoming

Movement : staging area to retail floor

Pallet return to CHEP or disposal

400 RFID TAGS

30 READERS

2 PML SERVERS

MIT Auto ID Center Test : Phase One

Source: AIDC


J&J DC KRAFT FOODS DC GILLETTE DC

WAL-MART FOOD DEPOTCLARKVILLE, AR

WAL-MART DEPOTBENTONVILLE, AR.

Warehouse Retail Floor Staging Area Retail Floor

Unit shelves on retailer's floor

Retailer's check out

SAM'S STORETULSA OKLAHOMA

SAM'S DEPOTKANSAS CITY

WAL-MART STORETULSA, OKLAHOMA

P&G MFG FACTORYCAPE GIRADEAU , MO

P&G DCUNILEVER DC

palletsCASES

UNITS

MIT Auto ID Center Test : Product Flow

Source: AIDC


ONS on the net read via PML to translate EPC ONS on the net read via PML to translate EPC

Source: AutoID Center

Dumb chips with EPC, Smart net hosts ONS


EPC Network – The Building Blocks

EPCInformation

Service

Tags The data carrier. Identity number is programmed into the memory.

EPC

Connected to the chip. Could be traditional wire or coil or could be printed antennas using conductive inks.

ReaderThe data capture device; portable or fixed (installed), connected to a Savant or network.

SavantServers which act as local repositories for EPCs and associated information, and which support sophisticated, flexible middleware for serving PML queries.

The code carried by the carrier; the globally unique pointer for making inquiries about the item associated with EPC.

Antenna

ONS Object Name Service; the distributed resource that “knows”where information about EPCs is held (just like DNS).

Structure to allow structured querying and reporting concerning EPCs.

Source: EPC Global


4

Source: AIDC


Slow Moving Barriers ?

increased chip size greater functionality

reduced functionality(networking & software)

reduced chip size(handling small chips)

Source: AIDC


RFID: Low Cost ?

time

5

10

15

20

die

s ize

/ cos

t in

cent

s handling costSilicon: 4 US Cents/mm2

Source: AIDC


1 cent

5 cents

25 cents

50 cents

Total Cost

2020 ???0.3cent0.1cent0.1cent0.5 cents

2010 ??1 cent1 cent1 cent2 cents

2003 ?

200120 cents5 cents5 cents20 cents

When ?PackagingAssemblyAntenna(IC) Chip

Smart Objects: Road to Ubiquitous Tagging?

Plastic PrintedPrinted on Objects

< 0.1 cents ?


Layers

Class V tags Readers. Can power other Class I, II and III tags;

Communicate with Classes IV and V.

Class IV tags: Active tags with

broad-band peer-to-peer communication

Class III tags:semi-passive RFID tags

Class II tags: passive tags with additional

functionality

Class 0/Class I:read-only passive tags

Upw

ard

com

patib

ility

Dow

nwar

d fa

ilsaf

e

Source: AIDC


RFID Transponder : Tag

Source: AIDC


Cheap Chip

100μm

Source: AIDC


Cheap Chip ManufacturingSource: AIDC


• Redirection Service – acts as telephone book in reverse– principle of Domain Name Service (DNS)

ONSSource: AIDC


MDMMDM


• Language for describing physical objects– classification and categorization

PMLSource: AIDC

P


Industry Specific


Savant

Savant Savant

data readers machinessensors

Savant

Savant

“Store”

“Regional”

“National”

EPC OS: AIDC “Savant” (just another middleware)

Source: AIDC


Data Storage

Class Server

Soap Interface

Query Processor 1

Query Processor 2

Query Processor n

Soap Query Soap Response

UpdateSchedules

RetrieveSchedules

Class ID

Class

Task Manager

EPC OS SoftwareSource: AIDC


Reader

Savant

PML Service(EPC Database)

ONS(cache)

•Capture Events Data (tag and sensors)

•Simple Filters

•Report Data

•Manage Readers

•Higher Level Filters

•Track and Trace Serial Items

•Referencing Business Transactions

•Object Type Data (e.g. pallet/case/item)

•Instance -level EPC data (e.g. expiry date)

•Fine -grained access control policy implementation

•Local copy of frequently -used ONS data

•Registration for static and dynamic ONS

•Collaboration on asset tracking

Filtered event data (optional)

event data

Databases(ERP..)

Additional data

queries

updates provides data topoints to

Enterprise

Application(s)

•Transmit ePC data using radio frequency

•Transmit sensor dataTag Tag Sensor

EPCs Temperature,...

EPC Network Architecture - Inside the Firewall


queries

updates provides data topoints to

Savant

PML Service(EPC Database)

ONS (cache)

Filtered event data (optional)

event dataInternal

DB(ERP)

Additional dataBusiness Transactions

Company A

PML AccessRegistry

Enterprise

Applications

Internal Database

(ERP)

Company B

Enterprise

Applications

Static ONS:• converts an EPC into an internet address to locate a PML ServiceDynamic ONS • provides means to locate current and previous EPC Custodians for the purpose of track and trace, recall etc.

•Web service interface describing the capabilities and data accessible through each PML service to trading partners.

ONS

EPC Network Architecture - Outside the Firewall


EPC in IMS

P


EPC in Retail

P


EPC in Theft Prevention

P


EPC in Drug Anti-Counterfeit


EPC in Healthcare Track & Trace


EPC in Waste Management

P


EPC in Patient Monitoring


Telemetry : RFID + Sensors


Telemetry – Shelf Life

• 76 million foodborne illness

• 1.8 million deaths worldwide

• 325,000 hospitalizations in US

• 5000 deaths in US

• 91 million tons of food disposed to landfills in US

• 26% of US food supply

• 824 million ‘hungry’ per year


ntTRE

QktQ g

a

⎥⎥⎦

⎤

⎢⎢⎣

⎡−

−=∂∂ )(

1e

Variables

• Ea Activation energy• k1 Arrhenius constant• n Order of the reaction• T Temperature• Q Quality• t Time



⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜

⎝

⎛

−⎟⎟

⎠

⎞

⎜⎜

⎝

⎛−

=

tk

o

TgRaE

QtQe1

e)(



++

Name: Food QualityDescription: Food Quality based ArrheniusDeveloper: Natick Army LaboratoriesID: EPC: 010300908808BF60000000AAComp: $0.25 per monthType: AnalyticRate: 1 to 10,000 secAlgorithm:

+

+

++

Food Quality

++

Name: Activation EnergyDescription: Activation EnergySymbol: EaAccess: ReadID: EPC: 010300908808BF6000000102Class: ScalarType: FloatUnit: m=2 kg=1 s=-2 u=-1Default: 25000.0

++

Name: Arrhenius ConstantDescription: Arrhenius ConstantSymbol: k1Access: ReadID: EPC: 010200908238760000023877Class: ScalarType: FloatUnit: s=-1Default: 0.002

++

Name: TemperatureDescription: TemperatureSymbol: TAccess: ReadID: EPC: 010200908238760000023877Class: ScalarType: FloatUnit: k=1Default: 286.0

++

Name: QualityDescription: Food QualitySymbol: QAccess: WriteID: EPC: 010200907ABC8 60000012875Class: ScalarType: FloatUnit: s=-1Default: 100.0

++

Name: Order of ReactionDescription: Order of ReactionSymbol: nAccess: ReadID: EPC: 01020084191000001289731Class: ScalarType: IntUnit:Default: 1



T

n, k1, EaT

PML

Q ?



RFID Temperature Sensor in US DoD MRE Simulation


RFID Monitoring Perishables (MRE Simulation)


01010111010101010 0000000000000010 1010 01001000001001010101010010111111010

001000000000 00 1001 0101010 10010 10101000000000000000 1010 1 01010100 1 0 00001001010101 01001011111

INSPECT

DISPOSE

Class 1Assessment

RFID Monitoring Expiration Date (MRE Simulation)

DISPOSE

ISSUE


RFID : Current Issues

• Spatial capacity of 1 kbpsm2

• Continuous wavelength• Narrow dedicated spectrum• Data corruption by frequency collision• Passive transponders in manufacturing ?• Palet size vs passive tag range ?• Metal objects: spare parts ?• Universal standards ? (915MHz, 13.56MHz, 2.45GHz, 125KHz)


PASSIVE ULTRAWIDEBANDSolution in Search of Problems ?

disruptive technology ? de facto global standard ?

TagArray (UC Berkeley)

Old Version: Active UWB from MSSI, Robert Fontana


AM Radio SW Radio Garage TV 2-6 FM Radio TV 7-13 TV 14-69 Cordless Ph

0.5 1.7 30 40 54 88 108 174 216 470 806 902

GPS Cell Ph BluTh, b/g 802.11a Satel TV

1.61.2 1.8 2.1 2.4

IC

5.0 5.8 10.7 12.5

MHz GHz

125 KHz 433 MHz 860-930 MHz 2.45 GHz13.56 MHz

U L T R A W I D E B A N D


Solution in Search of Problems ?

• Wide spectrum (>960 MHz, 3.1-10.6 GHz, 22-29 GHz) • Spatial capacity 1000 kbpsm2

• Power 200 mW (802.11b ~500mW; 802.11a ~2000mW)• Data 0.1 – 1.0 gbps2 (802.11b ~0.006gbps2 or 6mbps2 )• Contender for BlueTooth replacement• 600 picosecond bursts (avoids multipath interference) • UWB+GPS+RTLS : innovative combination ?• UWB + narrow-band is catalytic for passive UWB tags


Source: Robert Fontana

UWB Pulse


Less Power RequirementLess Power Requirement


Source: Intel & Robert Fontana


UWB RFID TechnologyUWB RFID Technology

UWB encodes information as pulse of RF energyTiming of pulses is used to relay information

Continuous wave RF modulates data signals over carrier waves.

UltrawideUltrawide band is carrierband is carrier--less RFless RFAmplitude Amplitude

Modulation (AM)Modulation (AM)Frequency Frequency

Modulation (FM)Modulation (FM)

tt

Low power requirements (low battery drain, lower health risks)Low cost transmitter design (no need for separate baseband + RF stages)30 foot radius coverage at 100Mbps (longer for lower data rates)Demonstrated ability to support very high data rates (100Mbps and beyond)Immunity to interference (from other devices and multipath signals)Inherit security at the signal level (UWB is very difficult to detect or defeat)Ability to acquire accurate location information (resolutions < 1 foot)

UWB Characteristics


WLAN Application

UWB 802.11ABluetooth 802.11B

Throughput

Range

Power

Resolution

~100 Mb 6-11 Mb35-54 Mb~700 Kb

40 mw 500 mw1-1.7W30 mw

0.01-10 km 100 m~100m10 m+

~1 ft NANANA


Floor Activity Simulation

UWB as LPS Indoor “GPS”

Track customer traffic flow after they pick up an itemTrack customer inspection of items even if they don’t buyCheck activity by display type (not just by dept)Measure wait times by cashier

Security Monitoring in Restricted Areas (Airports Operations) ?

Source: Intel


Powerline data transferOrthogonal Frequency Division Multiplexing (OFDM)

ATTH (AIT to the home)

Coasian analysis + VAR-GARCHTransaction cost economicsROI

-Frequency agnostic readers-Reader efficiencies

-Software Radio (SDR) Readers -OFDM

Widespread Adoption

Passive UWB tagsUltrawide band + narrow bandPervasive Use Cases

CommentsWhat’s needed

RFID Made Difficult ?


WiFi 802.11b, RFID BlueTooth, IPv6

WiFi 802.11b, RFID BlueTooth, IPv6

E merge

Data (information)WWW, Internet

Data (information)WWW, Internet GPS, Portals, Voice

Browsers

GPS, Portals, VoiceBrowsers

Un-WireUn-Wire

Data Information Semantic SupraNet

Data Information Semantic SupraNet

Multi-hop/nano SensorsAgents, Semantic Tags

Multi-hop/nano SensorsAgents, Semantic Tags

2010

Everything that computes also communicates and routes

Everything that communicates also computes and routes

Everything that routes also computes and communicates

Where are you?Where are you?dERP, Wearables

802.16, Mesh, UWB Locate, Process, Context


Emerging Computing TrendEmerging Computing Trend

year

log

(peo

ple

per c

ompu

ter)

Streaming Informationto/from Physical World

Number CrunchingData Storage

ProductivityInteractive


Emerging Computer Class

year

log

(peo

ple

per c

ompu

ter)

Mainframe

Minicomputer

Workstation

PCLaptop

PDA???


Roadmap for Electronic Devices (minus CAEN)(minus CAEN)

101 100 10-1102

104

106

108

Chi

p C

ompo

nent

s

Size (μ)

1010

1012

1018

1014

1016

10-2 10-3

Classical Age

Historical Trend

SIA Roadmap2010

CMOS

19952000

2005

1970

1980

1990

4oK

Quantum Age

77oK

295oK

Quantum State Switch

Source: Horst Simon, LBL


INTERNETINTERNET

SOURC E

INTELDot 01Sensor

“Unwired” Sensor NetWireless MultiWireless Multi--hop Broadcast Meshhop Broadcast Mesh


INTERNETINTERNET

Database

Processed Data QUERY

SOURC E

INTELDot 01Sensor

“Unwired” Sensor NetWireless MultiWireless Multi--hop Broadcast Meshhop Broadcast Mesh

Application

INFO

RM

AT

ION

QUERY

QU

ER

Y

in-network processing

802.15.4ZigBee


MIT Project OxygenEmerging network nodes may be billions of embedded devices generating exabytes of data per second but is that information?


Context-Aware Computing

Network

Processing+

communication

Processing+

communication

Processing+

communicationProcessing

+communication

LocationRFID, UWB, GPS

Sensors

ActuatorsD2B

Resourceinformation

EnvironmentalContext

• Human-centric– “Finding” applications

• Embedded– Sensors & Actuators– Devices– Monitoring & Control

Source: Hari Balakrishnan, MIT LCS & EECS


Context-aware Services (Service Supply Chain)(Service Supply Chain)

• Zero configuration• Context-aware, speech-driven, location-based (CRICKET location system)• Resource discovery and secure info (INS Intentional Naming System)• Unconstrained, adaptive mobility (routing) to capture network context (MIGRATE)



Beacons(ceiling)

B

θSPACE=NE43-510ID=34COORD=146 272 0http://cricket.lcs.mit.edu

Project Oxygen: CRICKET

H21



Ultrasonic sensor AntennaRF module (rcv)

Atmel processor

Listener Beacon

RF module (xmit)

RS232 i/f

MIT CRICKET PROTOTYPESource: Hari Balakrishnan, MIT LCS & EECS

MOTES


Context-aware Resource Discovery : INS• Services advertise-register resources• Consumers make queries for services• System matches services and consumers

Problem: naming systems name by (network) locationsNames should refer to what (not where)Use expressive language (XML)[service = camera]

[building = NE43[room = 510]

Intentional Name

image

Lookup

camera510.lcs.mit.edu

Resolverself-configuration



Deeply Embedded Networks

• # nodes >> # people• sensor/actuator data• unattended, inaccessible• prolonged deployment• energy constrained• operate in aggregate• in-network processing• dynamic functions• network programmable

Source: David Culler, University of California at Berkeley and INTEL Research Lab at UC Berkeley


Vast Networks of Tiny Devices

• Internet built around dedicated devices carefully configured and stable– high-power wireless subnets, 1-1 communication between named computers

HERE ……..• every little node is potentially a router• work together in application specific ways• collections of data defined by attributes• connectivity is highly variable• must self-organize to manage topology, routing, etc• for power savings, radios may be off most of the time


http://pubs.acs.org/cgi-bin/article.cgi/nalefd/2004/4/i09/pdf/nl049080l.pdf

NanoLetters (2004) 4 1785-1788A Conducting Polymer Nanojunction Sensor for Glucose DetectionErica S. Forzani, Haiqian Zhang, Larry A. Nagahara, Ishamshah Amlani, Raymond Tsui and Nongjian TaoDepartment of Electrical Engineering and Center for Solid State Electronics ResearchArizona State University, Tempe, Arizona, USA and The Microelectronics and Physical Sciences Laboratory, Motorola, Tempe, Arizona, USA


Sensor Network @ Work

Light, Temp, Humidity, Barometer, Passive IR

www.greatduckisland.net

Source: David Culler, INTEL Research Lab at UC Berkeley


Open Platform

Small microcontroller

- 8 kb code, 512 B data

Simple, low-power radio

- 10 kb

EEPROM storage (32 KB)

Simple sensors

WeC 99“Smart Rock”

Mica 02

128 KB code, 4 KB data

50 KB radio, 512 KB Flash

Rene 00

Designed for experimentation

-sensor boards

-power boards

Dot 01

DARPADARPA DARPAINTEL

www.tinyos.net

Source: David Culler, INTEL Research Lab at UC Berkeley


In-network Processing in Network of “Motes”• Ad hoc sensor field of nodes• Each node knows only its own location (node id)• Neighborhood discovery (learns of “neighbors” and their locations)• Local Processing (light)

TinyDB

Topology

Source: David Culler, INTEL Research Lab at UC Berkeley and David Tennenhouse, INTEL Research


Wide-Area Broad-Coverage Services

Traditional Point-to-Point Internet EmbeddedNetworks


TinyOS Users

• US DoD• ALTARUM• BAE SYSTEMS• VIGILANZ SYSTEMS• PHILIPS• FRANCE TELECOM • INTEL• GE• GRAVITON• HONEYWELL• HP• BOSCH• SIEMENS• XEROX


InIn--operation Sensoroperation Sensor--based Monitoring for Diagnostics and Predictive Maintenancebased Monitoring for Diagnostics and Predictive Maintenance

• Trains pass sensor points at 80 mph• Predicts ‘if’ & ‘type’ bearing failure (>97% accuracy)

-2

-1

0

1

2

0 0.01 0.02 0.03 0.04t

Track basedmicrophone

Sensor DataSensor Data

ClusteringNeural Nets

Decision TreesState Transitioning

Smart Maintenance Intelligent Diagnostics


• Trains pass sensor points at 80 mph• Predicts ‘if’ & ‘type’ bearing failure (>97% accuracy)

-2

-1

0

1

2

0 0.01 0.02 0.03 0.04t

Track basedmicrophone

Sensor DataSensor Data


Product Design :

Agent-based Optimization from Sensor Data in Semiconductor Wafer Fabrication

Optimize cell temperature in order to optimize for desired refractive index.

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

5.5 0.000

0.6330.0010.010.1

110

1001000

10000100000

Erro

r

n2

k2

Optimize growing semiconductor films with ellipsometer sensor.

FilmSubstrate

Incident Reflected

d

film thickness

φ1

φ0

circular polarized lightelliptically polarized light


RFID Linked Biometrics & Nano-sensor Net

Blood Glucose Nano-sensors


Actions

Sensing

Model

Action Plan= Procedure

Patientat home care

Intelligent Real Time

Healthcare for Independent Living: Sense, then, RespondReducing the Cost of Old Age ?

Harvard-MIT Center for Integrated Medicine and Information Technology

Patient Specific

Framework withdecision support

Precision Remote Controlled

Real-time micro-statusnetworked, mobile


Confluence of Technologies

Embedded Systems

MEMS

Many devices monitor andinteract with physical world

Exploit spatial & temporalcoupling to physical world

Small, untethered processing,Storage and control

Mass-produced, low-power,short range, sensors & actuators

Networking

Coordinate, perform higher-level tasks

Self-organized, power-awarecommunication


The White House said the increased technology spending – mentioned by President Clintonduring last week's State of the Union address –could be used, for example, to create "intelligent agents" that roam the Internet collecting data.

AP News Service, 24 January 1999


Inference Technique

Forward Chaining

Pattern Matching

Key Feature

Data driven

Fires upon matching of set of criteria

Monitoring

Truth Maintenance (Retraction)

Exception handling and alarms

“What-if” reasoning

Dynamic Inferencing Scenario-based business rules

Backward Chaining Goal driven


INSURANCEINSURANCE

Point-of-Sale Underwriting

Claims Processing

Renewal Processing

Intelligent Policy Configuration and

Pricing

Eligibility Determination

Cross Selling

Fraud Detection

GOVERNMENTGOVERNMENT

Welfare Eligibility Determination

Regulatory Compliance

Tax Assessment

Entitlements and Benefits

Determination

Pension Plan Forecasting

Worker’s Compensation Claims

BANKING/ BANKING/ FINANCEFINANCE

MANUFACTURINGMANUFACTURING

Online MortgageUnderwriting

Credit Scoring

Portfolio Management

Cross Selling

Fraud Detection

Overdraft Authorization

SEC Regulatory Compliance

Risk Management

Transportation

Retail

Petroleum/ Oil & Gas

Health Care

Telecom

Pharmaceutical

Utilities

Parts Selection

Order Configuration

Production Planning/Routing

Production Scheduling

Maintenance and Labor Scheduling

Material Safety Data Sheets

Distribution Management

OTHER INDUSTRIESOTHER INDUSTRIES


IntuitionLogic

Rules

Inferencing

Rules-Based Systems

PatternRecognition

Association

Adaptive Pattern Recognition• Prediction Models

- Classification – data reduction- State-transition prediction - Recipe: given an input set, predict the outcome- Quality of Models- Measures for False Positives and False Negatives- Rank importance level of each input to the outcome- Principal Component Analysis – dimension reduction- Decision tree: transform relationships into rules- Global optimization- Statistical summaries/correlations

• Adaptive to changing environments • Able to deal with complex problems• Unlimited in the number of metrics that can be modeled• Accommodates both linear and non-linear relationships• Data driven – avoid human bias


Input dataInput data






Future EventFuture EventPredictionPrediction

State Transitioning


INPUTINPUT

Neuron: Non-linear Transfer Function

OUTPUTOUTPUT

Input dataInput dataInput dataInput dataInput dataInput data

OutcomeOutcomeOutcomeC a

use

Effe

ct

Patterns & RelationshipsPatterns & Relationships


CauseCause

EffectEffect

Training AlgorithmWeight

adjustment


OutcomeOutcomeOutcome

TrainingTrainingPredictionPrediction• Anticipate component failure• Replace part prior to failure• Preventive maintenance plan• Improve customer response• Reduce repair cycles• Support performance metrics• Better identify causes of problems• Learn to adapt to the environment



CauseCause

EffectEffect

Training / LearningAlgorithm

Weight

adjustment



PredictionPrediction• Anticipate component failure• Replace part prior to failure• Preventive maintenance plan• Improve customer response• Reduce repair cycles• Support performance metrics• Better identify causes of problems• Learn to adapt to the environment



LEARNLEARN

AUTONOMOUSAUTONOMOUS

COOPERATECOOPERATE

Smart AgentsSmart Agents

Collaborative Learning AgentsCollaborative Learning Agents

Collaborative AgentsCollaborative AgentsInterface AgentsInterface Agents


Advances in data routing emerging from study of Ants

Ant–based algorithms developed from swarm intelligence

X

A

PheromonePheromone


Advances in data routing emerging from study of Ants

Ant–based algorithms developed from swarm intelligence

X

A

PheromonePheromone

XA


X

A

A

A

X

X

Adaptive ?Adaptive ?


Replenishment Planning

Smart Shelf(RFID)

Interface AgentLow Inventory AlertLow Inventory Alert

File Sender

Actual Inventory

Planned & Actual Inventory

Planned Consumption & Replenishment

Planned Inventory

Filters, Logic

ERP DW

Backorder

PortalPortal

Inventory Early Warning Agent

Illustration: SAP AG


Smart Planning with Intelligent Objects

Distribution Center

Distribution Center

Distribution Center

Distribution Center

Store 1Store 1

Store 2Store 2

Store 3Store 3

Store n

PlantPlant

MANUFACTURER

RETAILER

3 days2 days

X days ?

Y days ?

REPLENISHMENT

RFID Data

Inventory

Consumption

AGENT

Information Agent

0

50

100

AGENT

Inventory Early Warning Agent

EVENT

ACTION


Data Agents collect ►DataMonitoring Agent triggers ►AlertInventory Management Agent executes ►Substitution

M2 can be substituted for SKU M1

Inventory of M2 is 2000

OOS Danger

Less chance of a stockout with substitution via agent actions

(M1 & M2)

Multi-Agent System

Source: SAP AG


Core Engine

Encapsulations of Application Logic (OR algorithms), Agents, DatEncapsulations of Application Logic (OR algorithms), Agents, Data, Context, Process Semanticsa, Context, Process Semantics


AAAA

AA

AAAA

ERPERP

““Personal AssistantPersonal Assistant””AgentsAgents

Agents as Agents as IntelligentInterface ManagersInterface Managers

AgentAgent--toto--AgentAgentCommunicationCommunication

AgentsAgents““behind the scenesbehind the scenes””

InterInter--applicationapplicationCommunicationCommunication

Web of Agents ? Web of Agents ?

Semantic TagsSemantic Tags –– SL TagsSL Tags

Adaptive ?Adaptive ?


Difference Engines Difference Engines (1950)

Source: Marvin Minsky, AI Lab, MIT


Source: From Neurons to the Brain

Basic Neural Circuits


8 8 Agents

8 corners of larger cube8 Agents repeated 8 times

8 corners of this cube1 corner = 1 Agent8 Agents connected

== 512512


512512 512512

512512

512512

512512512512

512512Agents interconnectedAgents interconnected

8 X 512 = 40968 X 512 = 4096


Repeat this cube-on-cube pattern 10 times (10 steps).

Supercube (810 = 1, 073,741,824) will contain over 1 billion Agents.

Each Agent in the original smallest cube (of 8 Agents) cancommunicate with 1 billion Agents (sources, variables) in 10 steps.

Link each Agent to 50 other Agents:Each Agent communicates with >15 billion Agents in 6 steps (506).

CocaCola can monitor nearly each RFID tagged unit case of its product. Real-time data can be collected by an Agent (Agency) in mere 6 steps for analysis (inventory, distribution, storage, transit, temperature). In 2004, CocaCola produced 19.8 billion unit cases.


Model

Distributed Agent Based Models


Distributed Agent Based Models


Where Artificial Intelligence Meets Natural Stupidity !!Where Artificial Intelligence Meets Natural Stupidity !!


Adaptive Adaptive AutonomicAutonomic

Autonomic Agent Architecture

Source: IBM Systems Journal 41 368 (2002)


XML Core

XML eXtensions

Languages and Open Standards


1969 General Markup Language (GML) - Charles Goldfarb, Ed Mosher, Ray Lorie

1971 Document Type Definition (DTD)

1975 Document Composition Facility (DCF)

1978 Standard General Markup Language (SGML) ANSI Initiative

1983 SGML Computer Graphics Association (CGA)

1986 SGML - International Organization for Standardization (ISO)

1989 HyperText Markup Language (HTML) - Tim Berners-Lee, CERN

1993 HTML Browser Mosaic - Marc Andreessen National Center for Supercomputing Applications (NCSA) University of Illinois

1996 eXtensible Markup Language (XML)World Wide Web Consortium (W3C) Initiative

1998 eXtensible Markup Language (XML)World Wide Web Consortium (W3C)

1999 XML-based Physical Markup Language (PML)RFID Object Description Language (AIDC, MIT)

2003 Ontology Working Language (OWL) DAML + OILDARPA Agent Markup Language + Ontology Inference Layer

Languages

Compiled by: David Brock


Development

• Individuals

• Academia

• Corporations

• Industry Consortia

• Government

• International Organizations


Explosion ……4MLAMLAMLAMLAMLAMLAMLABMLABMLACMLACMLACAPACS X12ADMLAECMAFMLAGMLAHMLAIMLAIMLAIFAL3ANMLANNOTEAANATMLAPMLAPPMLAQLAPPELARMLARMLASMLASMLASTMARMLARMLASML

ARMLARMLASMLASMLASTMATMLATMLATMLATMLAWMLAXMLAXMLAXMLAXMLBMLBMLBMLBMLBMLBMLBannerMLBCXMLBEEPBGMLBHTMLBIBLIOMLBIOMLBIPSBizCodesBLM XMLBPMLBRMLBSMLBCXMLBEEPBGMLBHTML

BiblioMLBCXMLBEEPBGMLBHTMLBIBLIOMLBIOMLBIPSBizCodesBLM XMLBPMLBRMLBSMLCMLxCMLCaXMLCaseXMLxCBLCBMLCDACDFCDISCCELLMLChessGMLChordMLChordQLCIMCIMLCIDSCIDXxCILCLTCNRPComicsMLCIMCIMLCIDS

CIDXxCILCLTCNRPComicsMLCovad xLinkCPLCP eXchangeCSSCVMLCWMICycMLDMLDAMLDaliMLDaqXMLDASDASLDCMIDOIDeltaVDIG35DLMLDMMLDocBookDocScopeDoD XMLDPRLDRIDSMLDSDDXSEMLEMLDLMLEADebXML

eBIS-XMLECMLeCoEcoKnowedaXMLEMSAeosMLESMLETD-MLFieldMLFINMLFITSFIXMLFLBCFLOWMLFPMLFSMLGMLGMLGMLGXMLGAMEGBXMLGDMLGEMLGEDMLGENGeoLangGIMLGXDGXLHy XMHITISHR-XMLHRMMLHTMLHTTPL

HTTP-DRPHumanMLHyTimeIMLICMLIDEIDMLIDWGIEEE DTDIFXIMPPIMS GlobalInTMLIOTPIRMLIXMLIXRetailJabberXMLJDFJDoxJECMMJLifeJSMLJSMLJScoreMLKBMLLACITOLandXMLLEDESLegalXMLLife DataLitMLLMMLLogMLLogMLLTSC XMLMAML

MatMLMathMLMBAMMISMLMCFMDDLMDSI-XMLMetaruleMFDXMIXMMLLMMLMMLMMLMoDLMOSMPMLMPXMLMRMLMSAMLMTMLMTMLMusicXMLNAMLxNALNAA AdsNavy DTDNewsMLNMLNISO DTBNITFNLMXMLNVMLOAGISOBIOCFODF

ODRLOeBPSOFXOILOIMOLifEOMLONIX DTDOOPMLOPMLOpenMathOffice XMLOPMLOPXOSDOTAPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLPMLP3PPDMLPDXPEF XMLPetroMLPGMLPhysicsMLPICSPMMLPNMLPNMLPNGPrintML

PrintTalkProductionMLPSLPSIQMLQAMLQuickDataRBACRDDlRDFRDLRecipeMLRELAXRELAX NGREXMLREPMLResumeXMLRETMLRFMLRightsLangRIXMLRoadmOPSRosettaNet PIPRSSRuleMLSMLSMLSMLSMLSAMLSABLESAE J2008SBMLSchemtronSDMLSearchDM-XMLSGML

SHOESIFSMMLSMBXMLSMDLSDMLSMILSOAPSODLSOXSPMLSpeechMLSSMLSTMLSTEPSTEPMLSVGSWAPSWMSSyncMLTMLTMLTMLTalkMLTaxMLTDLTDMLTEIThMLTIMTIMTMMLTMXTPTPAMLTREXTxLife

UMLUBLUCLPUDDIUDEFUIMLULFUMLSUPnPURI/URLUXFVMLvCalendarvCardVCMLVHGVIMLVISA XMLVMMLVocMLVoiceXMLVRMLWAPWDDXWebMLWebDAVWellMLWeldingXMLWf-XMLWIDLWITSMLWorldOSWSMLWSIAXMLXML CourtXML EDI

XML FXML KeyXMLifeXML MPXML NewsXML RPCXML SchemaXML SignXML QueryXML P7CXML TPXMLVocXML XCIXAMLXACMLXBLXSBELXBNXBRLXCFFXCESXchartXdeltaXDFXFormsXGFXGLXGMMLXHTMLXIOPXLFXLIFFXLinkXMIXMSGXMTPXNS

Compiled by: David Brock


Systems 2000

Adaptive Mobile eXtended Decision Systems 2020

TOOLSTOOLS

OR and Game TheoryLanguagesDistributed Artificial IntelligenceAutonomous AgentsSemantic WebGrid ComputingSimulationStreaming DatabaseClockspeed

DATA MOBILITYDATA MOBILITY

802.11b / WiFi802.11a, 802.11g, 802.16BlueToothMesh NetworksUltrawideband (UWB)Sensors (MEMS NEMS)GPS / RTLSIPv6, 4GRadio Frequency Identification

C O

N V E R

G E N

C E

C O

N V E R

G E N

C E


VMI ≈ JTID

• Vendor Managed Inventory (P&G)• Just-in-Time Distribution (Barilla)

Process names, context of words and their meanings, usage,differs with country, industry and host of other factors thatmay not be standardized reflecting one universal description.

SEMANTICS Adaptive ?Adaptive ?ProcessProcessContext Context


Relationships


Tim BernersTim Berners--LeeLeeSemantic Web Semantic Web

Shoumen Datta, MIT Forum for Supply Chain Innovation, School of Engineering <[email protected]>199www.w3c.org

Semantic Web BusSemantic Web Bus


SimulationSimulation

DML

DMP

ADP ADL

Host

ADP

DMP

•DML Data Modeling Language•ADL Automated Decision Language•DMP Data Modeling Protocol•ADP Automated Decision Protocol


Applications


Network

Past


Network + Data

Present


Network + Data + ModelsNetwork + Data + Models

Future ?


Distributed Interactive Simulation“A Template for Distributed Modeling”


Data Modeling Language (DML)

Model

Input Output


Data Modeling Language (DML)


DMP DMP

Data Modeling Protocol (DMP)


Input Command

Output Command

Automated Decision Language (ADL)


Automated Decision Protocol (ADP)


ADP

ADP

Automated Decision Protocol (ADP)


DATA

Architecture Standards Models Hardware Software Applications

Extract Intelligence from Real-Time Data to Feed (information) Processes


Model

Data Models


Data Models


value network complexity

need

for s

tand

ard

The more complex the network, the greater the need for standards.

Standards


Data Modeling Languages and ProtocolsDML – Data Modeling LanguageDMP – Data Modeling Protocol

Data Control Languages and ProtocolsADL – Automatic Decision LanguageADP – Automatic Decision Protocol

Standards: Data Project


•

•

•

•

•

•

•

••

•

•

Where is the ROI ?Where is the ROI ?

An Analogy from Quantum PhysicsAn Analogy from Quantum Physics


•

•

••

•

•

• •

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•


•

•

•

•

•

• •

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•


•

•

•

•

•

•

••

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

• •

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

••

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

••

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

• •

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

••

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•


•

•

•

•

••

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

• ••

•

••

•

••

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

• •

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•••

•

• •

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

••

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

••

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

• •

•

•

•

•

•

•

•

•

•

•

•

• •

•

•

•

•

•

•

•

•

•

••

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

••

•

•

•

••

•••

•

•

•

•

••

•

••

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•• •

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

• ••

•

••

•

•

••

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

• •

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

••

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

••

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

••

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

••

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

••

•

•

••

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

••

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

Young’s Double Slit ExperimentYoungYoung’’s Double Slit Experiment with Electronss Double Slit Experiment with Electrons

Dr. Akira Tonomura, Hitachi Research Laboratories

more innovations arise from borrowing and combining than

Documents