Particle Computer Programming - TECO – …cdecker/pub/Decker_Particle_Computer...Christian Decker, 2005 – Particle Programming 21 ConCom cont. • Flat model – Sentence imposes

Particle Computer Programming

Christian Decker TecO, University of Karlsruhe

October 12, 2005

Christian Decker, 2005 – Particle Programming

2

Personal Introduction

Christian Decker • Telecooperation Office (TecO),

University of Karlsruhe, Germany • 3rd year PhD student • Since 6 years affiliated w/ TecO • Research interests

– Software for ubiquitous computing systems

– Operating systems for wireless sensor nodes

• Various projects – AwareGoods, SmartShelf, Smart-Its, DigiClip, CoBIs


3

Co-Authors

• Manabu Isomura (KDDI Research) • Till Riedel (PhD student, TecO) • Emilian Peev, Philipp Scholl,

Thomas Morper (TecO student workers)

Emilian Philipp

Manabu

Till


4

What is a Particle Computer?

• Tiny wireless sensor node • Integrated in everyday objects • Enabling technology for Ubiquitous Computing

applications

Particle Computer (20 x 17mm)

Sensor data from a Particle Computer within a PDA application


5

Course Mission

To enable you to build complete applications using Particle Computers

uPart Sensor nodes & Network

Data processing

Application front-end

?

Data provider

Data reasoning

Action

Application


6

Objectives and Target Group

• Objectives – Insight into TecO‘s Particle Computer System – Sensor basics – Introduction in the Particle software tool suite and

programming

• Target Group – Student with interest in Ubicomp and Sensor

networks – Required programming experiences, e.g. Java,

C/C++


7

Course Outline

• Introduction • Particle System Architecture • Sensors • Data Processing • Software and Tools • Exercise


8

Introduction


9

A Brief History of Particle Computer

• Roots: EC project „Smart-Its“ (2001-2003) • Goal: Augment mundane everyday objects by

small embedded electronic devices to form digital relationships

• Results: wireless sensor platforms

Particle 1.01 (TecO)

BTNode rev2 (ETH)

DIY Smart-It (Lancs)


10

Particle Zoo

Sensor board SSmall • 3-axis acceleration, temperature, audio,

ambient and visible light sensor, force

Particle 2/29 – pPart • Freely programmable, customized RF protocol • 128KB program ROM, 512KB for data storage

zPart – ZigBee Particle • Freely programmable, onboard movement sensor • 128KB program ROM, 512KB for data storage

Blueticle • Bluetooth radio, Zeevo • Acceleration, temp., audio, light and force sensor


11

Particle Zoo cont.

uPart • Integrated wireless sensor node • Light, temperature, movement and

sensor sensor • Coin-cell powered

Your course platform


12

Particle Zoo Supplements

XBridge • Bridges Particle data to UDP network • Supports cell-of-origin location

system • Self-contained, i.e. no further

computer system required

USB Bridge • Bridges Particle data to UDP network • Requires laptop/desktop system


13

And there is more...

• Software for all platforms • Libraries and tools • New and upcoming devices

http://particle.teco.edu


14

Course Approach

1. Learn about the system

?

Application 5. Do your own exercises!

4. Learn about the tools

3. Learn about data processing

2. Learn about the sensors


15

System Architecture


16

Particle System Architecture

uPart

UDP Network

ParticleDB

Particle Analyzer

uPart uPart

Bridge

...

Particle Computer

Back end

uPart Router

uPart Router


17


uPart

UDP Network

ParticleDB

Particle Analyzer

uPart uPart

Bridge

... uPart Router

uPart Router

Flat architecture / no middleware


18


uPart

UDP Network

ParticleDB

Particle Analyzer

uPart uPart

... uPart Router

uPart Router

Flat architecture: All components are Particle Computer

Bridge


19

Flat Architecture – Consequence

• Messages have to be distributed to all components – Particle sensor nodes broadcast on RF – Bridge broadcasts on UDP

• Common communication language between all

components – ConCom


20

ConCom • Data encoded as tuples, strictly typed

– Type: 3 bytes, freely selectable – Length: 1byte, data length – Data: data to transmit

• Subject = first tuple, identifies application • Sentence = subject + [tuples]*

Leng

thD

ata

...

Subject Tuple Tuple

Type

A B C 1 1Le

ngth

Dat

a

Type

S T E 2 23 5


21

ConCom cont. • Flat model

– Sentence imposes no hierarchy – Complies to the architecture

• Composite semantic – Sentence can be extended by additional tuples – Meaning does not change

• Contextual semantic – Order of tuples may encode additional information

• Publish/subscribe model – Application subscribes on subject – Several application run parallel – Tuple type reuse – Cross-layer optimizations


22

The Big Picture

• ConCom is common communication language throughout the system • ConCom is used on all platforms, uParts, zPart, pParts, Blueticle …

no middleware required

ConCom

uPart

UDP Network

ParticleDB

Particle Analyzer

zPart pPart

... uPart Router

uPart Router

Bridge

ConCom

ConCom

ConCom

ConCom

ConCom


23

References

• A. Krohn, M. Beigl, C. Decker, P. Robinson, T. Zimmer, ConCom – A language and Protocol for Communication of Context, Technical Report ISSN 1432-7864 2004/19

• Decker, C., Krohn, A., Beigl, M., Zimmer T.

The Particle Computer System. IPSN Track on Sensor Platform, Tools and Design Methods for Networked Embedded Systems (SPOTS). Proceedings of the ACM/IEEE Fourth International Conference on Information Processing in Sensor Networks 2005, Los Angeles, USA


24

Know Your Hardware


25

uPart – Technical Details

• Speak “mü-part” • MCU: 12F675 at 4 MHz • Memory:

– Internal Flash 1.4 kByte, – Internal SRAM 64 Byte – Internal EEPROM 128 Byte

• Communication: Transmitter only – 869 Mhz / 315 Mhz – 2-FSK, ALOHA protocol – 15m indoor, up to 30m outdoor

• Sensors – Light, temperature, movement, voltage

• Power supply – Single CR1632 coin cell – Lasts for several weeks (~35s duty cycle, CR1632)

uPart 1/40


26

Sensing and Communication

• Both underlie a duty-cycle mechanism Operation phase • Sensor sampling

– Samples light, movement, temperature, voltage sensor

• Compression – Aggregates several consecutive samples

• Communication – Packet contains recent and a set of previous compressed sensor values

Sleep Sleep Sleep

Operation Operation Operation Operation

Time

Sleep >> Operation


27

System Layout

• This course will use uParts in single-hop communication distance.

ConCom

ConCom

ConCom

USB

USB Bridge


28

uPart Packet Format

• Header (6 bytes) – Version, field strength, sequence number – ID (1.2.3.4.x.x.x.x)

• Payload (max. 11+24=35 bytes) in ConCom • Data encodes

– configuration, i.e. sampling cycles, compression, sensor selection – recent uncompressed sensor values – compressed previous sensor information

Header Payload CRC16

AMP | lenght | data

Ref: http://particle.teco.edu/upart/ Protocols


29

uPart ID Scheme

• 8 byte ID = 1.2.3.4 + 4 byte significant ID

Ref: http://particle.teco.edu/upart IDs

id0

id1 id2 id3

• Format:1.2.3.4.id0.id1.id2.id3(id0..id3 in decimal)

• On the uPart: id0..id3 in hex • Example:

– uPart: 00 01 00 10 – Full ID: 1.2.3.4.0.1.0.16


30

uPart Configuration

• uPart are transmitting-only device • But, configuration possible via double usage of light

sensor on LCD or CRT • Applet generates black/white pattern encoding

configuration data • uPart ready to receive config

after bootup • Configuration options

– Sampling intervals = transmit rate – Compression – Sensor selection

Online: http://particle.teco.edu/upart uPart Config


31

Sensor Basics


32

Overview

uPart 1/40

Movement (digital)

Temperature (analog)

Light (analog)

PIC microcontroller • Analog-Digital converter (10bit ADC) • Digital I/O

Battery (analog)


33

Analog & Digital Sensors

• Analog sensors – Sensor output voltage encodes

the sensor value – ADC translates in digital value

• Digital sensors

– Sensor value encoded as “1“ and “0” – Example: PWM

MCU Sensor

GND

Vdd

Source: ADXL Datasheet


34

Movement Sensor

• Binary sensor, ballswitch (on/off state) • Motion detection, orientation dependent • No current, really low power sensor!

Encapsulated ball, Shake it, you hear it!

• count #states (on/off) • continous state length • on/off cycle

Signal (ideal) :

Switch opened Vdd Switch closed GND

GND Vdd


35

Movement Sensor Sampling

• 1 sample every 144ms (short wake-up) • Count “on”-state, min{#on, 255} • Reset when sending

Sleep

Send

Time Sample Count

Sample Count

… Reset Counter

min{#on, 255}


36

Compression

Compression coverage = 12 * Compression factor * transmit rate

Sleep

Send

Time min{#on, 255}

Send Send

min{#on, 255}

… min{#on, 255}

Send

3 2 1 0 7 6 5 4

average diff

Bit

12 byte compression ring buffer

Compression Coverage Time

average: step encoding, width 16

#avg.“on“counts = average * 16

diff = min{maxCounts–minCounts, 15}


37

Light Sensor

• TSL13, analog sensor, neutral to angular displacement • Sampled once per sampling cycle • Compression scheme like ballswitch

TSL13T Internals Source: TSL13 Datasheet

Photo Diode


38

Temperature Sensor

• TC1047A, analog sensor • Output voltage linear to temperature • Output independent from input voltage

Source: TC1047A datasheet

Source: TC1047A datasheet


39

Temperature Sensor

temperature = const. VTC1047_out = const. And

Vdd_MCU != const. sampleADC_TC1047 != const. I.e. (temp. = const.)

Problem: How to measure Vdd_MCU?

1024

* 1047___1047

TCADCMCUddoutTC

sampleVV =

MCUddTCADC

Vsample _

1047_

~1


40

Voltage Sensor

• ADC is powered by same source which should be measured (Vdd != const.)

• ADC samples reference diode in reverse operation

ADC

refdd sample

VV

1024*=

Diode’s reverse characteristic Source: LM385 Datasheet

Formula for computing the uPart supply voltage


41

Voltage Sensor cont.

Supply Voltage Accuracy

2

2,2

2,4

2,6

2,8

3

3,2

3,4

380 390 400 410 420 430 440 450 460 470 480 490

Sampled Values from ADC

Su

pp

ly V

olt

age

[V]

Exact uPart Implementation


42

Sensor Selection

• uPart configuration – Sampling intervals = transmit rate – Compression – Sensor selection

• Compression – applied only on selected sensors – Light compression like ballswitch – Temperature averaged

• Selection options – Sensor 1: Movement (fix) – Sensor 2:

• light sensor, OR • temperature sensor


43

Problems with Sensors

• Systematic problems – Sensors must be aligned to an application

• Orientation of sensors is crucial – placement of movement sensor is an interesting

research question!

• Computation and compression are lossy • Sampling intervals too large

– Inaccurate results – Missed events – Idea: increase sampling

(therewith transmit) rate Source: Albrecht Schmidt, Summer School 2005


44

Increase Transmit Rate...

Unslotted ALOHA Performance

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 10 20 30 40 50 60

Transmitting interval [s]

Pro

bab

ility

of

No

Co

llisi

on

5 nodes

10 nodes

15 nodes

20 nodes

25 nodes

50 nodes

75 nodes

100 nodes

150 nodes

200 nodes

250 nodesIncrease transmit rate


45

More on Sensors...

• uPart sensors – http://particle.teco.edu/upart/ Protocols – http://particle.teco.edu/upart/ Datasheet

• Albrecht Schmidt

“From Sensors to Context” Dagstuhl Summer School Summer School on Wireless Sensor Networks and Smart Objects 2005 http://www.vs.inf.ethz.ch/events/dag2005/program/lectures/schmidt-sensors-2.pdf


46

Data Processing

Introduction to Fuzzy Logic


47

Introduction

• Lotfi Zadeh (* 4. Februar 1921) • Founded theory of fuzzy sets and fuzzy

logic • Approach: describe a system

mathematically as similar as a human being would describe it linguistically

• Fuzzy Sets

– Elements are part of sets to a certain degree

• Fuzzy Logic – Operations on fuzzy sets – Superset of Boolean logic

Lotfi Zadeh (2004)


48

Motivating Example

Example: What is a convenient temperature?

Temp. ( °C)

Membership µ

1

0 20 25 Temp.

( °C)

Membership µ

1

0 20 25

0,6

Everything between 20°C and 25°C, but not 19,9°C.

20°C is to a certain degree a convenient temperature.


49

Fuzzy Set

• Elements: • Membership function: • Def. Fuzzy Set • Special case (Boolean Set): • Fuzzification: determine the truth value

Uu ∈[ ]1,0: →Uµ A

{ }]1,0[)(,|))(,(: ∈∈= uµUuuµuA AA

{ }1,0: →Uµ A

[ ]1,0:)( →uuµ A


50

Operations on Fuzzy Sets

• NOT: • AND: • OR: • Other functions for logic operators are

possible.

)(0.1)( uµuµ AA −=¬

{ })(),(min)()( vµuµvµuµ BABA =∧

{ })(),(max)()( vµuµvµuµ BABA =∨


51

Fuzzy Expert System

Process: 1. Fuzzification 2. Inference 3. Composition 4. Defuzzification

Expert System “reason about data“

If ... and ..., then ... If ... or ..., then ... ...

Membership Functions

Rules

Conclusion

Data


52

Fuzzification & Inference

• Fuzzification – Apply membership function on actual data value

• Inference – Determine truth value of the premise of each rule

• Ex. “If a is HIGH and b is LOW, then c is HIGH”

• Rule only fires, if

– Apply it to the conclusion of each rule, result is new fuzzy set • Ex. “If a is HIGH and b is LOW, then c is HIGH”

)()(:Pr bµaµµ LOWHIGHemise ∧=

0Pr >emiseµ

{ }],0[)(,|))(,(:)(premiseHIGHHIGH

c µuµUuuµuHIGH ∈∈=


53

Composition & Defuzzification

• Composition – Combination of all result fuzzy sets – Methods:

• MAX, i.e. OR operation across all sets • SUM, i.e. sum truth values point-wise

• Defuzzification – Compute crisp value from final fuzzy set – “inverse“-operation to fuzzification – Methods:

• Centroid, i.e. center of gravity of the set • Maximum, i.e. largest truth value


54

Example

• Input: Data from uParts in Badges on Ubicomp05 • Task: Find the sessions reasoned from light and movement data.

Light Membership Function

Movement Membership

Function

Session Membership

Function


55

Example Fuzzy Expert System

if light is low and movement is low then event certain; /* Rule 1 */

if light is high and movement is low then event none; /* Rule 2 */

if light is low and movement is high then event none; /* Rule 3 */

if light is high and movement is high then event none; /* Rule 4 */

defuzzification

input: input: output:

and

and

and

and

fuzzification

com

po

sition

Fig. Matlab


56

Example Results

Ubicomp Data

Fuzzy Expert System

Results


57

More on Data Processing...

• Kristof van Laerhoven’s Homepage http://www.comp.lancs.ac.uk/~kristof/

Readings • K. Van Laerhoven and H.-W. Gellersen. "Spine versus Porcupine: a Study in

Distributed Wearable Activity Recognition". In Proceedings of the eighth International Symposium on Wearable Computers, ISWC 2004, Arlington, VA. ISBN: 0-7695-2186-X; IEEE Press, 2004, pp. 142-150.

• K. Van Laerhoven, A. Schmidt and H.-W. Gellersen. "Multi-Sensor Context-Aware Clothing". In Proceedings of the sixth International Symposium on Wearable Computers, ISWC 2002, Seattle, WA. ISBN: 0-7695-1816-8; IEEE Press, 2002, pp. 49-57. PDF


58

Software and Tools


59

General Remarks

• Remember system architecture – All data is broadcasted on UDP – All components speak ConCom

• Default: Bridges broadcast on UDP 5555, i.e. application have to listen on 5555 in order to receive data in ConCom format.

ConCom

ConCom

USB

USB Bridge

ConCom

UDP Broadcast on 5555


60

libparticle

• Communication library in C for Windows and Linux

• UDP broadcast communication • ConCom tuple coding, • Powerful filters, e.g. ConCom, addresses etc. • Plain, addressed, acknowledged,

fragmented/file data transfer • Tunneling, i.e. connect two remote Particle networks via TCP • All PC applications base on libparticle

– E.g. consoleanalyzer, send2particle (included examples)

• ParticleAnalyzer, USBBridge, ParticleDB, OtAP, timeserver (TecO tool set)

Application

libparticle

PC / OS

Network


61

Libparticle – Example

int main(...) { ... sockd = p_socket_open(0 , 0, port); printf("Listening:\n"); for (;;) { fflush(stdout); packet = p_socket_recv(sockd, sockd); if (packet != NULL) p_describe_pkt(packet); } return 0; }

Extensive Examples can be found in the libparticle directory on the course CD


62

Libparticle – Language Bindings

• Previous: re-implemented libparticle for Java (TecO) and C# (Lancaster)

• Now: SWIG • SWIG = interface compiler, wraps C-

libparticle • object-orientied interface is defined

(.i files) • SWIG generates bindings for other

languages, e.g. Java, C#, Ruby, Python, PHP,…

• SWIG libraries handle type conversion (through typemaps)

App(Java)

libparticle (C)

App (C#)

Wrapper (C)

Wrapper (lang)


63

Libparticle – OO Structure


64

libparticleJava – Example

public class uPartVoltage { static {

/** load particleJava native library */ System.loadLibrary("particleJava"); }

public static void main(String[] args) { uPartVoltage voltage = new uPartVoltage(); while( true ) { System.out.println(voltage.getVoltage() );

}

} protected String getVoltage() {... }

... private float parseTuple(ParticleTuple pt) { ParticleTuple amp; long value;float voltage amp = packet.findFirstAcl( "amp" ); value = amp.getAclByte( 3 ); voltage = (float)value / 10; return voltage; } } // end class uPartVoltage


65

Language Bindings – Status

• Every language binding supports – C-error to exception mapping. – OO-Interface. – all functionality of libparticle.

• Java – C-array to java-array convenience mapping and vice versa.

• Csharp – no modification.

• Ruby – no modification.

• Php4: – minor mod for exception handling.


66

pComponents

• Collection of JavaBeans • Graphically compose Applications • Encapsulate common functionality:

– Scanner – scan for particles and their tuples. – Scope – graphical data plotter. – Console – text representation of network packets. – packetEditor – build packets and send them. – socketThread – easily receive packets

concurrently.

• Under development: – particleTemplate – template based filtering. – otap – Over-the-Air Programming. – otad – Over-the-Air Debugging.

libparticle as bean

Console bean


67

ParticleAnalyzer

• Real-time data analysis tool • Scanning and managing of Particle network • filtering, displaying and plotting of Particle data • Create/sending of Particle data • all functionality encapsulated in pComponents. • “clicked together” with Visual Editor of Eclipse.


68

ParticleAnalyzer cont.

Bean libparticle socket connected with Console bean

Bean based ParticleAnalyzer


69

Software Stack

App(Java)

libparticle (C)

App (C#)

Wrapper (C)

Wrapper (lang)

PC / OS

Network

App (Ruby)

App (PHP) pComponents

App (C)

ParticleAnalyzer

Systems and Toolkits


70

uPartLoggerSystem

libparticle (C)

PC / OS

Network

MySQL

Apache WWW

logger

Client

uParts

Display

uPartLogger System


71

Equip4j Toolkit for Particles

• Equip4j: Distributed component based software framework (EQUATOR project, UK)

• Equip4j-Application: set of functional software components connected through event exchange mechanism

• Development: graphical composition of components, definitions of events exchange path, rapid approach

• Component Repository: controlling webcams, record audio, TTS, neural networks, etc.

• Particles enabling components

– Representation: ParticleFactory, ParticleInstance – ConCom Parser: ParticleDemultiplexer, StringSplitter – Feedback: ParticleSender (for pParts, zParts, no uParts)


72

Equip4j for uPart

• uPart specific components – UPartDBConnector: fetches, exports, replays data from a

database – UPartAmpParser: interprets a data set

Parsing

Representation of uPart

Network & Filter

USB Bridge


73

More Software...

• Particle PC software – http://particle.teco.edu/software/index.html – More software, tools + docs

• uPart website

– http://particle.teco.edu/upart – Specifications, protocols, uPartConfig


74

Software on CD

“4 Quick Steps to Success”


75

Prerequisites

• Software for Win2000 SP4, WinXP SP2

• Java 1.5 required (on CD) • Install and connect the USB

Bridge on your computer • Deactivate virtual network

cards, e.g. from VMWare • Start your uPart, place battery

correctly

USB Bridge

Insert Battery CD: <Drive>:\index.html


76

Quick Step 1: Logging

• ready-to-run Apache/PHP/MySQL installation

• Extract the .zip file,

follow the instructions • Run the uPart logger • Go to http://localhost/

and plot uPart data

CD: <Drive>:\uPartLoggerSystem_1_bundle\ + Install.txt


77

Quick Step 2: Equip4j Toolkit

• ready-to-run Equip4j installation

• Extract the .zip file,

follow the instructions • Start-up the toolkit • Create the

ParticleFactory + watch data comming in

CD: <Drive>:\Equator4uPart_2_bundle\ + Manual.pdf

Drag & Drop


78

Quick Step 3: Dump Your Data

• Within Equip4j, create the uPartDBConnector

• DataExporterGUI will

come up automatically • Fetch(1) and export(2)

uPart data to a textfile

CD: <Drive>:\Equator4uPart_2_bundle\ + Manual.pdf

1

2


79

Quick Step 4: Use Excel

• Import the data dump in Excel • Select the voltage field • Plot the values in series Success! 2,6

2,7

2,8

2,9

3

3,1

3,2

3,3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


80

JFS2

• Free Fuzzy Expert System • programming language JFL • Combines traditional

programming and fuzzy logic

• Supports informal descriptions, e.g. rule of thumbs, example values, etc.

• Interface to gnuplot, C compiler

CD: <Drive>:\JFS2\ + Quick Intro

title "Stopping a bicycle"; operators and min precedence 30; or max precedence 20; whileop max; ... input distance "Distance to stop point" metres defuz avg; speed "Speed" speed_dom defuz avg; output brake "brake force" percent defuz avg; adjectives distance near 5; distance medium 17.5; distance far 30; ... program if speed is slow and distance is near then brake is low; if speed is slow and distance is medium then brake is none; if speed is slow and distance is far then brake is none;


81

Additional Material

• libparticle, pComponents • Programming examples + documentation • ParticleAnalyzer • USB bridge software and drivers • Eclipse 3.1.1, JDK1.5 • Tools: Ant, JUnit, SWIG

CD: <Drive>:\index.html


82

Exercises


83

Vision: uParts everywhere


84

Exercise

• Build the AwareOffice – Recognize typical office activities – Support users in their activities

• Sub-projects – Annotation of whiteboard activity – Environmental control – Meeting detection – Encourage activities – Document management – Meeting Annotation


85

Programming

This is your part!

Data from the office environment


86

Development Process

• Bottom up approach

?

Application

1. Deploy uParts on people, places, things

2. Record their data

3. Construct a fuzzy expert system

4. Implement, run, test and evaluate your system

5. Document your approach and put your results on a website

uPar

t N

etw

ork

Dat

a pr

oces

sing

A

pplic

atio

n fr

ont-

end


87

Development Process + Activities

Attach uParts Collect Data Review Data Find rules

Time

Replay your data + apply rules

Put application togehther

Run application

Time

Document places

Draw Membership Functions

Report results, Code

Convert in C Write Equip Component

Document Code!


88

Tool Chain Logging & Reviewing

Export data

Design Fuzzy Expert System

Compile Fuzzy System to C code

Run the System live

Put your results online

uPartLogger System

Equip4j UPartDBConnector

JFS2

Test and Evaluate

public class TemplateBeanInfo extends SimpleBeanInfo {

protected Class beanClass = Template.class;

public PropertyDescriptor[] getPropertyDescriptors() {

try {

return new PropertyDescriptor[] { // an input - get and set

new PropertyDescriptor("input", beanClass, "getInput", "setInput"), // an output - get only new PropertyDescriptor("output", beanClass, "getOutput", null) } catch (IntrospectionException e) { throw new Error(e.toString()); } } }

Build Equip Component

Equip4j

Put everything together

WWW

Start here


89

In the unlikely case, that...

• Nothing works – Determine how far the data are propageted within the tool chain – Use the ParticleAnalyzer to check the network (Do you see AMP

packets?)

• Performance is low due to Equip4j – Wrap Equip components as standalone application

• Documentation is incomplete – Go to http://particle.teco.edu, http://particle.teco.edu/upart

• Nothing helps... – Use the support, http://forum.teco.edu, [email protected]


90

Exercise – Annotate Whiteboard Activities

• Goal: A picture log of your whiteboard writings • Tasks

– Attach uParts on pens, sponge and optional whiteboard – Record uPart data during the typical activities like writing, playing

w/ pens, erasing – Select and describe appropriate situations for taking a picture – Shoot pictures automatically in those situation and summarize

them in a log, e.g. website

• Questions – Is there a difference in the detection reliability when using the

system with and without the additional input from the uPart(s) on the whiteboard?

– What are possibilities to avoid the writer in the line-of-sight of the camera and whiteboard?


91

Exercise – Environmental Control

• Goal: Room controller and recorder • Tasks

– Mount uParts on objects, e.g. windows, doors, chairs, tables etc., – Record sensor values and reflect object status, e.g. website – Detect people entering the room and document them by a

camera, results can be compiled on a website/weblog – Alert unusual situations, e.g. open window during night

• Questions – What are the criteria for your object selection? – What (alert) situations can you detect? – How reactive is your system? Time difference between activity

and detection


92

Exercise – People Activity

• Goal: Personal fitness trainer for office workers • Tasks

– Attach uParts on people, document those places+orientation – Record people’s activities, e.g. movement levels – Display activities of single extremities, e.g. on a website – Select and describe activities you want to recognize – System should request people to practise and prove the exercise

by documenting it automatically, e.g. Text2Speech, Camera

• Questions – What are good places for mounting sensors? – What are the criteria for the activity selection? – How reliable is the activity detection? Real vs. system


93

Exercise – Meeting Detection

• Goal: Detect and display spontaneous meetings • Tasks

– Attach uParts on chairs and record their movement information – Define the meeting and its parameters, e.g. number of persons, – Detect these meetings and display the state on a electronic

doorplate, e.g. a website – Test your system on false positives, e.g. What happens when

the room is cleaned up?

• Questions – What is the smallest meeting group your system can detect? – What is the reliability of your system? – What are measures to avoid false positives?


94

Exercise – Document Management

• Goal: Electronic document containers • Tasks

– Attach uParts to paper documents – Support following activities

• Loader: “load” the electronic version if paper document is moved in a projector beams

• Security rules: describe and implement security rules, e.g. if document is put in a bag.

• Forget-me-not: reminder, if a document is probably forgotten, e.g. not moved for a time

• Questions – Can you recognize patterns for the loader activity? – What is the rate of false positives and false negatives for the

security activity? – What may be other conditions indicating a forgotten document?


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Exercise – Meeting Annotation

• Goal: Meeting log / blog via audio + video • Tasks

– Attach uParts on chairs, tables, pens, windows, people – Define and describe situations within a meeting, where annotation

would be useful Split here in two teams

• Record audio + video periodically

• Select in post processing valueable scenes

• Record audio + video only if situation was detected

• Questions – Which approach is more reasonable? Why? – What may be necessary, e.g. sensors, to improve the system?


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Final Test (optional)

• Build the AwareOffice, i.e. let all your applications run in parallel

• Question: What happens?


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References

• TecO, “Impromptu – Creating Ad-hoc Pervasive Computing Environment” Video at at Pervasive 2004 in "Advances in Pervasive Computing", ISBN 3-85403-176-9, pp. 377-381, Vienna, Austria. (http://www.teco.edu/~zimmer)

• Equip examples at TecO:

http://particle.teco.edu Software


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Thank you for your attention.

Documents

Particle Computer Programming - TECO – …cdecker/pub/Decker_Particle_Computer...Christian Decker, 2005 – Particle Programming 21 ConCom cont. • Flat model – Sentence imposes