Recording: https://www.youtube.com/watch?v=DxTetwYsXvo&index=1&list=PLiVCejcvpsevQ_I9oDIK6eIgau45fWje2
The Mbed Simulator allows you to cross-compile Mbed OS 5 applications and run them on your computer.
Running Android on the Raspberry Pi: Android Pie meets Raspberry Pi
Slides from a lightning talk at FOSDEM 2019
https://fosdem.org/2019/schedule/event/android_pi/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-google
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Pete Warden, Staff Research Engineer and TensorFlow Lite development lead at Google, presents the "Using TensorFlow Lite to Deploy Deep Learning on Cortex-M Microcontrollers" tutorial at the May 2019 Embedded Vision Summit.
Is it possible to deploy deep learning models on low-cost, low-power microcontrollers? While it may be surprising, the answer is a definite “yes”! In this talk, Warden explains how the new TensorFlow Lite framework enables creating very lightweight DNN implementations suitable for execution on microcontrollers. He illustrates how this works using an example of a 20 Kbyte DNN model that performs speech wake word detection, and discusses how this generalizes to image-based use cases. Warden introduces TensorFlow Lite, and explores the key steps in implementing lightweight DNNs, including model design, data gathering, hardware platform choice, software implementation and optimization.
Running Android on the Raspberry Pi: Android Pie meets Raspberry Pi
Slides from a lightning talk at FOSDEM 2019
https://fosdem.org/2019/schedule/event/android_pi/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-google
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Pete Warden, Staff Research Engineer and TensorFlow Lite development lead at Google, presents the "Using TensorFlow Lite to Deploy Deep Learning on Cortex-M Microcontrollers" tutorial at the May 2019 Embedded Vision Summit.
Is it possible to deploy deep learning models on low-cost, low-power microcontrollers? While it may be surprising, the answer is a definite “yes”! In this talk, Warden explains how the new TensorFlow Lite framework enables creating very lightweight DNN implementations suitable for execution on microcontrollers. He illustrates how this works using an example of a 20 Kbyte DNN model that performs speech wake word detection, and discusses how this generalizes to image-based use cases. Warden introduces TensorFlow Lite, and explores the key steps in implementing lightweight DNNs, including model design, data gathering, hardware platform choice, software implementation and optimization.
LATEST AND ADVANCED SEMINAR REPORT ON RASPBERRY PI AS PER UNIVERSITY FORMAT.
Raspberry Pi is a credit-card sized computer manufactured and designed in the United Kingdom by the Raspberry Pi foundation with the intention of teaching basic computer science to school students and every other person interested in computer hardware, programming and DIY-Do-it Yourself projects.
This is some kind of report for implementation and experimentation about "Hardware Accelerated WebKitGTK+ for the embedded device" which is running on the Raspberry Pi device in real. Authored by Changseok Oh, who is working in the Collabora from 2013.
Slide deck for my presentation at the Google Developer Group San Francisco. Step-by-step guide to turning a ThinkPad X220 into a Chromium OS Android development machine. Covers hardware, firmware and software upgrades. Dynamic up-to-date slides at https://goo.gl/ivaugY
Presented at FITC Toronto 2016
See details at www.fitc.ca
Hear and walk away with the best open source libraries and tools Jam3 has produced out of its collection of over 100 repositories. Learn how an award-winning agency has gone from being closed source to sharing its “secret sauce” with the community while still staying sane and profiting.
Objective
Exploring some of Jam3’s open source gems
Target Audience
Web developers, digital agencies, creative coders.
Five Things Audience Members Will Learn
Why you should Open Source
Jam3’s Open Source tools and libraries
How to Open Source your company
How to manage over 100 Open Source repositories
How to create more reusable and modular code
My presentation for Google Developer Group San Francisco. Step-by-step guide to turning a ThinkPad X220 into a Chromium OS Android development machine. Covers flashing modified Coreboot firmware, building and installing Chromium OS from source, and hardware upgrades.
Updated slides at https://goo.gl/ivaugY
The Motorola Evoke QA4 The demo discusses two applications running on separate OSes, working seamlessly together on a single low-cost ARM processor, with no performance degradation.
Continuous integration and deployment has become an increasingly standard and common practice in software development. However, doing this for machine learning models and applications introduces many challenges. Not only do we need to account for standard code quality and integration testing, but how do we best account for changes in model performance metrics coming from changes to code, deployment framework or mechanism, pre- and post-processing steps, changes in data, not to mention the core deep learning model itself?
In addition, deep learning presents particular challenges:
* model sizes are often extremely large and take significant time and resources to train
* models are often more difficult to understand and interpret making it more difficult to debug issues
* inputs to deep learning are often very different from the tabular data involved in most ‘traditional machine learning’ models
* model formats, frameworks and the state-of-the art models and architectures themselves are changing extremely rapidly
* usually many disparate tools are combined to create the full end-to-end pipeline for training and deployment, making it trickier to plug together these components and track down issues.
We also need to take into account the impact of changes on wider aspects such as model bias, fairness, robustness and explainability. And we need to track all of this over time and in a standard, repeatable manner. This talk explores best practices for handling these myriad challenges to create a standardized, automated, repeatable pipeline for continuous deployment of deep learning models and pipelines. I will illustrate this through the work we are undertaking within the free and open-source IBM Model Asset eXchange.
LATEST AND ADVANCED SEMINAR REPORT ON RASPBERRY PI AS PER UNIVERSITY FORMAT.
Raspberry Pi is a credit-card sized computer manufactured and designed in the United Kingdom by the Raspberry Pi foundation with the intention of teaching basic computer science to school students and every other person interested in computer hardware, programming and DIY-Do-it Yourself projects.
This is some kind of report for implementation and experimentation about "Hardware Accelerated WebKitGTK+ for the embedded device" which is running on the Raspberry Pi device in real. Authored by Changseok Oh, who is working in the Collabora from 2013.
Slide deck for my presentation at the Google Developer Group San Francisco. Step-by-step guide to turning a ThinkPad X220 into a Chromium OS Android development machine. Covers hardware, firmware and software upgrades. Dynamic up-to-date slides at https://goo.gl/ivaugY
Presented at FITC Toronto 2016
See details at www.fitc.ca
Hear and walk away with the best open source libraries and tools Jam3 has produced out of its collection of over 100 repositories. Learn how an award-winning agency has gone from being closed source to sharing its “secret sauce” with the community while still staying sane and profiting.
Objective
Exploring some of Jam3’s open source gems
Target Audience
Web developers, digital agencies, creative coders.
Five Things Audience Members Will Learn
Why you should Open Source
Jam3’s Open Source tools and libraries
How to Open Source your company
How to manage over 100 Open Source repositories
How to create more reusable and modular code
My presentation for Google Developer Group San Francisco. Step-by-step guide to turning a ThinkPad X220 into a Chromium OS Android development machine. Covers flashing modified Coreboot firmware, building and installing Chromium OS from source, and hardware upgrades.
Updated slides at https://goo.gl/ivaugY
The Motorola Evoke QA4 The demo discusses two applications running on separate OSes, working seamlessly together on a single low-cost ARM processor, with no performance degradation.
Continuous integration and deployment has become an increasingly standard and common practice in software development. However, doing this for machine learning models and applications introduces many challenges. Not only do we need to account for standard code quality and integration testing, but how do we best account for changes in model performance metrics coming from changes to code, deployment framework or mechanism, pre- and post-processing steps, changes in data, not to mention the core deep learning model itself?
In addition, deep learning presents particular challenges:
* model sizes are often extremely large and take significant time and resources to train
* models are often more difficult to understand and interpret making it more difficult to debug issues
* inputs to deep learning are often very different from the tabular data involved in most ‘traditional machine learning’ models
* model formats, frameworks and the state-of-the art models and architectures themselves are changing extremely rapidly
* usually many disparate tools are combined to create the full end-to-end pipeline for training and deployment, making it trickier to plug together these components and track down issues.
We also need to take into account the impact of changes on wider aspects such as model bias, fairness, robustness and explainability. And we need to track all of this over time and in a standard, repeatable manner. This talk explores best practices for handling these myriad challenges to create a standardized, automated, repeatable pipeline for continuous deployment of deep learning models and pipelines. I will illustrate this through the work we are undertaking within the free and open-source IBM Model Asset eXchange.
Adding intelligence to your LoRaWAN deployment - The Things Virtual ConferenceJan Jongboom
LoRaWAN devices are typically simple, they grab some sensor data and deliver it back to the network. By adding some embedded machine learning we can make them a lot more intelligent!
Teaching your sensors new tricks with Machine Learning - CENSIS Tech Summit 2019Jan Jongboom
We collect more sensor data than ever, but throw most of it away due to cost, bandwidth or power constraints. In this presentation we'll look at embedded machine learning, pushing intelligence directly to the sensor edge. Given during the CENSIS Tech Summit 2019 in Glasgow, Scotland.
Adding intelligence to your LoRaWAN devices - The Things Conference on tourJan Jongboom
Want to get started? Check the tutorial here: https://www.edgeimpulse.com/blog/adding-machine-learning-to-your-lorawan-device/
Talk about machine learning for IoT devices (TinyML), and everything that it entails. From signal processing to neural networks to classic ML algorithms. Presented in Reading, UK and Hyderabad, India during The Things Conference on Tour.
Machine learning on 1 square centimeter - Emerce Next 2019Jan Jongboom
Machine Learning is widely applied, but the models operate on digital data and run in big data centers. But there's more to the world. This is my presentation from Emerce Next 2019 about pushing ML to the smallest of devices.
Fundamentals of IoT - Data Science Africa 2019Jan Jongboom
As data scientists your job is to create order in the data chaos. But where does this data come from? Real-world data does not magically appear cleanly in your Matlab scripts. This is a talk about the fundamentals of IoT, and how to retrieve data from the real world using sensors and devices. Given during Data Science Africa 2019 in Addis Ababa.
LoRaWAN is great, but it requires so much hardware. As I live on a plane I want something better. Presentation about simulating LoRaWAN devices. Here's a video of the simulator: https://www.youtube.com/watch?v=C1S8knMlX7w
Firmware Updates over LoRaWAN - The Things Conference 2019Jan Jongboom
IoT deployments last for ten years, but that's a long time. Requirements change, vulnerabilities are found, and standards evolve. You'll need a firmware update solution.
Talk during The Things Conference 2019.
Faster Device Development - GSMA @ CES 2019Jan Jongboom
Presentation about interesting open source developments that can be used in conjunction with LTE Cat-M1 and NB-IoT. Presentation from the GSMA IoT workshop at CES 2019.
Introduction to Mbed - Etteplan seminar - August 2018Jan Jongboom
What is Arm Mbed, what is Arm Pelion, and how can it help me create IoT devices faster? Introductionary talk during the Etteplan seminars in Oulu and Espoo 21-22 August 2018 about LoRa in Mbed.
Machine Learning on 1 cm2 - Daho.am 2018Jan Jongboom
Putting machine learning on edge nodes adds local control, reduces privacy concerns, and doesn't require a fast internet connection. uTensor and CMSIS-NN bring ML to the cheapest of computers: microcontrollers. $2 cost and 1 cm2 in size they're found everywhere.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Italy Agriculture Equipment Market Outlook to 2027harveenkaur52
Agriculture and Animal Care
Ken Research has an expertise in Agriculture and Animal Care sector and offer vast collection of information related to all major aspects such as Agriculture equipment, Crop Protection, Seed, Agriculture Chemical, Fertilizers, Protected Cultivators, Palm Oil, Hybrid Seed, Animal Feed additives and many more.
Our continuous study and findings in agriculture sector provide better insights to companies dealing with related product and services, government and agriculture associations, researchers and students to well understand the present and expected scenario.
Our Animal care category provides solutions on Animal Healthcare and related products and services, including, animal feed additives, vaccination
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.