Wireless Sensor Network (WSN) for Smart Industries

Abstract
It is forecast that the number of wireless sensor network nodes will reach approximately 120 million units in 2010, with the overall shipment value arriving at about US$15.0 billion. Major application markets are expected to comprise commercial, household, industrial applications, and automatic measurement. Commercial applications of wireless sensor network mainly lie in monitoring, energy saving, and security. Home appliance automation is the focus of household applications, in coordination with environment monitoring, energy saving, and security applications. Industrial applications include machine monitoring, energy saving, and process optimization. Automatic measurement refers to the automatic reading of electricity, water, and gas meters etc. This report analyzes the current applications and development of wireless sensor network technologies, covering smart living, environmental monitoring, automatic measurement, healthcare, and positioning.

Market Opportunity:
• The global total industrial WSN market will be worth $4.6 billion in 2011
• Process control/automation will be worth 22% of the total WSN market at this time
• Two thirds of the revenues will be for new markets, or emerging application areas that are limited today due to cost of wiring.
Markets & drivers: Drivers and inhibitors on the WSN market opportunity in each of the following solutions: process/automation, machine health, tank monitoring, emissions, environmental, and structural
Market forces analysis of each of the following target markets: Chemicals/petroleum products, electronics/electrical equipment, food & beverage, machinery, transportation & plastics, metals, minerals, oil, gas & mining, paper products, pharmaceuticals/labs/medical equipment, power generation & transmission, water & wastewater. In-depth data from an survey with 108 leading industry experts on targeted applications and markets, current development status, products and services, WSN inhibitors, 5-year market sizing, ASPs, etc. Technology dynamics including survey results from the industry experts on current adopted radios, frequencies, current standards situation, and standards supported

Market sizing (2007-2011):
• Equipment forecasts by unit (nodes and gateways) and by revenue in each target market for each of the solutions above
• WSN services forecasts in each solution/target market
Value system:
• In-depth analysis and research data on 62 industrial WSN companies including component suppliers, module makers, systems manufacturers, and backend systems/services providers
• Product segmentation
• Disruption & sustainability charts
• Technology dynamics: protocols and frequencies supported, development capabilities, integration strategies, applications, and services
• Profiles: offerings, partners, financials
• Intellectual property: Key WSN patents/patent holders for each product segment
End user survey
• Interviewee position and industry
• Current networking automation technologies used
• Current wireless users and applications
• Vendor selection criteria
• Technology details (radios, frequency used, etc.)
• Planned wireless applications
• Inhibitors
• WSN system preferences
• Rankings on most likely wireless applications
Market Opportunity:


• The global total industrial WSN market will be worth $4.6 billion in 2011
• Process control/automation will be worth 22% of the total WSN market at this time
• Two thirds of the revenues will be for new markets, or emerging application areas that are limited today due to cost of wiring.
Markets & drivers: Drivers and inhibitors on the WSN market opportunity in each of the following solutions: process/automation, machine health, tank monitoring, emissions, environmental, and structural
Market forces analysis of each of the following target markets: Chemicals/petroleum products, electronics/electrical equipment, food & beverage, machinery, transportation & plastics, metals, minerals, oil, gas & mining, paper products, pharmaceuticals/labs/medical equipment, power generation & transmission, water & wastewater. In-depth data from an survey with 108 leading industry experts on targeted applications and markets, current development status, products and services, WSN inhibitors, 5-year market sizing, ASPs, etc. Technology dynamics including survey results from the industry experts on current adopted radios, frequencies, current standards situation, and standards supported

Market sizing (2007-2011):
• Equipment forecasts by unit (nodes and gateways) and by revenue in each target market for each of the solutions above
• WSN services forecasts in each solution/target market
Value system:
• In-depth analysis and research data on 62 industrial WSN companies including component suppliers, module makers, systems manufacturers, and backend systems/services providers
• Product segmentation
• Disruption & sustainability charts
• Technology dynamics: protocols and frequencies supported, development capabilities, integration strategies, applications, and services
• Profiles: offerings, partners, financials
• Intellectual property: Key WSN patents/patent holders for each product segment
End user survey
• Interviewee position and industry
• Current networking automation technologies used
• Current wireless users and applications
• Vendor selection criteria
• Technology details (radios, frequency used, etc.)
• Planned wireless applications
• Inhibitors
• WSN system preferences
• Rankings on most likely wireless applications

Registration details

THE DEAD LINE FOR REGISTRATION AT 10/11/2009 FOR THE FIRST POINT

ADVANTAGES OF THE FIRST POINT:


THE FIRST TEAMS CAN UPLOAD THIRE PROJECT'S LOGO.
THEY CAN UPLOAD THE CONTACT PEARSON'S INFORMATION FOR THERE PROJECT.
THEY CAN UPLOAD THERE SPONSER'S LOGO FOR ONE MONTHE.


contact site mangment:
fell free to send your comment to sasahdy@yahoo.com

GET A SPONSER

Telecommunications in Egypt شركات اتصالات مصرية






Name: 3w net cafe(cyber cafe)
Phone: 02/3035734, Fax: 02/3021105
Email: farah@starnet.com.eg
Address: 53 lebanon st.
City: (Any)
Remarks:
Manager: Ayman zien
Contact Person: Omar
internet egypt , egypt telecome , it egypt , egypt internet


Name: A2Z-NET
Phone: 2022736651/2 Fax: 2022736653
Email: ahmedra@usa.net
Address: 27 Makram Ebied St, Nasr City
City: (Any)
Remarks:
Manager: Azza El-Nagar
Contact Person: Ahmed Abdallah
internet egypt , egypt telecome , it egypt , egypt internet
Name: Access
Phone: 02/3057007 Fax: 02/3450170
Email: amrh@access.com.eg
Address: 9, Ahmed Orabi St., - Mohandessin
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Access
Phone: 03/5222218 Fax: 03/5447709
Email: mwagdy@access.com.eg
Address: 83, Sedy Gaber St., - Sidi Gaber
City: ALEX
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Access Accepted Co.
Phone: 02/2619346-0123738022 Fax: 02/2619346
Email: infoco@egyguide.org
Address: 46 Ali Amin St. - Nasr City
City: CAIRO
Manager: Ahmed Nabil Farahat
Contact Person: Youssef Shaban
internet egypt , egypt telecome , it egypt , egypt internet
Name: AccessMe
Phone: 202/01015772 Fax: 202/4173220
Email: tarek.hosny@accessme.com
Address: 52 Al Thawra St. , Heliopolis, Cairo
City: (Any)
Remarks:
Manager:
Contact Person: Eng. Tarek Hosny
internet egypt , egypt telecome , it egypt , egypt internet
Name: Alam Al Computer & Internet
Phone: 02/4019234 Fax: 02/4019234
Email:
Address: 22, A, Obour Bldgs. - Nasr City
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Alexnet
Phone: 03/4845001 Fax: 03/4845004
Email: sales@alexnet.com.eg
Address: 5, Ahmed Orabi St., - Manshia
City: ALEX
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: AsuVet (Future Center)
Phone: 088/314657 Fax: 088/314657
Email: info@asuvet.com.eg
Address: 22 Ahmed Ali Aloba Street
City: (Any)
Remarks:
Manager: Dr. Tarek Amin
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: B E C - On Line
Phone: 066/345556 Fax: 066/388883
Email:
Address: El Masagrya Building Shark
City: PORT SAID
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: British Computer Systems
Phone: 02/2904074 Fax: 02/2718780
Email: elkassify@bec.com.eg
Address: 41, El Shahid Abd El Moneim Hafez St., - Heliopolis
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: caironet
Phone: 02/5254041 Fax: 02/5254041
Email: info@caironet.net
Address: 115 el salam tower Mist Helwan rood. Maadi
City: (Any)
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: charle egypt
Phone: 02/4518821 Fax: 02/4518821
Email: wemaster@charle_club.zzn.com
Address: 8 Shams Din St,zytoon
City: CAIRO
Manager: Ayman Refaie
Contact Person: MR.Salah
internet egypt , egypt telecome , it egypt , egypt internet
Name: Click Internet
Phone: 02/7371312 Fax: 02/7350298
Email: azdeen@click.com.eg
Address: 12 A, Hassan Sabry St., - Zamalek
City: CAIRO
Remarks:
Manager:
Contact Person:


internet egypt , egypt telecome , it egypt , egypt internet
Name: Collection
Phone: 02/7370157 Fax: 02/7368986
Email:
Address: 15 A, Mohamed El Maraashly St., - Zamalek
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Compu - Net 2000
Phone: 02/2975102 Fax: 02/2975102
Email: campunet2000@geganet.com
Address: 17, Aziz El Din Mohamed Atiya St., - Heliopolis
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Compu Castle Co.
Phone: 0402242109 Fax: 0402242109
Email: info@compu-castle.com.eg
Address: Shokry El Kowatly ST.
City: (Any)
Remarks:
Manager: Eng.Mohamed Farag
Contact Person: Eng.Mostafa Saleh
internet egypt , egypt telecome , it egypt , egypt internet
Name: Computer & Communication Center
Phone: 050/370602 Fax: 050/370603
Email: ccccafe@hotmail.com
Address: 10, Omar Ebn El Khatab St., - Mansoura
City: DAKAHLEYA
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Computer World
Phone: 02/4040308 Fax: 02/4046850
Email: info@topsites.cc
Address: 5, Abo El Attahia St., - Nasr City
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Contact
Phone: 03/4247680 Fax: 03/4247681
Email: contact@contact.com.eg
Address: 33, Dr, Ismail Helmy St., - Semouha
City: ALEX
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet


Name: Cool-Net Cafe
Phone: 03/5431561 Fax:
Email: coolnet1@cns-egypt.com
Address: 7 Asdekaa Bldg. street no.4 - Mostafa Kamel Buildings
City: (Any)
Remarks:
Manager: Dr.Raouf Eskandar
Contact Person: Ossama-0105047279,Sameh-0105753229
internet egypt , egypt telecome , it egypt , egypt internet
Name: Data Express
Phone: 03/4190019 Fax: 03/4190190
Email: dexpress@dataxprs.com.eg
Address: 50, Moustafa Kamel Sq. - Semouha
City: ALEX
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Data Express
Phone: 03/4274324 Fax: 03/4274324
Email: mserry@dataxprs.com.eg
Address: 50, Moustafa Kamel Sq. - Semouha
City: ALEX
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Datum
Phone: 02/2903501 Fax: 02/2903527
Email: info@mailer.datum.com.eg
Address: 51, Beirut St., - Heliopolis
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Datum IDS
Phone: 2903501 Fax: 2903527
Email: mokady@datum.com.eg
Address: 51 beirut street
City: (Any)
Remarks:
Manager: Mohamed El Kady
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: DATUM International Data Systems
Phone: 02/2903501 Fax: 02/2903527
Email: autoid@datum.com.eg
Address: 51 Beirut St. Heliopolis 11341, Cairo, Egypt.
City: (Any)
Remarks:
Manager: Dr. Hakam Kanafani
Contact Person: Hany Fekry
internet egypt , egypt telecome , it egypt , egypt internet
Name: Delta Net
Phone: 050/334729 Fax: 050/367651
Email:
Address: 1, Salah El Din El Ayoubi St., - Mansoura
City: DAKAHLEYA
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Delta Net System
Phone: 047/399015 Fax: 047/399015
Email:
Address: 12, Borg El Zeraiyeen
City: KAFR EL SHEIKH
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: EgSys
Phone: 02/4179783 Fax: 02/4179783
Email: service@eg-sys.com
Address: 1,Ramo Buildings - El- Nasr Rd. Nasr city
City: (Any)
Remarks:
Manager: Hisham Farid
Contact Person: Ahmed Elhami
internet egypt , egypt telecome , it egypt , egypt internet
Name: EgyNile
Phone: 02/3050723 Fax: 02/3026383
Email: info@egynile.com
Address: 20 lebanon st. - Mohandeseen
City: (Any)
Remarks:
Manager: Sherif AbdelNabi
Contact Person: Amr Fouad
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egypt Gate
Phone: 02/2675895 Fax:
Email:
Address: Flat 302, 11 Soliman El Tamawy st., behind Sheraton Heliopol
City: (Any)
Remarks:
Manager: Eng. Ayman Fathy
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egypt Network
Phone: 050/526654 Fax: 050/521985
Email: ahmed@egyptnetwork.com
Address: 24, Om Kalthom St., / Talkha - Mansoura
City: DAKAHLEYA
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egypt Network
Phone: 050/526654 Fax: 050/526654
Email: sales@egyptnetwork.com
Address: 24 om klthom St. Talkha
City: (Any)
Remarks:
Manager: Mr.Ahmed Ibrahim
Contact Person: Hazem El-Menawi
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egypt On line
Phone: 02/3954111 Fax: 02/3954999
Email: info@egyptonline.com
Address: 34, Adly St., - Downtown
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egypt Telcom
Phone: 03/4863363 Fax: 03/4855245
Email:
Address: 8, El Falaky St., Raml Station
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egyptdata
Phone: +20122149095 Fax:
Email: info@egyptdata.com
Address: http://www.egyptdata.com
City: CAIRO
Manager: info@egyptdata.com
Contact Person: info@egyptdata.com
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egyptian Information Services
Phone: 02/2679006 Fax: 02/2679005
Email: mail@eis.com.eg
Address: 6 A, Moustafa Refaai St., - Heliopolis
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: EgyptNetwork
Phone: 050/526654 Fax:
Email:
Address: 24 Om Kalthom St./Talkha/Dakhlia(behind talkha central offic
City: (Any)
Remarks:
Manager: Eng.Ahmed Ibrahim
Contact Person: Eng.Hazem El-Menawi
internet egypt , egypt telecome , it egypt , egypt internet
Name: Egytel ISP
Phone: 03/4806777 Fax: 03/4807999
Email: info@egytel.net
Address: 5 adeeb eshaaq , raml station , alexandria ,egypt
City: (Any)
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Energy Telecom
Phone: 03/4855245 Fax: 03/4807999
Email: energyco@egytel.net
Address: 5, Adeeb Ishak St., - Raml Tsation
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet


Name: Energy Telecom
Phone: 03/4806777 Fax: 03/4807999
Email: mail@egytel.net
Address: 5 Adeeb Ishak ,Ramleh Station
City: (Any)
Remarks:
Manager: Mr: Amr Nassar
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Energy Telecom
Phone: 03/4806777 o Fax: 03/4807999
Email: info@egytel.net
Address: 5 Adeeb Ishak St., Ramleh Station
City: (Any)
Remarks:
Manager: Mr. Amr NASSAR
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: ETSnet
Phone: 02-3015900 Fax: 02-3459405
Email: malzouk@etsnet.com.eg
Address: 193 Sudan st , Mohandiseen , Giza .
City: (Any)
Remarks:
Manager: Mohammed Al-Zouk
Contact Person: Mohammed Al-Zouk
internet egypt , egypt telecome , it egypt , egypt internet
Name: EZ_NET internet-cafe
Phone: 02/2837663 Fax:
Email: ezz_alden@hotmail.com
Address: Al-fardous st. from ahmed said st. abasia - cairo
City: (Any)
Remarks:
Manager: Ahmed Taha
Contact Person: Ahmed taha & eng. Yasir Taha
internet egypt , egypt telecome , it egypt , egypt internet
Name: Gega Net
Phone: 02/4149700 Fax: 02/4149800
Email: info@gega.net
Address: 84, El Thawra St., Almaza - Heliopolis
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Ghazi.net
Phone: 203-5806041 Fax: 203-5806042
Email: info@ghazi.net
Address: Masjed Ehedayah str., Boulkly
City: (Any)
Remarks:
Manager:
Contact Person: www.ghazi.net
internet egypt , egypt telecome , it egypt , egypt internet
Name: Global Net Internet Service Provider
Phone: 03/3958981 Fax: 03/3958417
Email: info@globalnet.com.eg
Address: 29, El Nabi Danial St., / El Ghonemey Mogamaa - Downtown
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: GlobalNet
Phone: 4912889/4913 Fax: 4958981
Email: abounar@globalnet.com.eg
Address: 29 El-Nabi Danial St. El Ghonemey Bldg. 6th Flour
City: (Any)
Remarks:
Manager: Ayman Al Kabani
Contact Person: Nasser AbouNar
internet egypt , egypt telecome , it egypt , egypt internet
Name: hinet
Phone: 050/353115 Fax: 050/3727
Email: asalem@hinet-eg.com
Address: 3 mahmoud samy
City: (Any)
Remarks:
Manager: abdalla salem
Contact Person: abdalla salem
internet egypt , egypt telecome , it egypt , egypt internet
Name: I A A - Internet Advanced Access
Phone: 03/5448826 Fax: 03/5448827
Email: info@iaa.com.eg
Address: 18, Syria St., - Roushdy
City: ALEX
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: IAA Communications & Internet Services
Phone: 03/544-8826 Fax: 03/544-882
Email: info@iaa.com.eg
Address: 18 Syria St. - Roshdi
City: (Any)
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: In Touch Communications Services
Phone: 02/3376407 Fax: 02/3376480
Email: sales@intouch.com
Address: 3, Mossadek St., - Dokki
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: In Touch Communications Services
Phone: 02/3311800 Fax: 02/3376480
Email:
Address: 3, Mussadak St., - Dokki
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Infinity
Phone: 3802264 Fax: 38053227
Email: info@infinity.com.eg
Address: 31,Road 6,Maadi
City: (Any)
Remarks:
Manager: Eng.Mohamed wael Rafea
Contact Person: Mrs.Zahraa Rafea
internet egypt , egypt telecome , it egypt , egypt internet
Name: International Electronic Center ( I E C )
Phone: 02/3028100 Fax: 02/3055434
Email: postmaster@iec.egnet.net
Address: 11 Abu El Mahasen El Shazly Sq. - Agouza
City: CAIRO
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Internet Advanced Access (IAA)
Phone: (203)5448826 Fax: (203)5448827
Email: info@iaa.com.eg
Address: 18 syria street roshdi
City: (Any)
Remarks:
Manager: Wael Elnozahi
Contact Person: Wael Elnozahi
internet egypt , egypt telecome , it egypt , egypt internet
Name: Internet Egypt
Phone: 02/7962882 Fax: 02/7949611
Email: info@ie-eg.com
Address: 2 Simon Bolivar Sq. Garden City
City: (Any)
Remarks:
Manager: Dr. Essam Hosny
Contact Person: Dr. Essam Hosny
internet egypt , egypt telecome , it egypt , egypt internet
Name: Internet Egypt
Phone: 064/330228 Fax: 064/223733
Email: info@ismailia.ie-eg.com
Address: 649, Coca Cola St.,
City: ISMAILIA
Remarks:
Manager:
Contact Person:
internet egypt , egypt telecome , it egypt , egypt internet
Name: Internet Fayoum
Phone: 084/348508 , Fax:
Email: eslam@internetfayoum.net
Address: Mohamed Sabry El-Bekbashy St, Fayoum
City: (Any)
Remarks:
Manager: Eng : Ashraf Ramadan
Contact Person: Acc :Eslam Koliep
internet egypt , egypt telecome , it egypt , egypt internet





Copyright © 2009 دليل شركات مصريه , مص

TRY TO LOG ON

أبحاث جامعة تكساس الشمالية
http://www.tsgs.unt.edu/graduation_process/
أبحاث جامعة رودز
http://www.ru.ac.za/library/theses/
أبحاث جامعة فلوريدا
http://gradschool.rgp.ufl.edu/etd/index.html
أبحاث جامعة ولاية كارولينا الشمالية
http://www.lib.ncsu.edu/etd/
أبحاث معهد ماساشوستس للتكنولوجيا
http://thesis.mit.edu/
أبحاث معهد واركستر للبوليتيكنيك
http://www.wpi.edu/Pubs/ETD/
أبحاث و أطروحات و رسائل جامعة شتوتغارت-ألمانيا
http://elib.uni-stuttgart.de/opus/d…lish/index.html
أطروحات مؤسسة كروز
http://www.crewes.org/cgi-bin/searc…CREWES%20Theses
الأطروحات و الرسائل الإلكترونية في فيغرو
http://viva.lib.virginia.edu/etd/sample.html
أطروحات و رسائل جامعة فيرجينيا في الإنسانبات
http://e====.lib.virginia.edu/ETD/ETD.html
أطروحات و رسائل جامعة كونكورديا
http://www2.curf.edu/~crfetd/
أطروحات و رسائل فيرجينيا تيك
http://etd.vt.edu/
برنامج الرسائل و الأطروحات الأسترالية
http://adt.caul.edu.au/
رسائل دكتوراه جامعة أوبسالا-السويد
http://publications.uu.se/theses/
رسائل و أطروحات جامعة تينيسي
http://physio1.utmem.edu/ETD/
المشروع الإلكتروني لأبحاث جامعة واترلو-كندا
http://www.lib.uwaterloo.ca/ETD/
مشروع جامعة غرب فيرجينيا للأطروحات و الرسائل الإلكترونية
http://www.wvu.edu/~thesis/
مشروع رابطة أوهايو
http://www.ohiolink.edu/etd/
مكتبة أبحاث جامعة أيوا الرقمية
http://etd.lib.uiowa.edu/etd.html
مكتبة الرسائل و الأطروحات الجامعية
http://www.theses.org/
مكتبة الشبكة الرقمية للرسائل و الأطروحات
http://www.ndltd.org/

EUROPE UNIVERSITIES

تدوينات القسم ‘جامعات أجنبية’
جامعة أوكسفورد
فبراير.05, 2009 جامعات أجنبية أضف تعليقك
http://www.ox.ac.uk

جامعة أوكسفورد من أهم الجامعات العالمية ، ترتيبها العاشر عالمياً ، والموقع يفتح على فهرس جيد حسب الأقسام والموضوعات …
جامعة لندن
فبراير.02, 2009 جامعات, جامعات أجنبية أضف تعليقك
http://www.lon.ac.uk/

هي تجمع لتسع عشر كلية مستقلة ومشهورة ، وعدد من المعاهد الجامعية والأنشطة..مما يجعلها مرنة في المواضيع التي يمكن للطالب الدراسة فيها
جامعة مدينة لندن
فبراير.02, 2009 جامعات, جامعات أجنبية أضف تعليقك
http://www.city.ac.uk/

من الجامعات البريطانية ، تقع قريباً من لندن المدينة وتحوي 23835 طالباً ، من 156 بلداً ، وعمرها أكثر من مائة عام
الدليل العالمي للكليات الطبية
يناير.24, 2009 برامج ومشاريع WHO, جامعات, جامعات أجنبية, فهارس مواقع أضف تعليقك
الدليل العالمي للكليات الطبية
وهو دليل تم تحديثه حتى نهاية عام 2007 ، وترعاه منظمة الصحة العالمية
دليل الجامعات العالمي
يناير.23, 2009 جامعات, جامعات أجنبية, فهارس مواقع أضف تعليقك
دليل الجامعات - مصنفة حسب البلاد
فهرس المؤسسات التعليمية في معظم دول العالم ، وهو عمل فردي تم تطويره ، ويحوي مئات العناوين الإلكترونية للجامعات
الكيمياء الحيوية والبيولوجيا الجزيئية
يناير.23, 2009 جامعات أجنبية أضف تعليقك
قسم الكيمياء الحيوية والبيولوجيا الجزيئية - كلية الطب - جامعة سينت لويس
جامعة بوسطن
يناير.20, 2009 جامعات أجنبية, مكتبات أضف تعليقك
جامعة بوسطن
دار نشر جامعة كمبريدج البريطانية
يناير.20, 2009 جامعات, جامعات أجنبية, مجلات, مكتبات أضف تعليقك
دار نشرجامعة كمبريدج البريطانية
تنشر الكتب العلمية والجامعية منذ 450 عاماً ولها موقع مباشر على الإنترنت للمجلات

مواقع مفيدة

http://www.ucla.edu/ جامعة كاليفورنيا
http://www.columbia.edu/ جامعة كولومبيا
http://www.uchicago.edu/ جامعة شيكاغو
http://faculty.ksu.edu.sa/ جامعة اوهايو
http://www.stanford.edu/ جامعة استانفورد
http://www.harvard.edu/ جامعة هارفرد
http://www.tcnj.edu/ جامعة نيوجيرسى



جامعات كندية
http://www.liv.ac.uk/ جامعة ليفربول
http://www.ox.ac.uk/ جامعة اوكسفورد
http://www.york.ac.uk/ جامعة يورك

Embedded Systems

Contents of Course
1. Embedded Systems Introduction
2. Software Introduction 7. System Components 10. Models
3. Real-Time Models 8. Communication
4. Periodic/Aperiodic Tasks
5. Resource Sharing
9. Low Power Design
11. Architecture
Synthesis
Swiss Federal 4a - 2
Institute of Technology
Computer Engineering
and Networks Laboratory
Software and
Programming
Processing and Hardware
Communication
6. Real-Time OS
12. Model Based Design

Interactive MATLAB Technical Kit
450 MB | ISO File | Video Tutorials
MATLAB® serves as the foundation for all MathWorks products. It includes a programming language and tools for algorithm development, data analysis, data visualization, and numeric computation.

Complete this form today to receive an interactive technical kit loaded with product demos and webinars, plus data sheets for MATLAB and Signal Processing Toolbox, the Technical Computing brochure, and the MathWorks Product Overview.

This kit includes the most popular MATLAB recorded presentations with Q & A sessions led by MATLAB experts.




Download Links:
http://rapidshare.com/files/154929341/matlab.part1.rar
http://rapidshare.com/files/154930149/matlab.part2.rar
http://rapidshare.com/files/154938865/matlab.part3.rar
http://rapidshare.com/files/154938950/matlab.part4.rar
http://rapidshare.com/files/154930333/matlab.part5.rar

http://rapidshare.com/files/78483939...CD1.part01.rar
http://rapidshare.com/files/78483940...CD1.part02.rar
http://rapidshare.com/files/78483941...CD1.part03.rar
http://rapidshare.com/files/78483942...CD1.part04.rar
http://rapidshare.com/files/78483943...CD1.part05.rar
http://rapidshare.com/files/78491193...CD1.part06.rar
http://rapidshare.com/files/78491195...CD1.part07.rar
http://rapidshare.com/files/78491197...CD1.part08.rar
http://rapidshare.com/files/78491198...CD1.part09.rar
http://rapidshare.com/files/78491199...CD1.part10.rar
http://rapidshare.com/files/78493060...CD1.part11.rar
http://rapidshare.com/files/78493061...CD1.part12.rar
http://rapidshare.com/files/78493063...CD1.part13.rar
http://rapidshare.com/files/78493064...CD1.part14.rar
http://rapidshare.com/files/78493065...CD1.part15.rar
http://rapidshare.com/files/78542022...CD1.part16.rar
http://rapidshare.com/files/78542023...CD1.part17.rar
http://rapidshare.com/files/78542025...CD1.part18.rar
http://rapidshare.com/files/78542026...CD1.part19.rar
http://rapidshare.com/files/78542028...CD1.part20.rar
http://rapidshare.com/files/78666955...CD1.part21.rar
http://rapidshare.com/files/78666957...CD1.part22.rar
http://rapidshare.com/files/78666958...CD1.part23.rar


CD 2


http://rapidshare.com/files/78671266...CD2.part01.rar
http://rapidshare.com/files/78671267...CD2.part02.rar
http://rapidshare.com/files/78671269...CD2.part03.rar
http://rapidshare.com/files/78671270...CD2.part04.rar
http://rapidshare.com/files/78671271...CD2.part05.rar
http://rapidshare.com/files/78675855...CD2.part06.rar
http://rapidshare.com/files/78675856...CD2.part07.rar
http://rapidshare.com/files/78675857...CD2.part08.rar
http://rapidshare.com/files/78675858...CD2.part09.rar
http://rapidshare.com/files/78675859...CD2.part10.rar
http://rapidshare.com/files/78679167...CD2.part11.rar
http://rapidshare.com/files/78679168...CD2.part12.rar
http://rapidshare.com/files/78679169...CD2.part13.rar
http://rapidshare.com/files/78679170...CD2.part14.rar
http://rapidshare.com/files/78679171...CD2.part15.rar
http://rapidshare.com/files/78683276...CD2.part16.rar
http://rapidshare.com/files/78683278...CD2.part17.rar
http://rapidshare.com/files/78683280...CD2.part18.rar
http://rapidshare.com/files/78683281...CD2.part19.rar
http://rapidshare.com/files/78683282...CD2.part20.rar
http://rapidshare.com/files/78774167...CD2.part21.rar
http://rapidshare.com/files/78774168...CD2.part22.rar
http://rapidshare.com/files/78774170...CD2.part23.rar
http://rapidshare.com/files/78774171...CD2.part24.rar

مرسلة بواسطة alfateh في 03:01 م 0 التعليقات
VTC C Programming Tutorial
download: http://www.megaupload.com/?d=NFOEPSB3
مرسلة بواسطة alfateh في 02:53 م 0 التعليقات

Wireless Sensor Networks: Signal Processing and Communications Perspectives by: Ananthram Swami, Qing Zhao, Yao-Win Hong, Lang Tong en
download:

Implementing the concept of Product-Driven Control using Wireless Sensor

Implementing the concept of Product-Driven Control using Wireless Sensor
Networks: some experiments and issues
David Gouyon*, Michael David*
* Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039 CNRS Nancy-Université
Faculté des Sciences et Techniques, BP 239, Vandoeuvre-lès-Nancy Cedex.
david.gouyon@cran.uhp-nancy.fr; michael.david@cran.uhp-nancy.fr
Abstract: In the dynamically moving context of mass-customization of products, new manufacturing
control architectures, based on the consideration of highly distributed, autonomous, adaptable and
efficiently cooperating units integrated by a plug-and-operate approach, seem to be efficient alternatives.
Amongst them, the concept of a product-driven distributed control promotes an active role of the product
in its own manufacturing. This paper focuses on the possibilities to implement this concept on a case study
using wireless sensor networks.
1. FROM INTEGRATED TO AGILE
MANUFACTURING CONTROL
Advances in the use of Information Technologies in
manufacturing systems give manufacturers an opportunity to
promote make-to-order business models and mass
customization of products (Da Silveira et al. 2001). Facing
this wide range of customized customer orders impacts the
whole set of enterprise information and control systems (Nof
et al. 2006), which integration capability has to be improved
according to the Enterprise Integration Capability Model
(Hollocks et al. 1997) (EICM Fig. 1), in a dynamically
moving context.
Adaptable Intelligent system
Interoperable Distributed system
Visible Integrated system
Rigid Hierarchic system
Fragmented Fragmented system
Fig. 1. Enterprise Integration Capability Model
Standards, as the IEC/ISO 62264 (ISO 2003) promoted by
the MESA (Manufacturing Enterprise Solutions Association,
http://www.mesa.org), the ISA (Instrumentation, Systems,
and Automation Society, http://www.isa.org) and the ISO
(International Organisation for Standardisation,
http://www.iso.org), enable manufacturing enterprise-control
system integration from the business level to the process level
in order to meet industry-led Business-to-Manufacturing
issues (Morel et al. 2003) (Fig. 2a). In this context,
Manufacturing Execution Systems (MES) ensure information
flow synchronic gateway between enterprise and shop floor
control systems and diachronic integration between execution
activities (service flows). The main issue is then to ensure
consistency of information and product flows.
A possible alternative, in order to reach the ‘interoperable’
level of EICM, is to put into question the
hierarchical/integrated vision of the enterprise-wide control
for a more interoperable or intelligent one by postulating the
customized product as the ‘controller’ of the manufacturing
enterprise resources (McFarlane et al. 2003, Morel et al.
2005) (Fig. 2b). The product, seen as a good by
manufacturing systems, and as information and service
supplier by business systems, ensures consistency between
physical and informational flows.
Plant-control system integration
Enterprise-control
system integration
Service Flow
Good Flow
a)
Product
Flow
Plant-control system
Interoperability
Plant-control system
Interoperability
Good Flow
Service Flow
Enterprise-control
system interoperability
Plant-control system
Interoperability
material data
data information
b)
Products
c)
Enterprise-control
system agility
Enterprise-control
… system agility
Plant-control
system
agility
Plant-control
system
… agility
Fig. 2. From Integrated to agile manufacturing
Another alternative (Fig. 2c), as promoted by the IMS
community, leads to the development of new architectures
based on the consideration of highly distributed, autonomous,
adaptable and efficiently cooperating units integrated by a
plug-and-operate approach, as done in multi-agent (Marik &
Lazansky 2006) and Holonic Manufacturing Systems (Deen
2003). Such an approach is also currently studied by the
European Project “Pabadis-Promise”, which aims at
hal-00321465, version 1 - 15 Sep 2008
Author manuscript, published in "17th IFAC World Congress, Séoul : Korea, Republic of (2008)"
DOI : 10.3182/20080706-5-KR-1001.3154
extending the idea of distributed control to an innovative
architecture which incorporates both resource and product
(http://www.pabadis-promise.org). Emerging infotronic
technologies embedded into product-driven control
(McFarlane et al. 2003) bring more or less research results
closer to actual deployment: Radio Frequency IDentification
(RFID), wireless networking, modern PLC and industrial PC
support of multi-agent systems…
This paper focuses on the possibilities to implement the
product-driven control concept with such infotronics
technologies. After a description in section 2 of the concept
of product-driven control, a comparison is made in section 3
between RFID tags and Wireless Sensor Networks (WSN)
motes to foresee which product intelligence levels can be
implemented. Section 4 presents a case study on which
experiment are being made with WSN motes.
2. PRODUCT-DRIVEN CONTROL
As the work presented in this paper is mainly focused on the
implementation of a product-driven control, this part aims
first at describing the concept.
2.1 Intelligent versus smart product
Considering an active role of the product leads to give it a
form of technical intelligence (Karkkainen et al. 2003),
which corresponds, according to (Wong et al. 2002), to:
1 Possess a unique identity,
2 Be capable of communicating effectively with its
environment,
3 Be able to retain or store data about itself,
4 Deploy a language to display its features, production
requirements etc.,
5 Be capable of participating in or making decisions
relevant to its destiny.
In function of these points, two levels are defined in Wong et
al. 2002:
- Level 1 Product Intelligence allows a product to
communicate its status (form, composition, location, key
features), i.e. it is information oriented. Level 1
essentially covers points 1 to 3 of the intelligent product
definition above.
- Level 2 Product Intelligence allows a product to assess
and influence its function (e.g. self-distributing inventory
and self-manufacturing inventory) in addition to
communicating its status, i.e. it is decision oriented.
Level 2 therefore covers points 1 to 5 of the intelligent
product definition above.
From an operational point of view, things can be very
different because it seems to be difficult to implement
directly into smart products all aspects of product
intelligence. At this time, much embedded devices have
neither enough processing power nor the ability to
communicate all the required information for the
manufacturing. For these reasons, some other cases can be
envisaged if active entities reside in computers and are
remotely linked to physical products and machines. Indeed,
some multi-agent manufacturing systems are already
implemented in real industrial environment (McFarlane et al.
2003), but there are some constraints, related for example to
the reliability of RFID: successful read rate is not yet 100%,
and for this reason, the system may not be fully observable.
In such an approach, the product is considered as central to
the automation rationale, and is logically provided with
information, decision and communication capabilities in
order to make it active in the scheduling and the execution of
its manufacturing operations (point 5 of Wong et al. 2002).
The system is then said « product-driven ». Holonic
Manufacturing Systems (HMS) constitute a repository to
formalize this concept of product-driven control.
2.2 Holonic Manufacturing Systems
Koestler (Koestler 1967) introduced the concept of the
Holon, which is an entity capable of functioning as a whole,
while simultaneously acting as a part of a whole in a
hierarchically ordered system. In other words, a Holonic
system is a combination of an heterarchical system with
centralised elements. Based on this concept, the IMS
community, especially in the area of Holonic Manufacturing
Systems (Valckenaers 2001, Deen 2003, Leitao & Restivo
2006) promotes conceptual architectures, which tend towards
providing manufactured product with an intelligent
behaviour. These HMS (Babiceanu & Chen 2006) are
distributed systems which consider holons, which can be
autonomous production units, cooperating to make products
in a dynamically reconfigurable environment (McFarlane et
al. 2003). In the HMS reference architecture PROSA (Van
Brussel et al. 1998), types of holons are resource holons,
order holons, staff holons and product holons, this last
concept showing explicitly the active role of products.
A very interesting point with HMS is that Chirn and
McFarlane evaluated that this approach can provide higher
reconfigurability and modularity when facing series of design
changes (Chirn & McFarlane 2005).
2.3 Product-Driven Automation
Following conceptual guidelines of HMS, the approach used
in this work focuses on the design of a product-driven
distributed control system (Fig. 3) (Pétin et al. 2007), which
is based on the cooperation between:
- product controllers which control the manufacturing
routes according to a scheduled list of operations the
product has to undergo; these controllers are specific for
each product occurrence in order to take into account their
customization,
- resource controllers which ensure correct execution of
transport and transformation operations and provide the
product controllers with accurate reports; control
flexibility relies on tuning call parameters of the
functional objects which coordinate and control the
elementary operations, or on downloading specific control
policies embedded into products.
hal-00321465, version 1 - 15 Sep 2008
Requests from products / Reports from resources
Product
control
Product
control
Product Material flows
Resource
Control
Resource
Control
Resource
Control
Product/Process
Information flows
Fig. 3. Product-driven control architecture
This cooperation consists in the exchange of requests of
operations (noted RQ) emitted by product controllers to
resource controllers, and reports of operations (noted RP)
emitted by resource controllers to product controllers.
The definition of these controllers are founded, on the one
hand, on the modelling of the manufacturing system
capabilities which describe the system topology and the
manufacturing operations performed by each resource, and,
on the other hand, on the modelling of product requirements
in terms of the operations it has to undergo. Such controllers
can be automatically and formally written by the use of the
product-driven control synthesis, as proposed by Pétin et al.
(2007). This synthesis is out of the scope of this paper which
focuses on the implementation aspects of the product-driven
control.
3. TWO IMPLEMENTATION TECHNOLOGIES
Many technologies can be tried to implement the concept of
product-driven control. Amongst them, this part aims at
comparing RFID tags and Wireless Sensor Networks nodes
possibilities, as given by vendors in technical descriptions.
3.1 RFID tags
RFID corresponds to an automatic identification technology
which relies on the remote reading and writing of information
on electronic tags (also called RFID tags or transponders)
(Finkenzeller 2003). RFID tags are at least composed of a
chip and an antenna. In general, the chip contains a processor,
a memory and a radio transmitter (Fig. 4).
Radio Processor
transmitter Memory
Antenna
Fig. 4. Overview of an RFID tag structure
Some cheaper tags, which are the most used, are said
“passive” because they have no internal power supply, do not
contain an integrated circuit. They can be used for discrete
identification. Many applications in product tracking,
inventory systems and libraries can be found (see for example
http://www.rfidjournal.com).
3.2 WSN motes
Emerging infotronics technology, as advances in
microelectronics and wireless communications, have recently
enabled the design of very tiny sensors. Such autonomous
sensors nodes embed power supply, sensing, data processing,
and wireless communication components (Akyildiz et al.
2002) and are used to build Wireless Sensor Networks
(WSN). They are commonly called ‘motes’ (Fig. 5). With
their capacities, motes can sense their physical environment,
receive messages via the wireless network, and even react by
making a decision or sending messages.
Power supply (power management)
Communication
unit
Processing unit
& memory Sensing unit
(wireless network
protocols) (OS & algorithm) (filtering and signal
adapting)
Fig. 5. Functionnal view of mote components
WSN can be found into numerous military, environmental,
human centric, robotics or logistics applications (Arampatzis
et al. 2005).
3.3 Implementation of product intelligence with tags or motes
Both technologies present interesting capacities which could
enable a more or less direct implementation of the concept of
product-driven control into a physical product.
With the help of the literature and the description given by
vendors about RFID (Finkenzeller 2003) and WSN (Akyildiz
et al. 2002) technologies, Table 1 summarizes the abilities
presented in technical descriptions of passive RFID tags,
active RFID tags, and WSN motes to implement the various
aspects of the product technical intelligence defined in
(McFarlane et al. 2003) and presented in section 2. A passive
RFID tag seems to be able to implement Level 1 product
intelligence, while an active RFID tag containing a processor
or a WSN mote seems to be able to implement Level 2
product intelligence.
Implementing product-driven control implies that, between
products and resources, communications can effectively
occur at each time. While the RFID technology needs a direct
communication between tags and antennas (in this case,
communications are limited by the existing infrastructure),
ad-hoc organisation of WSN motes can be used to propagate
messages. Such an ad-hoc organisation seems to be more
flexible (as architecture, one bridge can be enough). For these
reasons, WSN motes have been chosen in this study to
experiment the implementation of product-driven control on a
particular case study.
hal-00321465, version 1 - 15 Sep 2008
Table 1. Comparison between RFID tags and WSN motes possibilities
Passive RFID tag Active RFID tags Mote
1 Possess a unique identity Yes Yes Yes
2 Be capable of communicating
effectively with its environment
Yes, data can be requested by an
RFID reader
Yes, data can be requested by an
RFID reader
Yes, data can be send via UDP
protocol
3 Be able to retain or store data about
itself Yes, contains a memory Yes, contains a memory Yes, contains a memory
4
Deploy a language to display its
features, production requirements,
etc.
No, the memory only contains data,
not information
Yes, the processor can interpret
memory data into product
information
Yes, the processor can interpret
memory data into product
information
5
Be capable of participating in or
making decisions relevant to its
destiny
Not able to make decision Yes, able to make a decision using
an embedded algorithm
Yes, able to make a decision using
an embedded algorithm
Aspects of technical intelligence
4. CASE STUDY
The implementation of the concept of product-driven control
is tested with WSN motes in this paper on a scenario using
the Flexible Assembly Cell case study of the AIP-Primeca
Lorraine (http://www.aip-primeca.net).
4.1 Presentation of the AIPL Case Study
The cell involves six workstations which are interconnected
by a conveyor: one station for pallet loading, four similar
assembly stations, and one station for pallet unloading (Fig.
6). Six different product families can be assembled (Fig. 7).
Each workstation is able to perform from 1 to 4 assembly
operations and involves a vacuum generator and three air
cylinders to handle parts and products.
Loading workstation n°0 Workstation n°1 Workstation n°2
Unloading workstation n°5 Workstation n°4 Workstation n°3
Fig. 6. AIPL Flexible Assembly Cell
Product 01,09
Product 60,88,09
Product 60,88,11,10
Part 09
Part 01
Part 88
Part 11
Part 60
Part 10
Fig. 7 AIPL Product types
Each pallet is equipped with a P-Particle© WSN mote
(http://particle.teco.edu/) which implements the control part
(‘intelligent part’) of products. A restriction is made so that
each product will only go on one pallet during its assembly.
Workstations are equipped with a Programmable Logic
Controller (PLC), which implement resource controllers. The
communication between product motes and resource
controllers is ensured by an XBridge© which forwards UDP
packets (used for the motes to communicate) from the WSN
to the Industrial Ethernet and vice versa (Fig. 8).
Mote b
Mote a WSN/ Ethernet bridge
WSN
Industrial Ethernet
Workstation 1 PLC
Workstation 4 PLC
SCADA System
MES Server
MES Database
Conveyor PLC
Fig. 8. Principle of the platform technical architecture
As seen in Fig. 9, this platform, currently under specification
and development, plans product controllers to exchange
Requests (RQ) and Reports (RP) with their environment.
Product a program Supervisor
Conveyor PLC program
Work station 4 PLC
program
Work station n PLC
program
Work station 1 PLC
program
RQW_op60
RPW_WS4
RQT_OP60_WS4
RPT_WS4
RQ_op60_WS4
RP_op60_WS4_time_date
OPC Serve r
WS4 variables
WS1 variables
WSn variables
OPC items
Conveyor variables
RQ_config MES
New product process planning
Fig. 9. Principle of the platform applicative architecture
To validate the implementation of level 1 and 2 of product
intelligence, this paper focuses mainly on the product
behaviour and communication. As the intelligent part of the
product is implemented into motes, Teco Particle Analyser
software is used to configure motes and to analyse their
communications with external applications.
Motes are used to implement product intelligence only during
the manufacturing. Once the product is manufactured, the
corresponding mote memory is unloaded in order to store
traceability information into the MES. The mote is then
reconfigured in order to be used with a new product (Fig. 10).
hal-00321465, version 1 - 15 Sep 2008
Initial
A0 - Unconfigured
mote
A2 - Manufacturing of the
product / storage of p roduct
traceability information
[end of configuration]
[end of manufacturing]
[end of unloading]
A3 - Unloading of the
product / downloading
traceability information
A1 - Mote configuration /
emb edding product
p rocess p lan
[new product to be made]
Fig. 10. Activity diagram showing mote and product stages
during manufacturing
4.2 Implementing level 1 product intelligence
According to the definition given by Wong et al. (2002),
level 1 intelligence refers to the ability of a product to cover
points 1 to 3 of the definition: a unique identifier, ability to
communicate and to store data about the itself.
In order to test this level of product intelligence, the
configuration activity (A1 – Mote configuration / embedding
product process planning) presented in Fig. 10 is considered.
During this activity, detailed in Fig. 11, a mote which is not
configured with a product ID and process plan emits
periodically a request of configuration (NCF). Once the
manufacturing of a new product is planned by the supervisor
or the MES, the mote is reconfigurated (a new ID and a new
process plan). In order to acknowledge receipt of the
configuration, the product mote sends an ‘ELO’ message,
with the received configuration.
Product a:Product mote
Supervisor
NCF
CFG (IDProduct, Process Plan)
ELO (ID Product, Process Plan)
Fig. 11. Configuration sequence diagram
This scenario has been implemented on Particle© motes (Fig.
12). The analysis shows that the product emits a ‘NCF’
message, containing the mote ID (2.232.0.0.0.77.220.181)
corresponding to point 1 of (Wong et al. 2002). It receives
the configuration (sequence 14 ‘CFG’ with parameters {ID of
the product class, Class Number, Process Plan, …}), stores it
and is able to communicate it (points 2 & 3) by broadcasting
an ‘ELO’ message containing its ID (100 123) and its type
(97).
Fig. 12. Screenshot showing product and supervisor
exchanges for the first experiment
This first experiment shows that WSN motes can implement
at least level 1 product intelligence. A second scenario is
needed to experiment if WSN motes are able to implement
some more aspects of product intelligence.
4.3 Implementing level 2 product intelligence
As presented above, the level 2 of product intelligence
defined in (Wong et al. 2002) corresponds, in addition to
level 1 intelligence, to the ability of a product to deploy a
language to communicate and to participate in decisions
relevant to its destiny. The second experiment considers the
activity A2 of Fig. 10, in which the manufacturing is driven
by the product itself. The control is then based on the
exchange, between the product and its environment, of
Requests (RQ) and Reports (RP). A language is then defined
as follows:
- RQW_opi: request from the product: “which resource is
able to perform operation i to me?”
- RPW_WSj_opi: report from the supervisor: “the
workstation j is able to perform operation i” (the
workstation is chosen by the supervisor in function of an
optimization criteria, for example the waiting time)
- RQT_WSj_opi: request from the product to the conveyor:
“bring me to workstation j for operation i“
- RPT_WSj_opi: report from the conveyor: “you are now
at workstation j for operation i”
- RQ_opi_WSj: request from the product to workstation j:
“perform me operation i”
- RP_opi_WSj_time_date: workstation j reports to the
product: “I performed you operation i at time and date”
The ‘intelligent part’ of the product, is able to request
operations and to receive reports of operations. The order in
which the reports are emitted and the reports are waited is
defined in the control part of product in function of the
successive physical states of the product (Fig. 13) to ensure
the correct execution of the process plan. A similar sequence
is executed for each operation.
Initia l
1
[RQW_OPi]
[RPW_WSj] 2
3
[RQT_WSj]
4
[RPT_WSj]
5
Final [RP_OPi_WSj_time_date] [RQ_opi]
Fig. 13. Gerenic sequence of product internal behaviour
This internal behaviour can be formally synthesized as
described in Pétin et al. (2007), but it is out of the scope of
this paper. In addition, traceability information is stored in
the product in function of manufacturing parameters which
are given by resources.
An example of message exchange between product and
external applications is shown in Fig. 14. The product
requests resources to perform the operations scheduled in its
process plan, in function of the reports it receives.
Product a:Product mote
Supervisor
RQW_op60
RPW_WS4
Conveyor
RQT_OP60_WS4
RPT_WS4
Workstation 4
RQ_op60_WS4
RP_60_WS4_time_date
If product not at
Workstation 2
Fig. 14. Product and resource motes collaboration to perform
operation ‘op60’ on workstation 4
hal-00321465, version 1 - 15 Sep 2008
This example scenario has been successfully tested. Fig. 15
shows a screenshot with the exchange of product-driven
control messages between a product, a resource and a
supervisor. The product successively requests the operations
which are relevant to its manufacturing (for example
operations 60 and 88). Furthermore, traceability is ensured by
the storage into the product of manufacturing conditions (e.g.
operation 60 on workstation 4, at 15:35 on September 20th).
Fig. 15. Screenshot showing product message exchanges for
the second experiment
5. CONCLUSION AND OPEN ISSUES
This paper focuses on the possibilities to implement the
concept of product-driven control on a case study using
motes of wireless sensor networks. WSN, compared with
RFID, allow guaranteeing the continuity of information
availability during the overall manufacturing process of
products. The use of the Particle Analyser showed that it is
possible with motes, on a simple example, to cover level 1
and 2 of product intelligence, as defined by Wong et al.
(2007). Traceability is also ensured by storing manufacturing
operation information directly into the product.
Issues are now open on the efficiency of the concept
implementation by putting various product instances in a real
manufacturing environment. This will underline
communication and product conflict problems. The last ones
may be solved by the development of a ‘staff holon’ as
defined by Van Brussels et al. (1998). WSN sensing abilities
(temperature, distances between motes, acceleration …) will
also be used to situate and to monitor products in their
environment. Such data may be taken in account by products
in their decision making to ensure quality and to optimize
manufacturing flows.
REFERENCES
Akyildiz I.F., W. Su, Y. Sankarasubramaniam, E. Cayirci
(2002). Wireless sensor networks: a survey. Computer
networks, 38, pp. 393-422.
Arampatzis Th., J. Lygeros, S. Manesis (2005). A survey of
applications of wireless sensors and wireless sensor
networks, Proceedings of the 13th Mediterranean
Conference on Control and Automation, Limassol,
Cyprus, June 27-29, pp. 719-724.
Babiceanu R. F., F. F. Chen (2006). Development and
applications of holonic manufacturing systems: a survey,
Journal of Intelligent Manufacturing, 17, pp. 111-131.
Chirn J.-L., D. McFarlane (2005). Evaluating holonic control
systems: a case study. Proceedings of the 16th IFAC
world congress in Prague, Elsevier, ISBN 008045108X.
Da Silveira G., D. Borenstein, F.S. Fogliatto (2001). Mass
customization: literature review and research directions.
Int. Journal of Production Economics, 72, pp 1-13.
Deen, S.M. (Editor) (2003). Agent-based manufacturing -
Advances in the holonic approach, Springer, ISBN 3-
540-44069-0.
Finkenzeller K. (2003). RFID handbook: fundamentals and
application in contactless smart cards and
indentification, J. Wiley and Son, ISBN 0-470-84402-7.
International Organisation for Standardization (2003). ISO
62264: enterprise-control system integration.
Hollocks B.W., H.T. Goranson, D.N. Shorter, F.B. Vernadat
(1997). Assessing enterprise integration for competitive
advantage, ICEIMT’97, International Conference on
Enterprise Modelling and Modelling Technology, Berlin.
Karkkainen M., J. Holmstrom, K. Framling, K. Artto (2003).
Intelligent products – a step towards a more effective
project delivery chain, Computers in Industry, 50, pp.
141-151.
Koestler A. (1967). The ghost in the machine, ISBN 0-14-
019162-5.
Leitão P., F. Restivo (2006). ADACOR: A Holonic
architecture for agile and adaptive manufacturing
control, Computers in Industry, 57, pp. 121-130.
Marik V., J. Lazansky (2006). Industrial application of agent
technologies, Control Engineering Practice,
doi:10.1016/j.conengprac.2006.10.001.
McFarlane D., S. Sarma, J.L. Chirn, C.Y. Wong, K. Ashton
(2003). Auto id systems and intelligent manufacturing
control, Engineering Application of Artificial
Intelligence, 16 (4), pp. 365-376.
Morel G., H. Panetto, M. Zaremba, F. Mayer (2003).
Manufacturing enterprise control and management
system engineering: rationales and open issues. IFAC
Annual Reviews in Control, 27 (2), pp. 199-209.
Morel G., P. Valckenaers, J.M. Faure, C. Pereira, C. Diedrich
(2005). Survey paper on manufacturing plant control
challenges and issues, Proceedings of the 16th IFAC
world congress in Prague, ISBN 008045108X.
Nof S. Y., G. Morel, L. Monostori, A. Molina, F. Filip
(2006). From plant and logistics control to multienterprise
collaboration, IFAC annual reviews in control,
30 (1), pp. 55-68.
Pétin J.-F., D. Gouyon, G. Morel (2007). Supervisory
synthesis for product-driven automation and its
application to a flexible assembly cell, Control
Engineering Practice, 15, pp. 595-614.
Valckenaers P. (Editor) (2001). Special issue: Holonic
Manufacturing Systems, Computers In Industry, 46 (3),
pp. 233-331.
Van Brussel H., J. Wyns, P. valckenaers, L. Bongaerts, P.
Peeters (1998). Reference architecture for holonic
manufacturing systems: PROSA, Computers in Industry,
37 (3), pp. 255-274.
Wong C.Y., D. McFarlane, A. A. Ahmad Zaharudin, V.
Agarwal (2002). The intelligent product-driven supply
chain, IEEE International Conference on Systems, Man
and Cybernetics.
hal-00321465, version 1 - 15 Sep 2008

Distributed Signal Processing Techniques for Wireless Sensor

Editorial
Erchin Serpedin, Hongbin Li, Aleksandar Dogandžić, Huaiyu Dai, and Paul Cotae
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TXDistributed Signal Processing Techniques for Wireless Sensor
Networks

77843, USA
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken,
NJ 07030, USA
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011,
USA
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh,
NC 27695, USA
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San
Antonio, TX 78249, USA
Recent advances in micro electromechanical systems (MEMS) technology have enabled
the design of low-power low-cost smart sensors equipped with multiple onboard
functions such as sensing, computing and communications. Such intelligent devices
networked through wireless links have been referred to as wireless sensor networks and
recognized as one of the most important technologies for the 21st century. Wireless
sensor networks hold the promise to revolutionize the sensing technology for a broad
spectrum of applications, including infrastructure monitoring and surveillance, disaster
management, monitoring the health status of humans, plants, animals and industrial
machines, etc.
Wireless sensor networks can be viewed as a special case of wireless ad hoc networks,
and assume a multi-hop communication framework with no centralized infrastructure and
where the sensors cooperate spontaneously by forwarding each other's packets for
delivery from a source to a destination node. The multi-hop nature of sensor networks is
imposed by energy-consumption reasons because of the super-linear power loss of
wireless transmissions with respect to the propagation distance.
In general, the design of wireless sensor networks is subjected to a number of challenges:
low energy consumption which manifests in minimal energy expenditure in each sensor
node and efficient usage of power-saving sleep/wake-up modes, scalability in the
presence of a large number of sensors, possibility of frequent node failures and network
topology changes, collaborative signal processing and data aggregation techniques to
cope with the large number of sensors which might congest the network with information,
and efficient communication protocols to deal with the special broadcast communication
paradigm and the increased possibility of packet collisions and congestions for nodes
operating in closely spaced transmission ranges.
The scope of this special issue was to present the state-of-the-art and emerging
distributed signal processing techniques that deal with some of the above-mentioned
design challenges. This special issue consists of seven papers that treat important signal
processing aspects such as compression, quantization, estimation, detection,
synchronization and localization in wireless sensor networks. A short description of the
contributions brought by these papers is next presented.
In the paper ``Energy-Constrained Optimal Quantization for Wireless Sensor Networks”,
X. Luo and G. B. Giannakis deal with the important problem of designing efficient
quantizers that ensure optimal reconstruction at the fusion center of the measurements
yielded by a sensor as well as the estimation of a deterministic parameter by exploiting
the measurements collected by a set of sensors. The design is carried out under power
constraints and information such as channel propagation effects, modulation, and energy
consumed by transceiver circuitry is considered into the analysis. The effect of channel
coding on the reconstruction performance is also studied, and the optimum number of
quantization bits and energy levels are derived.
The problem of designing an optimal-level distributed transform for wavelet based
spatio-temporal data compression in wireless sensor networks is addressed by S. Zhou et
al. in the paper ``Ring Based Optimal-Level Distributed Wavelet Transform With
Arbitrary Filter Length For Wireless Sensor Networks.” This paper proposes a distributed
optimal-level spatio-temporal compression algorithm based on the ring model for general
wavelets with arbitrary supports. The proposed compression algorithm accommodates a
broad range of wavelet functions, effectively exploits the temporal and spatial correlation
of data measurements, and achieves significant reduction in energy consumption and
delay for data gathering in sensor clusters.
In the paper `` Distortion-Rate Bounds for Distributed Estimation using Wireless Sensor
Networks,” D. Schizas et al. address the problem of centralized and distributed rateconstrained
estimation of random signal vectors by exploiting a network of wireless
sensors (encoders) that communicate with a fusion center (decoder). Within the proposed
framework, the authors of this paper determine lower and upper bounds on the
corresponding distortion-rate (D-R) function using both centralized as well as distributed
estimation techniques.
The paper ``Distributed Event Region Detection in Wireless Sensor Networks,” coauthored
by J. Fang and H. Li, proposes a graph-based method for distributed eventregion
detection in wireless sensor networks. The proposed detection scheme exploits a
graphical model to take into account the fact that events occurring in geographically
neighboring sensors present a statistical dependency. The proposed detection scheme
admits also energy and bandwidth efficient distributed implementations.
Q. Chaudhari and E. Serpedin, in the paper ``Clock Estimation for Long-Term
Synchronization in Wireless Sensor Networks with Exponential Delays,” deal with the
maximum likelihood estimation of the clock parameters (phase, skew, and drift) in twoway
timing exchange mechanisms and in networks with exponentially distributed delays.
The paper entitled ``Extension of Pairwise Broadcast Clock Synchronization for Multi-
Cluster Sensor Networks,” co-authored by K. L. Noh et al., proposes a novel clock
synchronization protocol to minimize the overall energy consumption in wireless sensor
networks that assume general multi-cluster topologies. The proposed synchronization
approach relies on a receiver-only synchronization approach and it can be viewed as a
generalization of the Pairwise Broadcast Synchronization (PBS) protocol. Like PBS, the
proposed synchronization approach exhibits the distinct advantage that the number of
sensor nodes can be synchronized by only over-hearing time message exchanges between
pairs of nodes, and therefore it reduces significantly the overall network-wide energy
consumption by decreasing the number of required timing messages for synchronization.
Finally, in the paper ``Optimization of sensor locations and sensitivity analysis for engine
health monitoring using minimum interference algorithms,” P. Cotae et al. address the
problem of optimal placement of sensors in the presence of additive white Gaussian noise
(AWGN) by considering the sensors as systems that present full communications
capabilities and by minimizing the RF-interference induced by the wireless
communication channels among the sensor nodes. Numerical simulations and a
sensitivity analysis study are presented to illustrate the robustness of the proposed
algorithm.
The editors of this special issue would like to express their heartfelt ``Thank You!” to all
the people (editors, authors, and reviewers) who supported the publication of this special
issue.
Erchin Serpedin received (with highest distinction) the Diploma of Electrical
Engineering from the Polytechnic Institute of Bucharest, Bucharest, Romania, in 1991.
He received the specialization degree in signal processing and transmission of
information from Ecole Superieure D'Electricite, Paris, France, in 1992, the M.Sc. degree
from Georgia Institute of Technology, Atlanta, GA, in 1992, and the Ph.D. degree in
Electrical Engineering from the University of Virginia, Charlottesville, VA, in January
1999. In July 1999, he joined Texas A&M University in College Station, as an assistant
professor, and where currently holds the position of associate professor. His research
interests lie in the areas of signal processing, bioinformatics and telecommunications. He
received the NSF Career Award in 2001, the CCCT 2004 Best Conference Award, the
Outstanding Faculty Award in 2004, NRC Fellow Award in 2005, and TEES Award in
2005. He is currently serving as an associate editor for the IEEE Communications Letters,
IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE
Transactions on Wireless Communications, EURASIP Journal on Advances in Signal
Processing and EURASIP Journal on Bioinformatics and Systems Biology. Dr. Serpedin
served also as a technical co-chair of the Communications Theory Symposium at
Globecom 2006 Conference, and VTC Fall 2006: Wireless Access Track.
Hongbin Li received the B.S. and M.S. degrees from the University of Electronic
Science and Technology of China, Chengdu, in 1991 and 1994, respectively, and the
Ph.D. degree from the University of Florida, Gainesville, in 1999, all in electrical
engineering. From July 1996 to May 1999, he was a Research Assistant with the
Department of Electrical and Computer Engineering, University of Florida. He was a
Summer Visiting Faculty Member of the Air Force Research Laboratory, Rome, NY, in
summers 2003 and 2004. Since July 1999, he has been with the Department of Electrical
and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, where he is
an Associate Professor. His current research interests include wireless communications
and networking, statistical signal processing, and radars. Dr. Li is a member of Tau Beta
Pi and Phi Kappa Phi. He received the Harvey N. Davis Teaching Award in 2003 and the
Jess H. Davis Memorial Award for excellence in research in 2001 from Stevens Institute
of Technology, and the Sigma Xi Graduate Research Award from the University of
Florida in 1999. He is a member of the Sensor Array and Multichannel (SAM) Technical
Committee of the IEEE Signal Processing Society. He is an Associate Editor for the
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (1/2003 to 12/2006),
the IEEE SIGNAL PROCESSING LETTERS (1/2005 to 12/2006), and the IEEE
TRANSACTIONS ON SIGNAL PROCESSING (10/2006 to present), and serves as a
Guest Editor for EURASIP Journal on Applied Signal Processing Special Issue on
Distributed Signal Processing Techniques for Wireless Sensor Networks.
Aleksandar Dogandzic received the Dipl. Ing. degree (summa cum laude) in electrical
engineering from the University of Belgrade, Yugoslavia, in 1995, and the M.S. and Ph.D.
degrees in electrical engineering and computer science from the University of Illinois at
Chicago (UIC) in 1997 and 2001, respectively, under the guidance of Prof. A. Nehorai. In
August 2001, he joined the Department of Electrical and Computer Engineering, Iowa
State University, Ames, as an Assistant Professor. His research interests are in statistical
signal processing theory and applications. Dr. Dogandzic received the Distinguished
Electrical Engineering M.S. Student Award by the Chicago Chapter of the IEEE
Communications Society in 1996. Hewas awarded the Aileen S. AndrewFoundation
Graduate Fellowship in 1997, the UIC University Fellowship in 2000, and the 2001
Outstanding Thesis Award in the Division of Engineering, Mathematics, and Physical
Sciences, UIC. He is the recipient of the 2003 Young Author Best Paper Award and 2004
Signal Processing Magazine Award by the IEEE Signal Processing Society.
Huaiyu Dai received the B.E. and M.S. degrees in electrical engineering from Tsinghua
University, Beijing, China, in 1996 and 1998, respectively, and the Ph.D. degree in
electrical engineering from Princeton University, Princeton, NJ, in 2002.He was with
Bell Labs, Lucent Technologies, Holmdel, NJ, during summer 2000, and with AT&T
Labs-Research, Middletown, NJ, during summer 2001. Currently, he is an Assistant
Professor of Electrical and Computer Engineering at North Carolina State University,
Raleigh. His research interests are in the general areas of communication systems and
networks, advanced signal processing for digital communications, and communication
theory and information theory. His current research focuses on distributed signal
processing and crosslayer design (with a physical layer emphasis) in wireless ad hoc and
sensor networks, distributed, multicell, multiuser MIMO communications, and associated
information-theoretic and computation-theoretic analysis.
Paul Cotae was born in Falticeni, Romania, on June 21, 1955. He received the Dipl.Ing.
and M.S. degrees in communication and electronic engineering from the Technical
University of Iassy, Iasi, Romania, in 1980 and the Ph.D. degree in telecommunications
from Politechnica University of Bucharest, Bucharest, Romania, in 1992. Since 1984, he
has been with the Department of Electrical Engineering, Technical University of Iassy,
where he conducted research and teaching in the area of digital communications as a Full
Professor. From 1994 to 1998, he spent four years in the USA at the University of
Colorado at Colorado Springs and Boulder as a Fulbright Scholar and Visiting Associate
Professor, where he did research and teaching with the Electrical and Computer
Engineering Department and Applied Mathematics Department, respectively. He also
served as a Consultant to Navsys Corporation, Colorado Springs, in 1997. Currently, he
is with the University of Texas, San Antonio. His current research interests include
multiple access, modulation and coding, mobile communications, and digital
communication systems. He has authored or coauthored more than 90 papers in these
areas and four books. Dr. Cotae serves as an Associate Editor for IEEE
COMMUNICATIONS LETTERS, and he has been on the Technical Program Committee
and Session Chair of IEEE Conferences such as GLOBECOM (2003–2006), VTC Spring
2005, and ICC 2005 and 2006. He is a member of HKN (Eta Kappa Nu), the American
Society for Engineering Education, and the Society for Industrial and Applied
Mathematics.

Distributed Signal Processing Techniques for

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
Special Issue on
Distributed Signal Processing Techniques for
Wireless Sensor Networks
Call for Papers
Recent advances in hardware and wireless communications
technologies have made possible the design of low-cost, lowpower,
multifunctional sensor devices. When deployed in a
large number across a geographical area, these sensor devices
collaborate among themselves to create a network for
distributed sensing and automated information gathering,
processing, and communication. Wireless sensor networks
are a special case of wireless ad hoc networks that assume a
multihop communication framework with no infrastructure,
where the sensor devices cooperate to convey information
from a source to a destination. This revolutionary technology
will present a huge impact on a broad range of applications:
monitoring the health status of humans, animals, plants, and
civil-engineering structures, control and instrumentation of
industrial machines and home appliances, energy conservation,
security, detection of chemical and biological leaks. The
upcoming years will very likely witness a growing demand for
intelligent sensor systems that will be networked with wireless
local area networks (WLANs) and Internet for increased
functionality and performance.
In general, the design of wireless sensor networks is subject
to the following requirements:
• low energy consumption, which is manifested inminimal
energy expenditure in each sensor node and efficient
usage of power-saving sleep/wake-up modes
• scalability with the increase in the number of sensors
with the goal to extract information from noisy spatiotemporal
measurements collected at the nodes
• broadcast communication paradigm and the increased
possibility of packet collisions and congestions
• absence of centralized communication infrastructure
• possibility of frequent node failures and network
topology changes
The goal of this special issue is to present the state-ofthe-
artresults and emerging signal processing approaches
for wireless sensor networks that can cope with the abovementioned
challenges. Submitted articlesmust not have been
previously published and must not be currently submitted
for publication elsewhere. Topics of interest include the following:
• distributed estimation,detection, inference, and
learning algorithms
• clock and carrier synchronization techniques
• design of distributed modulation techniques
• distributed power control algorithms
• performance bounds and statistical analysis
Due to the existence of a concurrent call for proposals, papers
dealing with localization and tracking applications will
not be accepted.
Authors should follow the EURASIP JASP manuscript
format described at http://www.hindawi.com/journals/asp/.
Prospective authors should submit an electronic copy of their
complete manuscript through the EURASIP JASP Manuscript
Tracking System at http://www.hindawi.com/mts/, according
to the following timetable:
Manuscript Due May 1, 2007
First Review Round August 1, 2007
Publication Date November 1, 2007
GUEST EDITORS:
Erchin Serpedin, Department of Electrical and Computer
Engineering, Texas A&M University, College Station,
TX 77843, USA; serpedin@ece.tamu.edu
Hongbin Li, Department of Electrical and Computer Engineering,
Stevens Institute of Technology, Hoboken,
NJ 07030, USA; hli@stevens.edu
Aleksandar Dogandži´c, Department of Electrical and
Computer Engineering, Iowa State University, Ames,
IA 50011, USA; ald@iastate.edu
Huaiyu Dai, Department of Electrical and Computer Engineering,
North Carolina State University, Raleigh, NC 27695,
USA; Huaiyu_Dai@ncsu.edu
Paul Cotae, Department of Electrical and Computer Engineering,
University of Texas at San Antonio, San Antonio,
TX 78249, USA; paul.cotae@utsa.edu
Hindawi Publishing Corporation
http://www.hindawi.com

Basics of PLCs

STEP 2000

1
Table of Contents
Introduction ..............................................................................2
PLCs .........................................................................................4
Number Systems......................................................................8
Terminology ............................................................................14
Basic Requirements................................................................23
S7-200 Micro PLCs.................................................................28
Connecting External Devices..................................................39
Programming A PLC ...............................................................41
Discrete Inputs/Outputs .........................................................49
Analog Inputs and Outputs.....................................................61
Timers.....................................................................................64
Counters .................................................................................71
High-Speed Instructions .........................................................75
Specialized Expansion Modules .............................................78
Review Answers.....................................................................84
Final Exam ..............................................................................85
2
Introduction
Welcome to another course in the STEP 2000 series, Siemens
Technical Education Program, designed to prepare our
distributors to sell Siemens Energy & Automation products
more effectively. This course covers Basics of PLCs and related
products.
Upon completion of Basics of PLCs you should be able to:
• Identify the major components of a PLC and describe
their functions
• Convert numbers from decimal to binary, BCD, and
hexadecimal
• Identify typical discrete and analog inputs and outputs
• Read a basic ladder logic diagram and statement list
• Identify operational differences between different S7-200
models
• Identify the proper manual to refer to for programming or
installation of an S7-200 PLC
• Connect a simple discrete input and output to an S7-200
• Select the proper expansion module for analog inputs and
outputs
• Describe the operation of timers and counters
3
This knowledge will help you better understand customer
applications. In addition, you will be better able to describe
products to customers and determine important differences
between products. You should complete Basics of Electricity
before attempting Basics of PLCs. An understanding of many
of the concepts covered in Basics of Electricity is required
for Basics of PLCs. In addition you may wish to complete
Basics of Control Components. Devices covered in Basics
of Control Components are used with programmable logic
controllers.
If you are an employee of a Siemens Energy & Automation
authorized distributor, fill out the final exam tear-out card and
mail in the card. We will mail you a certificate of completion if
you score a passing grade. Good luck with your efforts.
SIMATIC, STEP 7, STEP 7-Micro, STEP 7-Micro/WIN, PG 702,
and PG 740 are registered trademarks of Siemens Energy &
Automation, Inc.
Other trademarks are the property of their respective owners.
4
PLCs
Programmable Logic Controllers (PLCs), also referred to as
programmable controllers, are in the computer family. They are
used in commercial and industrial applications. A PLC monitors
inputs, makes decisions based on its program, and controls
outputs to automate a process or machine. This course is meant
to supply you with basic information on the functions and
configurations of PLCs.
5
Basic PLC Operation PLCs consist of input modules or points, a Central Processing
Unit (CPU), and output modules or points. An input accepts a
variety of digital or analog signals from various field devices
(sensors) and converts them into a logic signal that can be used
by the CPU. The CPU makes decisions and executes control
instructions based on program instructions in memory. Output
modules convert control instructions from the CPU into a digital
or analog signal that can be used to control various field devices
(actuators). A programming device is used to input the desired
instructions. These instructions determine what the PLC will do
for a specific input. An operator interface device allows process
information to be displayed and new control parameters to be
entered.
Pushbuttons (sensors), in this simple example, connected to
PLC inputs, can be used to start and stop a motor connected to
a PLC through a motor starter (actuator).
6
Hard-Wired Control Prior to PLCs, many of these control tasks were solved with
contactor or relay controls. This is often referred to as hardwired
control. Circuit diagrams had to be designed, electrical
components specified and installed, and wiring lists created.
Electricians would then wire the components necessary to
perform a specific task. If an error was made the wires had
to be reconnected correctly. A change in function or system
expansion required extensive component changes and rewiring.
OL
M
CR
CR
L1
T1
T2
T3
L2
L3
OL
OL
OL
M
M
CR
M
Motor
Start
Stop
460 VAC
24 VAC
1
2
Advantages of PLCs The same, as well as more complex tasks, can be done with
a PLC. Wiring between devices and relay contacts is done in
the PLC program. Hard-wiring, though still required to connect
field devices, is less intensive. Modifying the application and
correcting errors are easier to handle. It is easier to create and
change a program in a PLC than it is to wire and rewire a circuit.
Following are just a few of the advantages of PLCs:
• Smaller physical size than hard-wire solutions.
• Easier and faster to make changes.
• PLCs have integrated diagnostics and override functions.
• Diagnostics are centrally available.
• Applications can be immediately documented.
• Applications can be duplicated faster and less expensively.
7
Siemens PLCs Siemens makes several PLC product lines in the SIMATIC® S7
family. They are: S7-200, S7-300, and S7-400.
S7-200 The S7-200 is referred to as a micro PLC because of its small
size. The S7-200 has a brick design which means that the
power supply and I/O are on-board. The S7-200 can be used on
smaller, stand-alone applications such as elevators, car washes,
or mixing machines. It can also be used on more complex
industrial applications such as bottling and packaging machines.
S7-300 and S7-400 The S7-300 and S7-400 PLCs are used in more complex
applications that support a greater number of I/O points. Both
PLCs are modular and expandable. The power supply and I/O
consist of separate modules connected to the CPU. Choosing
either the S7-300 or S7-400 depends on the complexity of
the task and possible future expansion. Your Siemens sales
representative can provide you with additional information on
any of the Siemens PLCs.
8
Number Systems
Since a PLC is a computer, it stores information in the form of
On or Off conditions (1 or 0), referred to as binary digits (bits).
Sometimes binary digits are used individually and sometimes
they are used to represent numerical values.
Decimal System Various number systems are used by PLCs. All number systems
have the same three characteristics: digits, base, weight. The
decimal system, which is commonly used in everyday life, has
the following characteristics:
Ten digits 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
Base 10
Weights 1, 10, 100, 1000, ...
Binary System The binary system is used by programmable controllers. The
binary system has the following characteristics:
Two digits 0, 1
Base 2
Weights Powers of base 2 (1, 2, 4, 8, 16, ...)
In the binary system 1s and 0s are arranged into columns. Each
column is weighted. The first column has a binary weight of
20. This is equivalent to a decimal 1. This is referred to as the
least significant bit. The binary weight is doubled with each
succeeding column. The next column, for example, has a weight
of 21, which is equivalent to a decimal 2. The decimal value is
doubled in each successive column. The number in the far left
hand column is referred to as the most significant bit. In this
example, the most significant bit has a binary weight of 27. This
is equivalent to a decimal 128.
9
Converting Binary The following steps can be used to interpret a decimal
to Decimal number from a binary value.
1) Search from least to most significant bit for 1s.
2) Write down the decimal representation of each column
containing a 1.
3) Add the column values.
In the following example, the fourth and fifth columns from the
right contain a 1. The decimal value of the fourth column from
the right is 8, and the decimal value of the fifth column from
the right is 16. The decimal equivalent of this binary number is
24. The sum of all the weighted columns that contain a 1 is the
decimal number that the PLC has stored.
In the following example the fourth and sixth columns from the
right contain a 1. The decimal value of the fourth column from
the right is 8, and the decimal value of the sixth column from
the right is 32. The decimal equivalent of this binary number is
40.
Bits, Bytes, and Words Each binary piece of data is a bit. Eight bits make up one byte.
Two bytes, or 16 bits, make up one word.
10
Logic 0, Logic 1 Programmable controllers can only understand a signal that
is On or Off (present or not present). The binary system is a
system in which there are only two numbers, 1 and 0. Binary 1
indicates that a signal is present, or the switch is On. Binary 0
indicates that the signal is not present, or the switch is Off.
BCD Binary-Coded Decimal (BCD) are decimal numbers where
each digit is represented by a four-bit binary number. BCD is
commonly used with input and output devices. A thumbwheel
switch is one example of an input device that uses BCD. The
binary numbers are broken into groups of four bits, each group
representing a decimal equivalent. A four-digit thumbwheel
switch, like the one shown here, would control 16 (4 x 4) PLC
inputs.
11
Hexadecimal Hexadecimal is another system used in PLCs. The hexadecimal
system has the following characteristics:
16 digits 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F
Base 16
Weights Powers of base 16 (1, 16, 256, 4096 ...)
The ten digits of the decimal system are used for the first ten
digits of the hexadecimal system. The first six letters of the
alphabet are used for the remaining six digits.
A = 10 D = 13
B = 11 E = 14
C = 12 F = 15
The hexadecimal system is used in PLCs because it allows the
status of a large number of binary bits to be represented in a
small space such as on a computer screen or programming
device display. Each hexadecimal digit represents the exact
status of four binary bits. To convert a decimal number to a
hexadecimal number the decimal number is divided by the base
of 16. To convert decimal 28, for example, to hexadecimal:
Decimal 28 divided by 16 is 1 with a remainder of 12. Twelve is
equivalent to C in hexadecimal. The hexadecimal equivalent of
decimal 28 is 1C.
The decimal value of a hexadecimal number is obtained by
multiplying the individual hexadecimal digits by the base 16
weight and then adding the results. In the following example
the hexadecimal number 2B is converted to its decimal
equivalent of 43.
160 = 1
161 = 16
B = 11
12
Conversion of Numbers The following chart shows a few numeric values in decimal,
binary, BCD, and hexadecimal representation.
13
Review 1
1. Identify the following:
2. The binary number system has a base ____________ .
3. The hexadecimal number system has a base
____________ .
4. Convert a decimal 10 to the following:
Binary ____________
BCD ____________
Hexadecimal ____________
14
Terminology
The language of PLCs consists of a commonly used set
of terms; many of which are unique to PLCs. In order to
understand the ideas and concepts of PLCs, an understanding
of these terms is necessary.
Sensor A sensor is a device that converts a physical condition into an
electrical signal for use by the PLC. Sensors are connected to
the input of a PLC. A pushbutton is one example of a sensor
that is connected to the PLC input. An electrical signal is sent
from the pushbutton to the PLC indicating the condition (open/
closed) of the pushbutton contacts.
Actuators Actuators convert an electrical signal from the PLC into a
physical condition. Actuators are connected to the PLC output.
A motor starter is one example of an actuator that is connected
to the PLC output. Depending on the output PLC signal the
motor starter will either start or stop the motor.
15
Discrete Input A discrete input, also referred to as a digital input, is an input
that is either in an ON or OFF condition. Pushbuttons, toggle
switches, limit switches, proximity switches, and contact
closures are examples of discrete sensors which are connected
to the PLCs discrete or digital inputs. In the ON condition a
discrete input may be referred to as a logic 1 or a logic high. In
the OFF condition a discrete input may be referred to as a logic
0 or a logic low.
A Normally Open (NO) pushbutton is used in the following
example. One side of the pushbutton is connected to the first
PLC input. The other side of the pushbutton is connected to an
internal 24 VDC power supply. Many PLCs require a separate
power supply to power the inputs. In the open state, no voltage
is present at the PLC input. This is the OFF condition. When the
pushbutton is depressed, 24 VDC is applied to the PLC input.
This is the ON condition.
16
Analog Inputs An analog input is an input signal that has a continuous signal.
Typical analog inputs may vary from 0 to 20 milliamps, 4 to 20
milliamps, or 0 to 10 volts. In the following example, a level
transmitter monitors the level of liquid in a tank. Depending on
the level transmitter, the signal to the PLC can either increase or
decrease as the level increases or decreases.
Discrete Outputs A discrete output is an output that is either in an ON or OFF
condition. Solenoids, contactor coils, and lamps are examples
of actuator devices connected to discrete outputs. Discrete
outputs may also be referred to as digital outputs. In the
following example, a lamp can be turned on or off by the PLC
output it is connected to.
17
Analog Outputs An analog output is an output signal that has a continuous
signal. The output may be as simple as a 0-10 VDC level that
drives an analog meter. Examples of analog meter outputs are
speed, weight, and temperature. The output signal may also
be used on more complex applications such as a current-topneumatic
transducer that controls an air-operated flow-control
valve.
CPU The central processor unit (CPU) is a microprocessor system
that contains the system memory and is the PLC decisionmaking
unit. The CPU monitors the inputs and makes decisions
based on instructions held in the program memory. The
CPU performs relay, counting, timing, data comparison, and
sequential operations.
18
Programming A program consists of one or more instructions that accomplish
a task. Programming a PLC is simply constructing a set of
instructions. There are several ways to look at a program such
as ladder logic, statement lists, or function block diagrams.
Ladder Logic Ladder logic (LAD) is one programming language used
with PLCs. Ladder logic uses components that resemble
elements used in a line diagram format to describe hard-wired
control. Refer to the STEP 2000 course Basics of Control
Components for more information on line diagrams.
STEP 2000
Basics of
Control
Components
Ladder Logic Diagram The left vertical line of a ladder logic diagram represents the
power or energized conductor. The output element or instruction
represents the neutral or return path of the circuit. The right
vertical line, which represents the return path on a hard-wired
control line diagram, is omitted. Ladder logic diagrams are read
from left-to-right, top-to-bottom. Rungs are sometimes referred
to as networks. A network may have several control elements,
but only one output coil.
19
In the example program shown example I0.0, I0.1 and Q0.0
represent the first instruction combination. If inputs I0.0 and
I0.1 are energized, output relay Q0.0 energizes. The inputs could
be switches, pushbuttons, or contact closures. I0.4, I0.5, and
Q1.1 represent the second instruction combination. If either
input I0.4 or I0.5 are energized, output relay Q0.1 energizes.
Statement list A statement list (STL) provides another view of a set of
instructions. The operation, what is to be done, is shown on the
left. The operand, the item to be operated on by the operation,
is shown on the right. A comparison between the statement
list shown below, and the ladder logic shown on the previous
page, reveals a similar structure. The set of instructions in this
statement list perform the same task as the ladder diagram.
Function Block Diagrams Function Block Diagrams (FBD) provide another view of a set of
instructions. Each function has a name to designate its specific
task. Functions are indicated by a rectangle. Inputs are shown
on the left-hand side of the rectangle and outputs are shown on
the right-hand side. The function block diagram shown below
performs the same function as shown by the ladder diagram
and statement list.
20
PLC Scan The PLC program is executed as part of a repetitive process
referred to as a scan. A PLC scan starts with the CPU reading
the status of inputs. The application program is executed using
the status of the inputs. Once the program is completed, the
CPU performs internal diagnostics and communication tasks.
The scan cycle ends by updating the outputs, then starts over.
The cycle time depends on the size of the program, the number
of I/Os, and the amount of communication required.
Software Software is any information in a form that a computer or PLC
can use. Software includes the instructions or programs that
direct hardware.
Hardware Hardware is the actual equipment. The PLC, the programming
device, and the connecting cable are examples of hardware.
21
Memory Size Kilo, abbreviated K, normally refers to 1000 units. When talking
about computer or PLC memory, however, 1K means 1024. This
is because of the binary number system (210=1024). This can be
1024 bits, 1024 bytes, or 1024 words, depending on memory
type.
RAM Random Access Memory (RAM) is memory where data can be
directly accessed at any address. Data can be written to and
read from RAM. RAM is used as a temporary storage area.
RAM is volatile, meaning that the data stored in RAM will be
lost if power is lost. A battery backup is required to avoid losing
data in the event of a power loss.
ROM Read Only Memory (ROM) is a type of memory that data can
be read from but not written to. This type of memory is used
to protect data or programs from accidental erasure. ROM
memory is nonvolatile. This means a user program will not lose
data during a loss of electrical power. ROM is normally used to
store the programs that define the capabilities of the PLC.
EPROM Erasable Programmable Read Only Memory (EPROM) provides
some level of security against unauthorized or unwanted
changes in a program. EPROMs are designed so that data
stored in them can be read, but not easily altered. Changing
EPROM data requires a special effort. UVEPROMs (ultraviolet
erasable programmable read only memory) can only be erased
with an ultraviolet light. EEPROM (electronically erasable
programmable read only memory), can only be erased
electronically.
Firmware Firmware is user or application specific software burned into
EPROM and delivered as part of the hardware. Firmware gives
the PLC its basic functionality.
22
Putting it Together The memory of the S7-200 is divided into three areas: program
space, data space, and configurable parameter space.
• Program space stores the ladder logic (LAD) or statement
list (STL) program instructions. This area of memory controls
the way data space and I/O points are used. LAD or STL
instructions are written using a programming device such as
a PC, then loaded into program memory of the PLC.
• Data space is used as a working area, and includes memory
locations for calculations, temporary storage of intermediate
results and constants. Data space includes memory
locations for devices such as timers, counters, high-speed
counters, and analog inputs and outputs. Data space can be
accessed under program control.
• Configurable parameter space, or memory, stores either the
default or modified configuration parameters.
23
Basic Requirements
In order to create or change a program, the following items are
needed:
• PLC
• Programming Device
• Programming Software
• Connector Cable
PLC Throughout this course we will be using the S7-200 because of
its ease of use.
24
Programming Devices The program is created in a programming device (PG) and then
transferred to the PLC. The program for the S7-200 can be
created using a dedicated Siemens SIMATIC S7 programming
device, such as a PG 720 (not shown) or PG 740, if STEP 7
Micro/WIN software is installed.
A personal computer (PC), with STEP 7 Micro/WIN installed,
can also be used as a programming device with the S7-200.
25
Software A software program is required in order to tell the PLC what
instructions it must follow. Programming software is typically
PLC specific. A software package for one PLC, or one family
of PLCs, such as the S7 family, would not be useful on other
PLCs. The S7-200 uses a Windows based software program
called STEP 7-Micro/WIN32. The PG 720 and PG 740 have STEP
7 software pre-installed. Micro/WIN32 is installed on a personal
computer in a similar manner to any other computer software.
Connector Cables PPI Connector cables are required to transfer data from the
(Point-to-Point Interface) programming device to the PLC. Communication can only
take place when the two devices speak the same language or
protocol. Communication between a Siemens programming
device and the S7-200 is referred to as PPI protocol (pointto-
point interface). An appropriate cable is required for a
programming device such as a PG 720 or PG 740. The S7-200
uses a 9-pin, D-connector. This is a straight-through serial device
that is compatible with Siemens programming devices (MPI
port) and is a standard connector for other serial interfaces.
Programming Device Cable
26
A special cable, referred to as a PC/PPI cable, is needed when a
personal computer is used as a programming device. This cable
allows the serial interface of the PLC to communicate with the
RS-232 serial interface of a personal computer. DIP switches on
the PC/PPI cable are used to select an appropriate speed (baud
rate) at which information is passed between the PLC and the
computer.
27
Review 2
1. A switch or a pushbutton is a ____________ input.
2. A lamp or a solenoid is an example of a ___________
output.
3. The ____________ makes decisions and executes
control instructions based on the input signals.
4. ____________ ____________ is a PLC programming
language that uses components resembling elements
used in a line diagram.
5. A ____________ consists of one or more instructions
that accomplish a task.
6. Memory is divided into three areas: ____________ ,
____________ , and ____________ ____________ space.
7. When talking about computer or PLC memory, 1K
refers to ____________ bits, bytes, or words.
8. Software that is placed in hardware is called
____________ .
9. Which of the following is not required when creating or
changing a PLC program?
a. PLC
b. Programming Device
c. Programming Software
d. Connector Cable
e. Printer
10. A special cable, referred to as a ____________ cable,
is needed when a personal computer is used as a
programming device.