Artificial Intelligence is being called the new electricity, data the new currency. Data processing platforms in the cloud are lowering the barriers to entry. What are the opportunities and challenges for companies to use data as an enabler for disruptive innovation?
Companies are facing many challenges like reducing development costs, shortening time-to-market and searching for new ways to differentiate within their market. When your company is looking for new disruptive products, it can be very interesting to take a look around and use a technology transfer to cope with your challenges.
Sensors are usually perceived as simple building blocks. However, when all constraints are taken into account it becomes more complex. How do you master this complexity and the involved risks? The case of sensor development for smart metering will show how to apply these methods in practice.
Innovation becomes more complex and multidisciplinary, and consequently more challenging and expensive. One way to remedy this, is by using simulation technology, facilitating design iterations and reducing the number of failed experiments.
Todays technology race is at high speed. Private companies plan trips to Mars and consumers print in 3D on their kitchen table. Joris tells his story about open hardware and what it can bring to you.
Transcat Webinar: Fluke Connect - How Wireless Is Changing the GameTranscat
Learn how Fluke Connect and wireless technology can allow you to work faster, more collaboratively, reduce paperwork and improve your safety. Visit transcat.com/flukeconnect for more details.
Presented by Paul Twite, Operations & Energy Services Manager, Delano Utilities
Topics Include:
Common Usage Applications
-Who?
-When?
-Where?
-Why?
Overview of Features
-Video Calls
-Live Trending
-Automatic Measurement Recording
-Equipment History Database
-Cloud Storage
Enabled Tools
-20 Enabled Kits
-Upcoming Releases
This document discusses using the Internet of Things (IoT) for home automation. IoT allows everyday objects to be connected to the internet and controlled remotely. The document proposes a home automation system that uses sensors like temperature, gas, light, and motion detectors to monitor a home. If thresholds are exceeded, such as the temperature rising too high, the system will automatically actuate processes to restore conditions, like turning on the air conditioning. User control and monitoring is also provided through a web server interface. The system aims to improve convenience, comfort, energy efficiency and security for users.
IRJET - IoT based Home Automation System through Voice Control using Google A...IRJET Journal
This document describes an IoT-based home automation system that allows control of electrical appliances using voice commands through a Google Assistant app. The system uses an Arduino microcontroller connected to ESP8266 WiFi modules and relays to control appliances. Users can control lights, fans, and other devices remotely using the Blynk app on their smartphone, which sends commands to the Arduino via ESP8266. This provides convenience and saves energy by automating appliance control. The system was tested successfully to control loads by voice commands through the Blynk and Google Assistant apps.
Naresh Kumar has 3 years of experience in designing and developing software for networking and mobile domains. He has expertise in SDN, cloud computing, NFV, and mobile application development. Some of his key contributions include developing SDN applications using the OpenDaylight controller, implementing network mechanisms in OpenStack, and creating an Android application to accumulate mobile sensor data. He has worked on several projects involving SDN testing, configuring SDN environments, and providing solutions to issues in the OpenDaylight controller.
Companies are facing many challenges like reducing development costs, shortening time-to-market and searching for new ways to differentiate within their market. When your company is looking for new disruptive products, it can be very interesting to take a look around and use a technology transfer to cope with your challenges.
Sensors are usually perceived as simple building blocks. However, when all constraints are taken into account it becomes more complex. How do you master this complexity and the involved risks? The case of sensor development for smart metering will show how to apply these methods in practice.
Innovation becomes more complex and multidisciplinary, and consequently more challenging and expensive. One way to remedy this, is by using simulation technology, facilitating design iterations and reducing the number of failed experiments.
Todays technology race is at high speed. Private companies plan trips to Mars and consumers print in 3D on their kitchen table. Joris tells his story about open hardware and what it can bring to you.
Transcat Webinar: Fluke Connect - How Wireless Is Changing the GameTranscat
Learn how Fluke Connect and wireless technology can allow you to work faster, more collaboratively, reduce paperwork and improve your safety. Visit transcat.com/flukeconnect for more details.
Presented by Paul Twite, Operations & Energy Services Manager, Delano Utilities
Topics Include:
Common Usage Applications
-Who?
-When?
-Where?
-Why?
Overview of Features
-Video Calls
-Live Trending
-Automatic Measurement Recording
-Equipment History Database
-Cloud Storage
Enabled Tools
-20 Enabled Kits
-Upcoming Releases
This document discusses using the Internet of Things (IoT) for home automation. IoT allows everyday objects to be connected to the internet and controlled remotely. The document proposes a home automation system that uses sensors like temperature, gas, light, and motion detectors to monitor a home. If thresholds are exceeded, such as the temperature rising too high, the system will automatically actuate processes to restore conditions, like turning on the air conditioning. User control and monitoring is also provided through a web server interface. The system aims to improve convenience, comfort, energy efficiency and security for users.
IRJET - IoT based Home Automation System through Voice Control using Google A...IRJET Journal
This document describes an IoT-based home automation system that allows control of electrical appliances using voice commands through a Google Assistant app. The system uses an Arduino microcontroller connected to ESP8266 WiFi modules and relays to control appliances. Users can control lights, fans, and other devices remotely using the Blynk app on their smartphone, which sends commands to the Arduino via ESP8266. This provides convenience and saves energy by automating appliance control. The system was tested successfully to control loads by voice commands through the Blynk and Google Assistant apps.
Naresh Kumar has 3 years of experience in designing and developing software for networking and mobile domains. He has expertise in SDN, cloud computing, NFV, and mobile application development. Some of his key contributions include developing SDN applications using the OpenDaylight controller, implementing network mechanisms in OpenStack, and creating an Android application to accumulate mobile sensor data. He has worked on several projects involving SDN testing, configuring SDN environments, and providing solutions to issues in the OpenDaylight controller.
Internet of Things IoT is a system of interrelated computing devices where all the things, including every physical object, can be connected making those objects intelligent, programmable and capable of interacting with humans. As more and more data are generated each day, IoT and its potential to transform how we communicate with machines and each other can change the world. The user operates the smart home devices year in year out, have produced mass operation data, but these data have not been utilized well in the past. This project focuses on the development of home automation system based on internet of things which allows the user to automate all the devices and appliances of home and merge them to provide seamless control over every side of their home. The data can be used to predict the user's behavior custom with the development of a machine learning algorithm, and then the prediction results can be employed to enhance the intelligence of a smart home system. The designed system not only gives the sensor data but also process it according to the requirement, for example switching on the light when it gets dark and it allows the user to control the household devices from anywhere. The cloud is used to send the sensor data through Wi Fi module and then a decision tree is implemented which decides the output of the electronic devices also, it is used to achieve the power control and local data exchanging which provide the user interface, store all the information corresponding to the specific house, and query the function information of an individual home appliance. Prof. Madhu B R | Vaishnavi K R | Dushyanth N Gowda | Tushar Jain | Sohan Chopdekar ""IoT Based Home Automation System over Cloud"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24005.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/24005/iot-based-home-automation-system-over-cloud/prof-madhu-b-r
N C C T Embedded Projects Intro & Presentationncct
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Integrator Roundtable Discussion: Facing the Future of AutomationInductive Automation
This webinar will bring together experienced system integrators from a variety of industries for a compelling discussion. Join us to learn how integrators are approaching some of today’s biggest challenges and helping customers realize the full potential of automation.
Get integrators’ insights on:
• "Brownfield" vs. "greenfield" projects
• Trends in HMI, SCADA, and MES
• Cloud vs. edge computing
• The importance of open standards
• The future of manufacturing jobs
• And more
Best Embedded Systems Projects Ideas In 2015elprocus
We provide you the Best Embedded Systems Projects Ideas In 2015. You can choose the best of your choice and interest from the list of topics we suggested. All new project ideas that are appearing focuses to improve the knowledge of Engineering students.
https://www.elprocus.com
Visit our page to get more ideas on Best Embedded Systems Projects Ideas In 2015 these ideas developed by professionals.
Elprocus provides free verified electronic projects kits around the world with abstracts, circuit diagrams, and free electronic software. We provide guidance manual for Do It Yourself Kits (DIY) with the modules at best price along with free shipping.
In the last few years the advancement technologies were improved in many ways using internet of things. It gives efficient and accurate results. Now we proposed a novel technique, which is used to operate any electrical component or components in home, office or any other place and control all these components like electrical bulbs, fans, refrigerators, etc. Through privacy based and customized way using electronic mail (e-mail). The experimental results conducted on Home Automation using Raspberry Pi.
What is Internet of things? What is smart home automation? How does a state-of-the-art home automation system work? What is the scope of IoT based home automation? How can Vinsol help you with IoT app development for home automation.
Home automation is a growing industry that allows users to control and monitor their home systems remotely using internet-connected devices. It provides convenience, control, and a sense of coolness to users. Common early applications included HVAC, lighting, audio/video, and intercom systems. Hardware interfaces like Arduino, Raspberry Pi, and ESP8266 modules connect sensors and devices to cloud services for remote access via apps and websites. The technology is moving towards more energy efficient green building features, advanced security including biometrics, and capabilities for monitoring vacant homes. It allows for flexible, programmable, and affordable automation of various systems and peripherals to make homes smarter and more efficient.
Home automation using android phones-Project 2nd phase pptthrishma reddy
This presentation will be useful for the Information science and Computer science students. It contains Use case diagrams, Activity diagrams and data flow diagrams along with details of other sensors.
"Analytics, Machine Learning & the Internet of Things"KristinHeitsch
This presentation discusses how businesses can leverage emerging technologies like machine learning and the Internet of Things. As data explodes due to sensors and intelligent devices, businesses need to take control of their data and use machine learning to make sense of it. The Internet of Things refers to the network of devices that connect to the internet and each other, and will be a $300 billion industry by 2020. For businesses to stay competitive, they must understand how to add value through technologies like monitoring, control, optimization and autonomy. Businesses also need to rethink their business models and analytics to adapt to disruptions from new technologies.
IBM Bluemix Nice Meetup #1 - CEEI NCA - 20160630 - IBM France Lab
This document outlines an agenda for the Nice Bluemix Meetup #1 on June 30, 2016 hosted by CEEI NCA. The agenda includes an introduction to IBM Bluemix, a presentation on connecting objects to Bluemix using IBM Watson IoT, a live demo of an industrial IoT application using Bluemix, and a presentation of a Smart Garden application developed for a hackathon and hosted on Bluemix. Attendees will also have a Q&A session and networking cocktail.
Topic: Augmented Internet of Things
Speaker: Francois Guibert (Executive Vice President and President, Greater China and South Asia Region STMicroeletronics)
This document discusses three approaches ("flavors") for developing smart products: leveraging existing products, retrofitting existing products, and building new smart products from scratch. It provides examples of each approach, including leveraging sensor data to improve fire detection and retrofitting a car sharing system. All three approaches can help drive industry transformation by enabling new business models and scaling effects as smart products connect and share more data. The document advocates considering each approach's constraints and potential for incremental versus disruptive innovation.
A Review Of Process Analytics In The Year 2012gautamkdas
The document reviews process analytics technologies and support approaches in 2012. Modular sampling systems and connectivity architectures allowed remote collaboration between technicians, engineers, vendors and sites. Intelligent applications and predictive diagnostics helped technicians resolve issues, while virtual teams collaborated across locations to solve complex problems. Process analytics involved both advanced technologies and effective human-machine collaboration facilitated by developments in mobile, wireless and interactive computing.
Microservices: The Future-Proof Framework for IoTCapgemini
Dr Michael Capone Principal Analyst - Capgemini
The data generated by IoT-enabled machines, vehicles and devices can provide companies with insight into user behaviour that they can use to create a personal connection with their customers. Companies are, therefore, scrambling to implement IoT systems in order to generate, capture, protect, and analyse this valuable data. But the insights created are only valuable when they trigger consequent decisions and timely actions. There are many potential users of IoT data such as marketing, sales, held service, product
development, customer support, operations, and supply chain not to mention external users like vendors and partners. Each user group needs to be able to access and select different data and apply different logic and analytic approaches to perform specific tasks.
Furthermore, each group can have unique usability requirements. As companies become more IoT mature and start to plan for “data actionability,” the disadvantages of a homogenous IoT stack or departmental systems become obvious. The best option from a data quality, user acceptance, and ROI perspective is a microservices IoT platform.
The document provides an overview of IoT (Internet of Things), including its definition, benefits, applications, challenges and future trends. Some key points:
- IoT connects physical devices over the internet and allows them to exchange data without human involvement. Billions of smart devices are part of the IoT ecosystem.
- IoT benefits organizations by improving efficiency, enhancing customer service, saving costs and generating more revenue through better decision making. Popular consumer applications include smart homes and wearables. Industrial IoT focuses on monitoring factory processes.
- Challenges of IoT include security vulnerabilities, privacy issues regarding data collection and sharing, and ensuring reliability across connected systems. Standards and regulations are needed to address these challenges
AllSeen Alliance members EnOcean Alliance, Insteon, Heaven Fresh Canada and Muzzley hosted a roundtable panel discussion at Mobile World Congress 2015.
Panelists: Eduardo Pinheiro, Muzzley
Graham Martin, EnOcean Alliance
Joe Gerber, Insteon
Imran Bashir, Heaven Fresh Canada Inc.
The document discusses the Internet of Things (IoT) in 3 paragraphs:
1) It defines IoT as connecting physical devices to the Internet and using sensors to collect data and turn it into useful insights. This creates new opportunities for businesses and economies.
2) IoT is enabling disruptive changes across industries through technologies like the Industrial Internet which combines machines, analytics and insights.
3) Popular IoT applications include home automation, healthcare devices, smart cities infrastructure, and banking solutions, though security is a major concern that developers must address.
Integrated smart sensors provide continuous process and diagnostic data to increase productivity, minimize downtime, and enable faster product changeovers. Smart sensors offer advanced diagnostic information to facilitate preventative maintenance and reduce unplanned downtime. Multiple sensor profiles stored in programmable logic controllers allow fast changeovers between products to increase throughput by 5-10%.
Internet of Things IoT is a system of interrelated computing devices where all the things, including every physical object, can be connected making those objects intelligent, programmable and capable of interacting with humans. As more and more data are generated each day, IoT and its potential to transform how we communicate with machines and each other can change the world. The user operates the smart home devices year in year out, have produced mass operation data, but these data have not been utilized well in the past. This project focuses on the development of home automation system based on internet of things which allows the user to automate all the devices and appliances of home and merge them to provide seamless control over every side of their home. The data can be used to predict the user's behavior custom with the development of a machine learning algorithm, and then the prediction results can be employed to enhance the intelligence of a smart home system. The designed system not only gives the sensor data but also process it according to the requirement, for example switching on the light when it gets dark and it allows the user to control the household devices from anywhere. The cloud is used to send the sensor data through Wi Fi module and then a decision tree is implemented which decides the output of the electronic devices also, it is used to achieve the power control and local data exchanging which provide the user interface, store all the information corresponding to the specific house, and query the function information of an individual home appliance. Prof. Madhu B R | Vaishnavi K R | Dushyanth N Gowda | Tushar Jain | Sohan Chopdekar ""IoT Based Home Automation System over Cloud"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd24005.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/24005/iot-based-home-automation-system-over-cloud/prof-madhu-b-r
N C C T Embedded Projects Intro & Presentationncct
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Integrator Roundtable Discussion: Facing the Future of AutomationInductive Automation
This webinar will bring together experienced system integrators from a variety of industries for a compelling discussion. Join us to learn how integrators are approaching some of today’s biggest challenges and helping customers realize the full potential of automation.
Get integrators’ insights on:
• "Brownfield" vs. "greenfield" projects
• Trends in HMI, SCADA, and MES
• Cloud vs. edge computing
• The importance of open standards
• The future of manufacturing jobs
• And more
Best Embedded Systems Projects Ideas In 2015elprocus
We provide you the Best Embedded Systems Projects Ideas In 2015. You can choose the best of your choice and interest from the list of topics we suggested. All new project ideas that are appearing focuses to improve the knowledge of Engineering students.
https://www.elprocus.com
Visit our page to get more ideas on Best Embedded Systems Projects Ideas In 2015 these ideas developed by professionals.
Elprocus provides free verified electronic projects kits around the world with abstracts, circuit diagrams, and free electronic software. We provide guidance manual for Do It Yourself Kits (DIY) with the modules at best price along with free shipping.
In the last few years the advancement technologies were improved in many ways using internet of things. It gives efficient and accurate results. Now we proposed a novel technique, which is used to operate any electrical component or components in home, office or any other place and control all these components like electrical bulbs, fans, refrigerators, etc. Through privacy based and customized way using electronic mail (e-mail). The experimental results conducted on Home Automation using Raspberry Pi.
What is Internet of things? What is smart home automation? How does a state-of-the-art home automation system work? What is the scope of IoT based home automation? How can Vinsol help you with IoT app development for home automation.
Home automation is a growing industry that allows users to control and monitor their home systems remotely using internet-connected devices. It provides convenience, control, and a sense of coolness to users. Common early applications included HVAC, lighting, audio/video, and intercom systems. Hardware interfaces like Arduino, Raspberry Pi, and ESP8266 modules connect sensors and devices to cloud services for remote access via apps and websites. The technology is moving towards more energy efficient green building features, advanced security including biometrics, and capabilities for monitoring vacant homes. It allows for flexible, programmable, and affordable automation of various systems and peripherals to make homes smarter and more efficient.
Home automation using android phones-Project 2nd phase pptthrishma reddy
This presentation will be useful for the Information science and Computer science students. It contains Use case diagrams, Activity diagrams and data flow diagrams along with details of other sensors.
"Analytics, Machine Learning & the Internet of Things"KristinHeitsch
This presentation discusses how businesses can leverage emerging technologies like machine learning and the Internet of Things. As data explodes due to sensors and intelligent devices, businesses need to take control of their data and use machine learning to make sense of it. The Internet of Things refers to the network of devices that connect to the internet and each other, and will be a $300 billion industry by 2020. For businesses to stay competitive, they must understand how to add value through technologies like monitoring, control, optimization and autonomy. Businesses also need to rethink their business models and analytics to adapt to disruptions from new technologies.
IBM Bluemix Nice Meetup #1 - CEEI NCA - 20160630 - IBM France Lab
This document outlines an agenda for the Nice Bluemix Meetup #1 on June 30, 2016 hosted by CEEI NCA. The agenda includes an introduction to IBM Bluemix, a presentation on connecting objects to Bluemix using IBM Watson IoT, a live demo of an industrial IoT application using Bluemix, and a presentation of a Smart Garden application developed for a hackathon and hosted on Bluemix. Attendees will also have a Q&A session and networking cocktail.
Topic: Augmented Internet of Things
Speaker: Francois Guibert (Executive Vice President and President, Greater China and South Asia Region STMicroeletronics)
This document discusses three approaches ("flavors") for developing smart products: leveraging existing products, retrofitting existing products, and building new smart products from scratch. It provides examples of each approach, including leveraging sensor data to improve fire detection and retrofitting a car sharing system. All three approaches can help drive industry transformation by enabling new business models and scaling effects as smart products connect and share more data. The document advocates considering each approach's constraints and potential for incremental versus disruptive innovation.
A Review Of Process Analytics In The Year 2012gautamkdas
The document reviews process analytics technologies and support approaches in 2012. Modular sampling systems and connectivity architectures allowed remote collaboration between technicians, engineers, vendors and sites. Intelligent applications and predictive diagnostics helped technicians resolve issues, while virtual teams collaborated across locations to solve complex problems. Process analytics involved both advanced technologies and effective human-machine collaboration facilitated by developments in mobile, wireless and interactive computing.
Microservices: The Future-Proof Framework for IoTCapgemini
Dr Michael Capone Principal Analyst - Capgemini
The data generated by IoT-enabled machines, vehicles and devices can provide companies with insight into user behaviour that they can use to create a personal connection with their customers. Companies are, therefore, scrambling to implement IoT systems in order to generate, capture, protect, and analyse this valuable data. But the insights created are only valuable when they trigger consequent decisions and timely actions. There are many potential users of IoT data such as marketing, sales, held service, product
development, customer support, operations, and supply chain not to mention external users like vendors and partners. Each user group needs to be able to access and select different data and apply different logic and analytic approaches to perform specific tasks.
Furthermore, each group can have unique usability requirements. As companies become more IoT mature and start to plan for “data actionability,” the disadvantages of a homogenous IoT stack or departmental systems become obvious. The best option from a data quality, user acceptance, and ROI perspective is a microservices IoT platform.
The document provides an overview of IoT (Internet of Things), including its definition, benefits, applications, challenges and future trends. Some key points:
- IoT connects physical devices over the internet and allows them to exchange data without human involvement. Billions of smart devices are part of the IoT ecosystem.
- IoT benefits organizations by improving efficiency, enhancing customer service, saving costs and generating more revenue through better decision making. Popular consumer applications include smart homes and wearables. Industrial IoT focuses on monitoring factory processes.
- Challenges of IoT include security vulnerabilities, privacy issues regarding data collection and sharing, and ensuring reliability across connected systems. Standards and regulations are needed to address these challenges
AllSeen Alliance members EnOcean Alliance, Insteon, Heaven Fresh Canada and Muzzley hosted a roundtable panel discussion at Mobile World Congress 2015.
Panelists: Eduardo Pinheiro, Muzzley
Graham Martin, EnOcean Alliance
Joe Gerber, Insteon
Imran Bashir, Heaven Fresh Canada Inc.
The document discusses the Internet of Things (IoT) in 3 paragraphs:
1) It defines IoT as connecting physical devices to the Internet and using sensors to collect data and turn it into useful insights. This creates new opportunities for businesses and economies.
2) IoT is enabling disruptive changes across industries through technologies like the Industrial Internet which combines machines, analytics and insights.
3) Popular IoT applications include home automation, healthcare devices, smart cities infrastructure, and banking solutions, though security is a major concern that developers must address.
Integrated smart sensors provide continuous process and diagnostic data to increase productivity, minimize downtime, and enable faster product changeovers. Smart sensors offer advanced diagnostic information to facilitate preventative maintenance and reduce unplanned downtime. Multiple sensor profiles stored in programmable logic controllers allow fast changeovers between products to increase throughput by 5-10%.
Sensoplex - Your Partner in the Wearables' MarketSensoplex
Sensoplex is a Silicon Valley company expert in sensing, wireless and low power computing technologies.
We provide leading edge professional services for the definition, development and production of innovative wearables for fitness, wellness and health applications.
Our development kits enable customers to rapidly test & expand their ideas and applications in their market.
Our proven flexible hardware & software frameworks provide a solid foundation for customers to develop their product in record time.
Working together we can reduce your time-to-money by as much 50%.
Presented the 28th October 2015 at the 6th International Conference and Exhibition on body Scanning Technologies 2015, Hometrica Consulting, Lugano, Switzerland.
The access to the 3D representation of people’s body shape has multiple applications to consumer goods which performance is related to human body dimensions or shape. This is the case of wearables such as clothing, footwear, headgear, orthotics, or equipment/environments such as furniture, transports or workstations. Some of the existing and potential applications of 3D human representations include personalisation, virtual try-on or size allocation for wearables or product configuration/adjustment for equipment/environments.
However, the cost of 3D scanners is high; the devices are too bulky for homes and retail stores; and its proper use requires expertise to get the relevant parameters from the 3D object (e.g. measurements). These three barriers are currently hindering the massive spreading of 3D scanners as consumer good or as typical in-store appliance.
This paper describes an array of approaches for realistically estimating human 3D shapes (i.e. full bodies or feet) using a regular smartphone or just entering a set of parameters (e.g. age, gender and self-taken measurements). The proposed approaches are based on data-driven 3D reconstructions, using parameterised shape spaces created from large 3D human body or feet databases. The algorithm finds the combination of shape parameters that best matches either the silhouettes extracted from the images or the body measurements entered.
Despite not being actual body scanners, these solutions are easy-to-use and can provide enough accuracy for applications such as virtual try-on, made-to-measure or size allocation of certain types of wearables. Moreover, they can be distributed to the final consumer or to the points of sale at a really reduced cost (or even for free), thus overcoming the main barriers to the massive spreading of its use in e-commerce, new retail experiences, new production pipelines or new business models.
In order to illustrate these technologies, some examples of application to different contexts (i.e. virtual worlds, e-commerce and personalisation) are presented: virtual try-on of female fashion (VisuaLook), size allocation for childrenswear (KIDSIZE), personalised comfort insoles (Sunfeet) and personalised shoes (Feetz).
The document discusses how machine data from various sources such as IoT devices, industrial systems, mobile devices, and other systems can be collected and analyzed using Splunk software. Splunk provides capabilities for data ingestion, indexing, searching, analyzing, and visualizing large amounts of machine data. It also discusses how Splunk has been used by companies in various industries to gain insights from their machine data to improve operations, security, customer experience, and business outcomes. Specific use cases highlighted include predictive maintenance, anomaly detection, supply chain optimization, and understanding customer behavior.
In this presentation, Aeshwarya introduces IoT and associated trends. Her interest area is in the application layer of the IoT devices where he wants to work on smart healthcare applications.
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
AI and Machine Learning for the Connected Home with Stephen GalsworthyDatabricks
Quby is the creator and provider of Toon, a leading European smart home platform. We enable Toon users to control and monitor their homes using both an in-home display and app. As a data driven company, we use AI and machine learning to generate actionable insights for our end users. Using the data we collect via our IoT devices we have introduced multiple data driven services, including an energy waste checker and a boiler monitoring service. In this talk, Stephen will describe how AI and machine learning are implemented on the Toon platform, and will show multiple AI use cases relating to the connected home. We’ll take a look at how Deep Learning algorithms are used to detect inefficient appliances from electricity meter data and how streaming algorithms allow users to be alerted to anomalies with their heating systems in near real-time. Stephen will share the experiences from the Data Science and Data Engineering teams at Quby with bringing data science algorithms from R&D to production and the lessons learned in offering multiple data driven services to hundreds of thousands of users on a daily basis.
Industrial Internet, Should I be Interested?ionSign Oy
The document discusses the opportunities and challenges presented by the Industrial Internet and smart, connected products. It notes that the Industrial Internet is expected to create $14.2 trillion in value by 2030 and that 70% of this value will come from business-to-business applications. The Industrial Internet involves using data from connected machines and devices to create better solutions for work and personal needs. Successful implementation requires digitally reimagining businesses, creating new business models, starting small and investing gradually, and dealing with internal resistance to changes enabled by data. Both new customer value and strategic challenges will result, including decisions around features, data ownership, business models, and company scope.
Similar to Data science, self learning algorithms (by Alexander Frimout & Max Nie) (20)
This document provides an overview of artificial intelligence (AI) and machine learning. It begins by defining AI as computer systems able to perform cognitive tasks like reasoning, decision making, perception, and language understanding. It then discusses what AI is good at, including classification, pattern recognition, prediction, and information retrieval. The document also covers different types of machine learning algorithms like supervised and unsupervised learning. It aims to demystify key AI concepts and discuss opportunities for applying AI in the chemical industry.
To explain the tremendous evolution in ICT the last decades, one typically refers to the Law of Moore. Today we’re facing with new languages and methodologies popping up like buzz words. Understanding the evolution of programming and cornerstone concepts will help you to position and value future programming languages and trends.
Although GPS developments started in 1967, it’s only in the early 70’s that NASA launched its first Earth Observation satellite. Contemporary technologies like satellite navigation and remote sensing are key in our daily life. Still, we ain’t seen nothing yet. Explore the potential of these technologies in your future products or services, and find a way to get things done.
The document discusses the history and evolution of human-computer interfaces from early mechanical switches to modern touchscreens, voice assistants, virtual and augmented reality. It notes that while technology has advanced greatly, good interface design must still prioritize usability, clarity and putting the human user first. Emerging areas discussed include mid-air haptics, brain-computer interfaces, and the growing importance of interfaces as a driver of innovation across many industries.
At the end of the ’60s, the first research programs started on humanoid robots. Although this technology inspired many of us, it’s only now that we see first applications arise. What’s the status of the robotics technology? How is it impacted by other technologies like AI, RF and energy storage? Let’s discuss the evolution and potential for your business based on recent projects and technologies available at our partners and alumni start-ups.
Let’s discuss the boundary conditions to innovate successfully based on some cases in Flanders and the Netherlands. Most governments are taking about half of their gross national product for their account. They can be better off by being open to innovation in the public purchase process and act as a launching customer for their industry and start-ups.
1) The document discusses moonshot projects like landing on the moon and their ability to inspire and drive innovation.
2) It provides examples of moonshot projects from history like the Apollo missions and modern examples like projects from Google X.
3) Key lessons from successful moonshots are that they require bold goals like 10x improvements, committed leadership, and tolerance for failure during development.
The human aspect in driving innovation is not to be underestimated. Driving innovation is done by implementing the necessary tools and structures. However driving desired behavior on individual and team level is at least equally important. Möbius will share insights in how to build an innovation and continuous improvement culture.
The key concept in any start-up? The freedom to experiment as basis for accelerated learning. Or like Pink Floyd sang in the 70’s: “We don’t need no education.” Is your FabLab ready for it?
How do you set up your venture and accelerate growth? Start-ups are in a fundamental different position than corporates. As a consequence they play a different ball game. What can the start-up culture learn from the ’60s?
What are the differences between popular innovation project management methodologies? Why does project management often fail? Learn how risk assessment should define your methodology in order to become a real innovation factory. The waterfall methodology has been promoted for years as the best practice for IR&D management. These days agile and scrum are increasingly popular as alternative. Hater or believer? Good or bad? Get guided through our body of knowledge as published in the Inspire Toolbox.
Project management in innovation can only be successful if it’s driven by a robust methodology integrating a clear quality assurance concept. What can trendy methodologies learn from the house of quality?
50 years ago, innovation centers like Xerox or Philips’ Natlab set the benchmark with their methodologies on organizing innovation. Today, innovation is influenced by rapid-moving triggers and companies have to organize accordingly. Discover current best practices on organizing your innovation ecosystem.
How do you reinvent a mature business with the right cocktail of user insights and technologies? The message is clear: choose for differentiation or servitization, or loose.
AI has an inevitable impact on your next generation products and services. Things evolved relatively slow since the first AI experiments in the ’60s. Today, AI is accelerating as no other technology has done before. How will the 4th wave of Artificial Intelligence transform your future business?
Discover the newest insights in understanding value drivers and their impact on new products and services. From technology improvement towards platform economy with the value pyramid. How to use new design skills and service design to create experience economy?
The adoption of innovative IoT technology in smart cities seems to go slower than the utopian predictions of the last decade. This might be because the traditional business-to-government model is no longer sufficient to bear the investment cost and get these systems operational. Multi-sided business models can offer
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Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
Data science, self learning algorithms (by Alexander Frimout & Max Nie)
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Template presentation Innovation Day 2016CONFIDENTIAL
Max Nie
Coordinator digital lab & project office
Max.Nie@Verhaert.com
Alexander Frimout
Consultant InnoLab
alexander.frimout@verhaert.com
TRACK 3: EVOLVING ARCHITECTURES
DATA SCIENCE:
SELF LEARNING ALGORITHMS
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EVOLVING ARCHITECTURES
Learning machines in a new data world
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A TRADITIONAL HARDWARE PRODUCT IS “MATURE”
Everything you need
(and will ever need)
In one handy box
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WHAT HAPPENS TO THE BOX ONCE IT LEAVES THE COMPANY?
“We have no idea”
Sounds familiar?
Maintenance?
How is it used?
How long does it last?
What goes wrong?
Are people happy with our product?
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THE PRODUCT IS EXPECTED TO ALWAYS PERFORM
TO ITS STANDARD
Sometimes an “error” with a product doesn’t show until later…
…or a users mess up the intended use of a product...
…and the only option is to fix/improve it in a next generation
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SOFTWARE DEVELOPMENT TAKES A DIFFERENT APPROACH
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EVEN SO CALLED “MATURE” SOFTWARE IS
NEVER TRULY FINISHED
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NEW, CONNECTED PRODUCTS ALSO HAVE THIS POSSIBILITY
Self learning machines can add enormous value!
• Personal experience tailored to the user
• Evolving products that promise more
• Better understanding of your own product
• Reduced costs for user & manufacturer
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ML CAN DO VERY COOL STUFF (BUT WE DON’T
FULLY UNDERSTAND WHY)
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• The difference between 95% and 99% accuracy
in speech recognition is game changing
• Training a speech recognition app requires $100 of electricity
• 1 super computer to run a Neural Net with 100 billion connections
• 10^19 floating point operations on thousands of parallel GPUs
• 4 TB training data.
THE CATCH: ML REQUIRES TRULY MASSIVE
AMOUNTS OF TRAINING DATA
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ML IS NOT NEW TECHNOLOGY,
THE BREAKTHROUGH IS IN THE SCALE
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Can you ignore this?
How do you play this game?
ML IS DRIVEN BY VERY BIG TECH WITH VERY BIG DATA
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A new class of software interfaces that interacts
at our own messy level
• Pictures
• Speech
• Text
• Expressions
• Behavior
ML ENABLES PRODUCTS THAT UNDERSTAND
AND INTERACT WITH US
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SPEECH AND NATURAL LANGUAGE RECOGNITION
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1. In this ecosystem volumes of training data are the currency
• Your added value is determined by how much you really know
2. Artificial Intelligence is the next computing platform
• New value chains and classes of products will emerge
3. Software and CPU’s are cheap; training data is not
• Algorithms and hardware are not a source of differentiation,
• Building training data is the basis for ROI
4. Performance of smart product continuously grows based on
the flywheel of user generated data feedback
• Through machine learning
• Through superior user insights
IMPLICATIONS
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1. What data assets do you own?
2. What data assets could you create?
YOUR DATA ASSETS
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HOW TO PLAY
Principles of smart product innovation
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Buying not just a product,
but a promise
Some services are only
possible after a sufficiently
large data set/user base
Our world is evolving fast,
we expect our products to
evolve with us
Making the most out of data to improve your product
INCREASING VALUE WITH LEARNING & ADAPTIVE PRODUCTS
Tesla cars have driven over 150
million miles autonomously
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Don’t just start collecting data without first knowing
Build the use cases for your product/service:
• What is the added value of this solution? What advantages
or improvements am I offering my users?
• Is this a good fit with my product? Can I do this technically?
• What market am I targeting? Can I make a profit with this?
BUILDING THE RIGHT USE CASES
WHY
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Involve experts from all fields
Typically during a pressure
cooker or sprint session
Keep an open mind and build
a wide range of diverging
cases
Select the right ones by
objectively criticizing all
aspects
BUILDING USE CASES REQUIRES
A MULTIDISCIPLINARY APPROACH!
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SOME EXAMPLES OF USE CASES
IF …
What is my trigger?
I detect the performance of my
elevator dropping
I can monitor the heat profile
and exact hotspot of a
transformer
THEN …
What action can I perform?
I want to dispatch a technician
early
I can set up cooling much
more rapidly and efficiently
BECAUSE …
What is the underlying driver?
I want to prevent is from
malfunctioning later
Getting stuck in an elevator
causes huge dissatisfaction
with my hotel guests
Better heat management can
increase operating life by
several years
Uniform cooling is inefficient
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3 THINGS ARE NEEDED FOR MACHINE LEARNING
1. Training Data which has been tagged, categorized,
or otherwise sorted by humans.
2. Software libraries which build the machine learning
models by evaluating training data.
3. Hardware CPUs and GPUs which run the software’s
calculations.
More and more
becoming commodities
• Computation in the cloud
• Low powered networking
• Low powered CPU
• Minimal storage
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1. Product performance increases as more training data is fed
2. New user growth from ever increasing performance
3. Unique insights from product data drive product evolution and revolution
ONGOING PRODUCT PERFORMANCE
IMPROVEMENT DRIVEN BY DATA
More
users
More
data
Better
product/
service
Better
algorithms
1. Training data
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Unique data can allow you to provide
a unique service or product
1. Many different factors can make
your data unique!
2. You don’t have to generate all data yourself
3. Putting together all the right pieces of the
puzzle is important
DATA IS NOT A COMMODITY!
Unique location
Established base
Always-on machinery
Product data
User data
Infrastructure access
Pre-installed sensor
You have more access to unique data than you think!
Product usage
Unique technology
Financial dataIntelligentX:
The beer that’s continuously
getting better
Market data
R&D testing
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• Hardware to collect & process data can & should be cheap!
• Cheap sensors
• Computation in the cloud is mandatory to exploit big data assets
• Low powered networking
• Low powered CPU
• Minimal storage
• Hardware design must enable data collection for the right use
cases and contexts
• Think beyond mobile apps to wearables and other devices
• Form factor and price will drive hardware innovation, not performance
IMPACT ON HARDWARE
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• Hiring people to produce training data is too expensive
• So you must acquire an audience and let them create your training data
• The ideal data driven application creates training data and delivers value,
powered by the data captured
Offer value or meaning in return for data
THE IDEAL DATA DRIVEN APPLICATION
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1. It tracks your steps
2. It tracks your distance run by GPS
3. It loads your Spotify playlists
4. It connects to online services
5. It synchs data with fitness apps
6. It’s SDK allows 3rd party development
7. It’s an Alexa powered PA:
• “Alexa, play my workout list”
• “Alexa, what will the weather/traffic be?
• “Alexa, what’s the latest news”
• “Alexa, add milk to my shopping list”
• “Alexa, set the house temperature to 22°
• “Alexander, you have one meeting today
PEBBLE CORE: A $69 SMART PHONE
REPLACEMENT (FOR RUNNING)
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Hardware suppliers can become service providers!
Transformation process for organization
Requires you to consider alternative business models!
THIS WILL IMPACT YOUR ORGANIZATION & BUSINESS MODEL!
From …
Buying a car
To …
Subscribing to a flexible
transportation service
Who will handle user communication?
Do we need an IT department? Who are our new stakeholders?
How will we handle data?
Map your new ecosystem!
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Try to think of use cases for improving a product with data for…
EXERCISE
…a pillow
Hint: you can include an actuators!
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WHAT DOES IT TAKE
Developing self learning algorithms
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DATA DRIVEN INNOVATION PROCESS
Create smart
concept (use case)
Solve the data
science problem
Develop &
introduce productINNOVATE
Have training
data
INSIGHTS, PERFORMANCE
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DATA SCIENCE PROCESS
DATA
QUESTION
DATA
PRODUCT
TIDY
DATA
DATA
PROCESSING
DATA
ANALYSIS
Sneakernet
Manual download
Scraping Custom scripts
Descriptive
Exploratory
Predictive
Inferential
Causal
Mechanistic
Audience analysis
Premises
Conclusion(s)
• Answerable
with data
• Data is
obtainable
• Business &
user validated
• Explore
• Clean
• Transform
• Combine
• Descriptive
analysis
• Exploratory
analysis
• Inferential
analysis
• Predictive
analysis
• Prescriptive
analysis
• Post
• Visualization
• App
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‘SUPERVISED LEARNING’ BASED ON TRAINING DATA SETS
Regression problems Classification problems
Hypothesis Function Cost Function
Source: http://www.andrewng.org/
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THE OPTIMAL HYPOTHESIS MINIMIZES THE COST FUNCTION
Iterative convergence
Source: http://www.andrewng.org/
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GRADIENT DESCENT ALGORITHM FOR COST
FUNCTION MINIMIZATION
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THE JOY OF CONVEX COST FUNCTIONS
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UNFORTUNATELY REAL PROBLEMS ARE NONLINEAR
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THIS REQUIRES A MORE FLEXIBLE APPROACH
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NON LINEAR CLASSIFICATION
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MODELING THE XNOR FUNCTION WITH A NEURAL NETWORK
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BUILDING COMPLEXITY AND SCALE WITH NEURAL
NETWORKS
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TRAINING STEP 1: DEFINE NEURAL NETWORK ARCHITECTURE
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TRAINING STEP 2: EVALUATE COST AND PARTIAL
DERIVATIVE FUNCTIONS
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TRAINING STEP 3: MINIMIZE NON CONVEX COST FUNCTION
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HOW DEEP LEARNING OVERCOMES THE BIAS
VARIANCE TRADE-OFF
1. Example: set benchmark for speech recognition at human error rate of 1%
2. If training error is too high, e.g. 5% then you have a bias issue
run a bigger neural network
3. If validation set error is too high, e.g. 6% then you have a variance issue
get more data
4. Otherwise you’re done.
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• Machine learning libraries: Theano, Keras, NumPy,…
• Big data tooling: Hadoop, MapReduce, Spark,…
• MLaaS by Amazon, Google, IBM, Microsoft,…
• And their cloud API’s for Speech, Vision, Natural Language, Translation
TAKE ADVANTAGE OF OPEN SOURCE AND CLOUD
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EXAMPLE CODE TO MODEL AND FIT A NN USING
KERAS AND NUMPY
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform',
activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy' , optimizer='adam',
metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
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1. Commercial data partners
• Big tech companies
• Data brokers in all industry domains
• High resolution satellite data
2. Public open data
• Government agencies
• Academic institutions
• International organizations
• NGO’s
• Space agencies
ENRICH YOUR DATA ASSETS WITH OPEN DATA
FOR UNIQUE INSIGHTS
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1. Large scale supervised machine learning enables adaptive,
self learning products.
2. Large volumes of training data is a key competitive advantage,
find or make your own data assets!
3. Finding the right use cases and answering the right data questions
is critical, and requires a multidisciplinary effort.
4. Algorithms and computing are becoming commoditized. Leverage
open source and cloud computing and focus on strategic differentiation
based on unique data.
CONCLUSIONS
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VERHAERT CONNECT / CWI CASE:
ILLEGAL PARKING PREDICTION
• Training data asset: several years of scan car data
• Application concept: a heat map showing illegal parking probabilities
• Data question: predict illegal parking probabilities for each city neighborhood
• Modeling approach: discrete choice regression model
• Cost function: TBD
• Algorithms: maximum likelihood estimators
• R&D plan: 3 months
• Product development plan: 3 months
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VERHAERT CONNECT: A NATURAL PROPOSITION
SENSOR FUSION
TECHNOLOGY
INTEGRATION
ALGORITHMS
CONTEXT SENSITIVE
USER CENTRICBIG DATA
ADDED VALUE
MULTIDISCIPLINARY
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Innovation Day is an initiative of Masters in Innovation,
the umbrella brand of the Verhaert Group which aims
to connect, train and accelerate professional innovators.
Kruibeke
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E info@verhaert.com
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