Tag Archives: sensor

Tired of queuing for the office toilet? Meet Occu-Pi

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/office-toilet-occu-pi/

This is the story of Occu-Pi, or how a magnet, a Raspberry Pi, and a barrel bolt saved an office team from queuing for the toilet.

Occu Pi Raspberry Pi toilet signal

The toil of toilet queuing

When Brian W. Wolter’s employer moved premises, the staff’s main concern as the dearth of toilets at the new office, and the increased queuing time this would lead to:

Our previous office had long been plagued by unreasonably long bathroom lines. At several high-demand periods throughout the day we’d be forced to wait three, four, five people deep while complaining bitterly to each other until our turn to use the facilities arrived. With even fewer bathrooms in our new office, concern about timely access was naturally high.

Faced with this problem, the in-house engineers decided to find a technological solution.

Occu-Pi

The main thing the engineers had to figure out was just how to determine the difference between a closed door and an occupied stall. Brian explains in his write-up:

There is one notable wrinkle: it’s not enough to know the door is closed, you need to know if the bathroom is actually in use — that is, locked from the inside. After considering and discarding a variety of ‘creative’ solutions (no thank you, motion sensors and facial recognition), we landed on a straightforward and reliable approach.

The team ended up using a magnet attached to the door’s barrel bolt to trigger a notification. Simply shutting the door doesn’t act as a trigger — the bolt needs to lock the door to set off a magnetic switch. That switch then triggers both LED notifications and updates to a dedicated Slack channel.

Occu-Pi Raspberry Pi toilet signal

For the technically-minded, Occu-Pi is a pretty straightforward build. And those wanting to learn more about it can find a full write-up in Brian’s Medium post.

We’ve seen a few different toilet notification projects over the years, for example this project from DIY Tryin’ using a similar trigger plus a website. What’s nice about Occu-Pi, however, is the simplicity of its design and the subtle use of Slack — Pi Tower’s favoured platform for office shenanigans.

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Build your own weather station with our new guide!

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/build-your-own-weather-station/

One of the most common enquiries I receive at Pi Towers is “How can I get my hands on a Raspberry Pi Oracle Weather Station?” Now the answer is: “Why not build your own version using our guide?”

Build Your Own weather station kit assembled

Tadaaaa! The BYO weather station fully assembled.

Our Oracle Weather Station

In 2016 we sent out nearly 1000 Raspberry Pi Oracle Weather Station kits to schools from around the world who had applied to be part of our weather station programme. In the original kit was a special HAT that allows the Pi to collect weather data with a set of sensors.

The original Raspberry Pi Oracle Weather Station HAT – Build Your Own Raspberry Pi weather station

The original Raspberry Pi Oracle Weather Station HAT

We designed the HAT to enable students to create their own weather stations and mount them at their schools. As part of the programme, we also provide an ever-growing range of supporting resources. We’ve seen Oracle Weather Stations in great locations with a huge differences in climate, and they’ve even recorded the effects of a solar eclipse.

Our new BYO weather station guide

We only had a single batch of HATs made, and unfortunately we’ve given nearly* all the Weather Station kits away. Not only are the kits really popular, we also receive lots of questions about how to add extra sensors or how to take more precise measurements of a particular weather phenomenon. So today, to satisfy your demand for a hackable weather station, we’re launching our Build your own weather station guide!

Build Your Own Raspberry Pi weather station

Fun with meteorological experiments!

Our guide suggests the use of many of the sensors from the Oracle Weather Station kit, so can build a station that’s as close as possible to the original. As you know, the Raspberry Pi is incredibly versatile, and we’ve made it easy to hack the design in case you want to use different sensors.

Many other tutorials for Pi-powered weather stations don’t explain how the various sensors work or how to store your data. Ours goes into more detail. It shows you how to put together a breadboard prototype, it describes how to write Python code to take readings in different ways, and it guides you through recording these readings in a database.

Build Your Own Raspberry Pi weather station on a breadboard

There’s also a section on how to make your station weatherproof. And in case you want to move past the breadboard stage, we also help you with that. The guide shows you how to solder together all the components, similar to the original Oracle Weather Station HAT.

Who should try this build

We think this is a great project to tackle at home, at a STEM club, Scout group, or CoderDojo, and we’re sure that many of you will be chomping at the bit to get started. Before you do, please note that we’ve designed the build to be as straight-forward as possible, but it’s still fairly advanced both in terms of electronics and programming. You should read through the whole guide before purchasing any components.

Build Your Own Raspberry Pi weather station – components

The sensors and components we’re suggesting balance cost, accuracy, and easy of use. Depending on what you want to use your station for, you may wish to use different components. Similarly, the final soldered design in the guide may not be the most elegant, but we think it is achievable for someone with modest soldering experience and basic equipment.

You can build a functioning weather station without soldering with our guide, but the build will be more durable if you do solder it. If you’ve never tried soldering before, that’s OK: we have a Getting started with soldering resource plus video tutorial that will walk you through how it works step by step.

Prototyping HAT for Raspberry Pi weather station sensors

For those of you who are more experienced makers, there are plenty of different ways to put the final build together. We always like to hear about alternative builds, so please post your designs in the Weather Station forum.

Our plans for the guide

Our next step is publishing supplementary guides for adding extra functionality to your weather station. We’d love to hear which enhancements you would most like to see! Our current ideas under development include adding a webcam, making a tweeting weather station, adding a light/UV meter, and incorporating a lightning sensor. Let us know which of these is your favourite, or suggest your own amazing ideas in the comments!

*We do have a very small number of kits reserved for interesting projects or locations: a particularly cool experiment, a novel idea for how the Oracle Weather Station could be used, or places with specific weather phenomena. If have such a project in mind, please send a brief outline to [email protected], and we’ll consider how we might be able to help you.

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Protecting coral reefs with Nemo-Pi, the underwater monitor

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/coral-reefs-nemo-pi/

The German charity Save Nemo works to protect coral reefs, and they are developing Nemo-Pi, an underwater “weather station” that monitors ocean conditions. Right now, you can vote for Save Nemo in the Google.org Impact Challenge.

Nemo-Pi — Save Nemo

Save Nemo

The organisation says there are two major threats to coral reefs: divers, and climate change. To make diving saver for reefs, Save Nemo installs buoy anchor points where diving tour boats can anchor without damaging corals in the process.

reef damaged by anchor
boat anchored at buoy

In addition, they provide dos and don’ts for how to behave on a reef dive.

The Nemo-Pi

To monitor the effects of climate change, and to help divers decide whether conditions are right at a reef while they’re still on shore, Save Nemo is also in the process of perfecting Nemo-Pi.

Nemo-Pi schematic — Nemo-Pi — Save Nemo

This Raspberry Pi-powered device is made up of a buoy, a solar panel, a GPS device, a Pi, and an array of sensors. Nemo-Pi measures water conditions such as current, visibility, temperature, carbon dioxide and nitrogen oxide concentrations, and pH. It also uploads its readings live to a public webserver.

Inside the Nemo-Pi device — Save Nemo
Inside the Nemo-Pi device — Save Nemo
Inside the Nemo-Pi device — Save Nemo

The Save Nemo team is currently doing long-term tests of Nemo-Pi off the coast of Thailand and Indonesia. They are also working on improving the device’s power consumption and durability, and testing prototypes with the Raspberry Pi Zero W.

web dashboard — Nemo-Pi — Save Nemo

The web dashboard showing live Nemo-Pi data

Long-term goals

Save Nemo aims to install a network of Nemo-Pis at shallow reefs (up to 60 metres deep) in South East Asia. Then diving tour companies can check the live data online and decide day-to-day whether tours are feasible. This will lower the impact of humans on reefs and help the local flora and fauna survive.

Coral reefs with fishes

A healthy coral reef

Nemo-Pi data may also be useful for groups lobbying for reef conservation, and for scientists and activists who want to shine a spotlight on the awful effects of climate change on sea life, such as coral bleaching caused by rising water temperatures.

Bleached coral

A bleached coral reef

Vote now for Save Nemo

If you want to help Save Nemo in their mission today, vote for them to win the Google.org Impact Challenge:

  1. Head to the voting web page
  2. Click “Abstimmen” in the footer of the page to vote
  3. Click “JA” in the footer to confirm

Voting is open until 6 June. You can also follow Save Nemo on Facebook or Twitter. We think this organisation is doing valuable work, and that their projects could be expanded to reefs across the globe. It’s fantastic to see the Raspberry Pi being used to help protect ocean life.

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Recording lost seconds with the Augenblick blink camera

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/augenblick-camera/

Warning: a GIF used in today’s blog contains flashing images.

Students at the University of Bremen, Germany, have built a wearable camera that records the seconds of vision lost when you blink. Augenblick uses a Raspberry Pi Zero and Camera Module alongside muscle sensors to record footage whenever you close your eyes, producing a rather disjointed film of the sights you miss out on.

Augenblick blink camera recording using a Raspberry Pi Zero

Blink and you’ll miss it

The average person blinks up to five times a minute, with each blink lasting 0.5 to 0.8 seconds. These half-seconds add up to about 30 minutes a day. What sights are we losing during these minutes? That is the question asked by students Manasse Pinsuwan and René Henrich when they set out to design Augenblick.

Blinking is a highly invasive mechanism for our eyesight. Every day we close our eyes thousands of times without noticing it. Our mind manages to never let us wonder what exactly happens in the moments that we miss.

Capturing lost moments

For Augenblick, the wearer sticks MyoWare Muscle Sensor pads to their face, and these detect the electrical impulses that trigger blinking.

Augenblick blink camera recording using a Raspberry Pi Zero

Two pads are applied over the orbicularis oculi muscle that forms a ring around the eye socket, while the third pad is attached to the cheek as a neutral point.

Biology fact: there are two muscles responsible for blinking. The orbicularis oculi muscle closes the eye, while the levator palpebrae superioris muscle opens it — and yes, they both sound like the names of Harry Potter spells.

The sensor is read 25 times a second. Whenever it detects that the orbicularis oculi is active, the Camera Module records video footage.

Augenblick blink recording using a Raspberry Pi Zero

Pressing a button on the side of the Augenblick glasses set the code running. An LED lights up whenever the camera is recording and also serves to confirm the correct placement of the sensor pads.

Augenblick blink camera recording using a Raspberry Pi Zero

The Pi Zero saves the footage so that it can be stitched together later to form a continuous, if disjointed, film.

Learn more about the Augenblick blink camera

You can find more information on the conception, design, and build process of Augenblick here in German, with a shorter explanation including lots of photos here in English.

And if you’re keen to recreate this project, our free project resource for a wearable Pi Zero time-lapse camera will come in handy as a starting point.

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Project Floofball and more: Pi pet stuff

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/project-floofball-pi-pet-stuff/

It’s a public holiday here today (yes, again). So, while we indulge in the traditional pastime of barbecuing stuff (ourselves, mainly), here’s a little trove of Pi projects that cater for our various furry friends.

Project Floofball

Nicole Horward created Project Floofball for her hamster, Harold. It’s an IoT hamster wheel that uses a Raspberry Pi and a magnetic door sensor to log how far Harold runs.

Project Floofball: an IoT hamster wheel

An IoT Hamsterwheel using a Raspberry Pi and a magnetic door sensor, to see how far my hamster runs.

You can follow Harold’s runs in real time on his ThingSpeak channel, and you’ll find photos of the build on imgur. Nicole’s Python code, as well as her template for the laser-cut enclosure that houses the wiring and LCD display, are available on the hamster wheel’s GitHub repo.

A live-streaming pet feeder

JaganK3 used to work long hours that meant he couldn’t be there to feed his dog on time. He found that he couldn’t buy an automated feeder in India without paying a lot to import one, so he made one himself. It uses a Raspberry Pi to control a motor that turns a dispensing valve in a hopper full of dry food, giving his dog a portion of food at set times.

A transparent cylindrical hopper of dry dog food, with a motor that can turn a dispensing valve at the lower end. The motor is connected to a Raspberry Pi in a plastic case. Hopper, motor, Pi, and wiring are all mounted on a board on the wall.

He also added a web cam for live video streaming, because he could. Find out more in JaganK3’s Instructable for his pet feeder.

Shark laser cat toy

Sam Storino, meanwhile, is using a Raspberry Pi to control a laser-pointer cat toy with a goshdarned SHARK (which is kind of what I’d expect from the guy who made the steampunk-looking cat feeder a few weeks ago). The idea is to keep his cats interested and active within the confines of a compact city apartment.

Raspberry Pi Automatic Cat Laser Pointer Toy

Post with 52 votes and 7004 views. Tagged with cat, shark, lasers, austin powers, raspberry pi; Shared by JeorgeLeatherly. Raspberry Pi Automatic Cat Laser Pointer Toy

If I were a cat, I would definitely be entirely happy with this. Find out more on Sam’s website.

And there’s more

Michel Parreno has written a series of articles to help you monitor and feed your pet with Raspberry Pi.

All of these makers are generous in acknowledging the tutorials and build logs that helped them with their projects. It’s lovely to see the Raspberry Pi and maker community working like this, and I bet their projects will inspire others too.

Now, if you’ll excuse me. I’m late for a barbecue.

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Acunetix v12 – More Comprehensive More Accurate & 2x Faster

Post Syndicated from Darknet original https://www.darknet.org.uk/2018/05/acunetix-v12-more-comprehensive-more-accurate-2x-faster/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

Acunetix v12 – More Comprehensive More Accurate & 2x Faster

Acunetix, the pioneer in automated web application security software, has announced the release of Acunetix v12. This new version provides support for JavaScript ES7 to better analyse sites which rely heavily on JavaScript such as SPAs. This coupled with a new AcuSensor for Java web applications, sets Acunetix ahead of the curve in its ability to comprehensively and accurately scan all types of websites.

With v12 also comes a brand new scanning engine, re-engineered and re-written from the ground up, making Acunetix the fastest scanning engine in the industry.

Read the rest of Acunetix v12 – More Comprehensive More Accurate & 2x Faster now! Only available at Darknet.

Naturebytes’ weatherproof Pi and camera case

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/naturebytes-weatherproof-pi-and-camera-case/

Naturebytes are making their weatherproof Wildlife Cam Case available as a standalone product for the first time, a welcome addition to the Raspberry Pi ecosystem that should take some of the hassle out of your outdoor builds.

A robin on a bird feeder in a garden with a Naturebytes Wildlife Cam mounted beside it

Weatherproofing digital making projects

People often use Raspberry Pis and Camera Modules for outdoor projects, but weatherproofing your set-up can be tricky. You need to keep water — and tiny creatures — out, but you might well need access for wires and cables, whether for power or sensors; if you’re using a camera, it’ll need something clear and cleanable in front of the lens. You can use sealant, but if you need to adjust anything that you’ve applied it to, you’ll have to remove it and redo it. While we’ve seen a few reasonable options available to buy, the choice has never been what you’d call extensive.

The Naturebytes case

For all these reasons, I was pleased to learn that Naturebytes, the wildlife camera people, are releasing their Wildlife Cam Case as a standalone product for the first time.

Naturebytes case open

The Wildlife Cam Case is ideal for nature camera projects, of course, but it’ll also be useful for anyone who wants to take their Pi outdoors. It has weatherproof lenses that are transparent to visible and IR light, for all your nature observation projects. Its opening is hinged to allow easy access to your hardware, and the case has waterproof access for cables. Inside, there’s a mount for fixing any model of Raspberry Pi and camera, as well as many other components. On top of all that, the case comes with a sturdy nylon strap to make it easy to attach it to a post or a tree.

Naturebytes case additional components

Order yours now!

At the moment, Naturebytes are producing a limited run of the cases. The first batch of 50 are due to be dispatched next week to arrive just in time for the Bank Holiday weekend in the UK, so get them while they’re hot. It’s the perfect thing for recording a timelapse of exactly how quickly the slugs obliterate your vegetable seedlings, and of lots more heartening things that must surely happen in gardens other than mine.

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AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

Mayank Sinha’s home security project

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/home-security/

Yesterday, I received an email from someone called Mayank Sinha, showing us the Raspberry Pi home security project he’s been working on. He got in touch particularly because, he writes, the Raspberry Pi community has given him “immense support” with his build, and he wanted to dedicate it to the commmunity as thanks.

Mayank’s project is named Asfaleia, a Greek word that means safety, certainty, or security against threats. It’s part of an honourable tradition dating all the way back to 2012: it’s a prototype housed in a polystyrene box, using breadboards and jumper leads and sticky tape. And it’s working! Take a look.

Asfaleia DIY Home Security System

An IOT based home security system. The link to the code: https://github.com/mayanksinha11/Asfaleia

Home security with Asfaleida

Asfaleia has a PIR (passive infrared) motion sensor, an IR break beam sensor, and a gas sensor. All are connected to a Raspberry Pi 3 Model B, the latter two via a NodeMCU board. Mayank currently has them set up in a box that’s divided into compartments to model different rooms in a house.

A shallow box divided into four labelled "rooms", all containing electronic components

All the best prototypes have sticky tape or rubber bands

If the IR sensors detect motion or a broken beam, the webcam takes a photo and emails it to the build’s owner, and the build also calls their phone (I like your ringtone, Mayank). If the gas sensor detects a leak, the system activates an exhaust fan via a small relay board, and again the owner receives a phone call. The build can also authenticate users via face and fingerprint recognition. The software that runs it all is written in Python, and you can see Mayank’s code on GitHub.

Of prototypes and works-in-progess

Reading Mayank’s email made me very happy yesterday. We know that thousands of people in our community give a great deal of time and effort to help others learn and make things, and it is always wonderful to see an example of how that support is helping someone turn their ideas into reality. It’s great, too, to see people sharing works-in-progress, as well as polished projects! After all, the average build is more likely to feature rubber bands and Tupperware boxes than meticulously designed laser-cut parts or expert joinery. Mayank’s YouTube channel shows earlier work on this and another Pi project, and I hope he’ll continue to document his builds.

So here’s to Raspberry Pi projects big, small, beginner, professional, endlessly prototyped, unashamedly bodged, unfinished or fully working, shonky or shiny. Please keep sharing them all!

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[$] An introduction to MQTT

Post Syndicated from corbet original https://lwn.net/Articles/753705/rss

I was sure that somewhere there must be
physically-lightweight sensors with simple power, simple networking, and
a lightweight protocol that allowed them to squirt their data down the
network with a minimum of overhead. So my interest was piqued when Jan-Piet Mens spoke at FLOSS
UK’s Spring
Conference
on “Small Things for Monitoring”. Once he started passing
working demonstration systems around the room without interrupting the
demonstration, it was clear that MQTT was what I’d been looking for.

This is a really lovely Raspberry Pi tricorder

Post Syndicated from Helen Lynn original https://www.raspberrypi.org/blog/raspberry-pi-tricorder-prop/

At the moment I’m spending my evenings watching all of Star Trek in order. Yes, I have watched it before (but with some really big gaps). Yes, including the animated series (I’m up to The Terratin Incident). So I’m gratified to find this beautiful The Original Series–style tricorder build.

Star Trek Tricorder with Working Display!

At this year’s Replica Prop Forum showcase, we meet up once again wtih Brian Mix, who brought his new Star Trek TOS Tricorder. This beautiful replica captures the weight and finish of the filming hand prop, and Brian has taken it one step further with some modern-day electronics!

A what now?

If you don’t know what a tricorder is, which I guess is faintly possible, the easiest way I can explain is to steal words that Liz wrote when Recantha made one back in 2013. It’s “a made-up thing used by the crew of the Enterprise to measure stuff, store data, and scout ahead remotely when exploring strange new worlds, seeking out new life and new civilisations, and all that jazz.”

A brief history of Picorders

We’ve seen other Raspberry Pi–based realisations of this iconic device. Recantha’s LEGO-cased tricorder delivered some authentic functionality, including temperature sensors, an ultrasonic distance sensor, a photosensor, and a magnetometer. Michael Hahn’s tricorder for element14’s Sci-Fi Your Pi competition in 2015 packed some similar functions, along with Original Series audio effects, into a neat (albeit non-canon) enclosure.

Brian Mix’s Original Series tricorder

Brian Mix’s tricorder, seen in the video above from Tested at this year’s Replica Prop Forum showcase, is based on a high-quality kit into which, he discovered, a Raspberry Pi just fits. He explains that the kit is the work of the late Steve Horch, a special effects professional who provided props for later Star Trek series, including the classic Deep Space Nine episode Trials and Tribble-ations.

A still from an episode of Star Trek: Deep Space Nine: Jadzia Dax, holding an Original Series-sylte tricorder, speaks with Benjamin Sisko

Dax, equipped for time travel

This episode’s plot required sets and props — including tricorders — replicating the USS Enterprise of The Original Series, and Steve Horch provided many of these. Thus, a tricorder kit from him is about as close to authentic as you can possibly find unless you can get your hands on a screen-used prop. The Pi allows Brian to drive a real display and a speaker: “Being the geek that I am,” he explains, “I set it up to run every single Original Series Star Trek episode.”

Even more wonderful hypothetical tricorders that I would like someone to make

This tricorder is beautiful, and it makes me think how amazing it would be to squeeze in some of the sensor functionality of the devices depicted in the show. Space in the case is tight, but it looks like there might be a little bit of depth to spare — enough for an IMU, maybe, or a temperature sensor. I’m certain the future will bring more Pi tricorder builds, and I, for one, can’t wait. Please tell us in the comments if you’re planning something along these lines, and, well, I suppose some other sci-fi franchises have decent Pi project potential too, so we could probably stand to hear about those.

If you’re commenting, no spoilers please past The Animated Series S1 E11. Thanks.

The post This is a really lovely Raspberry Pi tricorder appeared first on Raspberry Pi.

The Helium Factor and Hard Drive Failure Rates

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/helium-filled-hard-drive-failure-rates/

Seagate Enterprise Capacity 3.5 Helium HDD

In November 2013, the first commercially available helium-filled hard drive was introduced by HGST, a Western Digital subsidiary. The 6 TB drive was not only unique in being helium-filled, it was for the moment, the highest capacity hard drive available. Fast forward a little over 4 years later and 12 TB helium-filled drives are readily available, 14 TB drives can be found, and 16 TB helium-filled drives are arriving soon.

Backblaze has been purchasing and deploying helium-filled hard drives over the past year and we thought it was time to start looking at their failure rates compared to traditional air-filled drives. This post will provide an overview, then we’ll continue the comparison on a regular basis over the coming months.

The Promise and Challenge of Helium Filled Drives

We all know that helium is lighter than air — that’s why helium-filled balloons float. Inside of an air-filled hard drive there are rapidly spinning disk platters that rotate at a given speed, 7200 rpm for example. The air inside adds an appreciable amount of drag on the platters that in turn requires an appreciable amount of additional energy to spin the platters. Replacing the air inside of a hard drive with helium reduces the amount of drag, thereby reducing the amount of energy needed to spin the platters, typically by 20%.

We also know that after a few days, a helium-filled balloon sinks to the ground. This was one of the key challenges in using helium inside of a hard drive: helium escapes from most containers, even if they are well sealed. It took years for hard drive manufacturers to create containers that could contain helium while still functioning as a hard drive. This container innovation allows helium-filled drives to function at spec over the course of their lifetime.

Checking for Leaks

Three years ago, we identified SMART 22 as the attribute assigned to recording the status of helium inside of a hard drive. We have both HGST and Seagate helium-filled hard drives, but only the HGST drives currently report the SMART 22 attribute. It appears the normalized and raw values for SMART 22 currently report the same value, which starts at 100 and goes down.

To date only one HGST drive has reported a value of less than 100, with multiple readings between 94 and 99. That drive continues to perform fine, with no other errors or any correlating changes in temperature, so we are not sure whether the change in value is trying to tell us something or if it is just a wonky sensor.

Helium versus Air-Filled Hard Drives

There are several different ways to compare these two types of drives. Below we decided to use just our 8, 10, and 12 TB drives in the comparison. We did this since we have helium-filled drives in those sizes. We left out of the comparison all of the drives that are 6 TB and smaller as none of the drive models we use are helium-filled. We are open to trying different comparisons. This just seemed to be the best place to start.

Lifetime Hard Drive Failure Rates: Helium vs. Air-Filled Hard Drives table

The most obvious observation is that there seems to be little difference in the Annualized Failure Rate (AFR) based on whether they contain helium or air. One conclusion, given this evidence, is that helium doesn’t affect the AFR of hard drives versus air-filled drives. My prediction is that the helium drives will eventually prove to have a lower AFR. Why? Drive Days.

Let’s go back in time to Q1 2017 when the air-filled drives listed in the table above had a similar number of Drive Days to the current number of Drive Days for the helium drives. We find that the failure rate for the air-filled drives at the time (Q1 2017) was 1.61%. In other words, when the drives were in use a similar number of hours, the helium drives had a failure rate of 1.06% while the failure rate of the air-filled drives was 1.61%.

Helium or Air?

My hypothesis is that after normalizing the data so that the helium and air-filled drives have the same (or similar) usage (Drive Days), the helium-filled drives we use will continue to have a lower Annualized Failure Rate versus the air-filled drives we use. I expect this trend to continue for the next year at least. What side do you come down on? Will the Annualized Failure Rate for helium-filled drives be better than air-filled drives or vice-versa? Or do you think the two technologies will be eventually produce the same AFR over time? Pick a side and we’ll document the results over the next year and see where the data takes us.

The post The Helium Factor and Hard Drive Failure Rates appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Converting a Kodak Box Brownie into a digital camera

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/kodak-brownie-camera/

In this article from The MagPi issue 69, David Crookes explains how Daniel Berrangé took an old Kodak Brownie from the 1950s and turned it into a quirky digital camera. Get your copy of The MagPi magazine in stores now, or download it as a free PDF here.

Daniel Berrangé Kodak Brownie Raspberry Pi Camera

The Kodak Box Brownie

When Kodak unveiled its Box Brownie in 1900, it did so with the slogan ‘You press the button, we do the rest.’ The words referred to the ease-of-use of what was the world’s first mass-produced camera. But it could equally apply to Daniel Berrangé’s philosophy when modifying it for the 21st century. “I wanted to use the Box Brownie’s shutter button to trigger image capture, and make it simple to use,” he tells us.

Daniel Berrangé Kodak Brownie Raspberry Pi Camera

Daniel’s project grew from a previous effort in which he placed a pinhole webcam inside a ladies’ powder compact case. “The Box Brownie project is essentially a repeat of that design but with a normal lens instead of a pinhole, a real camera case, and improved software to enable a shutter button. Ideally, it would look unchanged from when it was shooting film.”

Webcam woes

At first, Daniel looked for a cheap webcam, intending to spend no more than the price of a Pi Zero. This didn’t work out too well. “The low-light performance of the webcam was not sufficient to make a pinhole camera so I just decided to make a ‘normal’ digital camera instead,” he reveals.
To that end, he began removing some internal components from the Box Brownie. “With the original lens removed, the task was to position the webcam’s electronic light sensor (the CCD) and lens as close to the front of the camera as possible,” Daniel explains. “In the end, the CCD was about 15 mm away from the front aperture of the camera, giving a field of view that was approximately the same as the unmodified camera would achieve.”

Daniel Berrangé Kodak Brownie Raspberry Pi Camera
Daniel Berrangé Kodak Brownie Raspberry Pi Camera
Daniel Berrangé Kodak Brownie Raspberry Pi Camera

It was then time for him to insert the Raspberry Pi, upon which was a custom ‘init’ binary that loads a couple of kernel modules to run the webcam, mount the microSD file system, and launch the application binary. Here, Daniel found he was in luck. “I’d noticed that the size of a 620 film spool (63 mm) was effectively the same as the width of a Raspberry Pi Zero (65 mm), so it could be held in place between the film spool grips,” he recalls. “It was almost as if it was designed with this in mind.”

Shutter success

In order to operate the camera, Daniel had to work on the shutter button. “The Box Brownie’s shutter button is entirely mechanical, driven by a handful of levers and springs,” Daniel explains. “First, the Pi Zero needs to know when the shutter button is pressed and second, the physical shutter has to be open while the webcam is capturing the image. Rather than try to synchronise image capture with the fraction of a second that the physical shutter is open, a bit of electrical tape was used on the shutter mechanism to keep it permanently open.”

Daniel Berrangé Kodak Brownie Raspberry Pi Camera

Daniel made use of the Pi Zero’s GPIO pins to detect the pressing of the shutter button. It determines if each pin is at 0 or 5 volts. “My thought was that I could set a GPIO pin high to 5 V, and then use the action of the shutter button to short it to ground, and detect this change in level from software.”

This initially involved using a pair of bare wires and some conductive paint, although the paint was later replaced by a piece of tinfoil. But with the button pressed, the GPIO pin level goes to zero and the device constantly captures still images until the button is released. All that’s left to do is smile and take the perfect snap.

The post Converting a Kodak Box Brownie into a digital camera appeared first on Raspberry Pi.

NIST Issues Call for "Lightweight Cryptography" Algorithms

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/nist_issues_cal.html

This is interesting:

Creating these defenses is the goal of NIST’s lightweight cryptography initiative, which aims to develop cryptographic algorithm standards that can work within the confines of a simple electronic device. Many of the sensors, actuators and other micromachines that will function as eyes, ears and hands in IoT networks will work on scant electrical power and use circuitry far more limited than the chips found in even the simplest cell phone. Similar small electronics exist in the keyless entry fobs to newer-model cars and the Radio Frequency Identification (RFID) tags used to locate boxes in vast warehouses.

All of these gadgets are inexpensive to make and will fit nearly anywhere, but common encryption methods may demand more electronic resources than they possess.

The NSA’s SIMON and SPECK would certainly qualify.

10 visualizations to try in Amazon QuickSight with sample data

Post Syndicated from Karthik Kumar Odapally original https://aws.amazon.com/blogs/big-data/10-visualizations-to-try-in-amazon-quicksight-with-sample-data/

If you’re not already familiar with building visualizations for quick access to business insights using Amazon QuickSight, consider this your introduction. In this post, we’ll walk through some common scenarios with sample datasets to provide an overview of how you can connect yuor data, perform advanced analysis and access the results from any web browser or mobile device.

The following visualizations are built from the public datasets available in the links below. Before we jump into that, let’s take a look at the supported data sources, file formats and a typical QuickSight workflow to build any visualization.

Which data sources does Amazon QuickSight support?

At the time of publication, you can use the following data methods:

  • Connect to AWS data sources, including:
    • Amazon RDS
    • Amazon Aurora
    • Amazon Redshift
    • Amazon Athena
    • Amazon S3
  • Upload Excel spreadsheets or flat files (CSV, TSV, CLF, and ELF)
  • Connect to on-premises databases like Teradata, SQL Server, MySQL, and PostgreSQL
  • Import data from SaaS applications like Salesforce and Snowflake
  • Use big data processing engines like Spark and Presto

This list is constantly growing. For more information, see Supported Data Sources.

Answers in instants

SPICE is the Amazon QuickSight super-fast, parallel, in-memory calculation engine, designed specifically for ad hoc data visualization. SPICE stores your data in a system architected for high availability, where it is saved until you choose to delete it. Improve the performance of database datasets by importing the data into SPICE instead of using a direct database query. To calculate how much SPICE capacity your dataset needs, see Managing SPICE Capacity.

Typical Amazon QuickSight workflow

When you create an analysis, the typical workflow is as follows:

  1. Connect to a data source, and then create a new dataset or choose an existing dataset.
  2. (Optional) If you created a new dataset, prepare the data (for example, by changing field names or data types).
  3. Create a new analysis.
  4. Add a visual to the analysis by choosing the fields to visualize. Choose a specific visual type, or use AutoGraph and let Amazon QuickSight choose the most appropriate visual type, based on the number and data types of the fields that you select.
  5. (Optional) Modify the visual to meet your requirements (for example, by adding a filter or changing the visual type).
  6. (Optional) Add more visuals to the analysis.
  7. (Optional) Add scenes to the default story to provide a narrative about some aspect of the analysis data.
  8. (Optional) Publish the analysis as a dashboard to share insights with other users.

The following graphic illustrates a typical Amazon QuickSight workflow.

Visualizations created in Amazon QuickSight with sample datasets

Visualizations for a data analyst

Source:  https://data.worldbank.org/

Download and Resources:  https://datacatalog.worldbank.org/dataset/world-development-indicators

Data catalog:  The World Bank invests into multiple development projects at the national, regional, and global levels. It’s a great source of information for data analysts.

The following graph shows the percentage of the population that has access to electricity (rural and urban) during 2000 in Asia, Africa, the Middle East, and Latin America.

The following graph shows the share of healthcare costs that are paid out-of-pocket (private vs. public). Also, you can maneuver over the graph to get detailed statistics at a glance.

Visualizations for a trading analyst

Source:  Deutsche Börse Public Dataset (DBG PDS)

Download and resources:  https://aws.amazon.com/public-datasets/deutsche-boerse-pds/

Data catalog:  The DBG PDS project makes real-time data derived from Deutsche Börse’s trading market systems available to the public for free. This is the first time that such detailed financial market data has been shared freely and continually from the source provider.

The following graph shows the market trend of max trade volume for different EU banks. It builds on the data available on XETRA engines, which is made up of a variety of equities, funds, and derivative securities. This graph can be scrolled to visualize trade for a period of an hour or more.

The following graph shows the common stock beating the rest of the maximum trade volume over a period of time, grouped by security type.

Visualizations for a data scientist

Source:  https://catalog.data.gov/

Download and resources:  https://catalog.data.gov/dataset/road-weather-information-stations-788f8

Data catalog:  Data derived from different sensor stations placed on the city bridges and surface streets are a core information source. The road weather information station has a temperature sensor that measures the temperature of the street surface. It also has a sensor that measures the ambient air temperature at the station each second.

The following graph shows the present max air temperature in Seattle from different RWI station sensors.

The following graph shows the minimum temperature of the road surface at different times, which helps predicts road conditions at a particular time of the year.

Visualizations for a data engineer

Source:  https://www.kaggle.com/

Download and resources:  https://www.kaggle.com/datasnaek/youtube-new/data

Data catalog:  Kaggle has come up with a platform where people can donate open datasets. Data engineers and other community members can have open access to these datasets and can contribute to the open data movement. They have more than 350 datasets in total, with more than 200 as featured datasets. It has a few interesting datasets on the platform that are not present at other places, and it’s a platform to connect with other data enthusiasts.

The following graph shows the trending YouTube videos and presents the max likes for the top 20 channels. This is one of the most popular datasets for data engineers.

The following graph shows the YouTube daily statistics for the max views of video titles published during a specific time period.

Visualizations for a business user

Source:  New York Taxi Data

Download and resources:  https://data.cityofnewyork.us/Transportation/2016-Green-Taxi-Trip-Data/hvrh-b6nb

Data catalog: NYC Open data hosts some very popular open data sets for all New Yorkers. This platform allows you to get involved in dive deep into the data set to pull some useful visualizations. 2016 Green taxi trip dataset includes trip records from all trips completed in green taxis in NYC in 2016. Records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.

The following graph presents maximum fare amount grouped by the passenger count during a period of time during a day. This can be further expanded to follow through different day of the month based on the business need.

The following graph shows the NewYork taxi data from January 2016, showing the dip in the number of taxis ridden on January 23, 2016 across all types of taxis.

A quick search for that date and location shows you the following news report:

Summary

Using Amazon QuickSight, you can see patterns across a time-series data by building visualizations, performing ad hoc analysis, and quickly generating insights. We hope you’ll give it a try today!

 


Additional Reading

If you found this post useful, be sure to check out Amazon QuickSight Adds Support for Combo Charts and Row-Level Security and Visualize AWS Cloudtrail Logs Using AWS Glue and Amazon QuickSight.


Karthik Odapally is a Sr. Solutions Architect in AWS. His passion is to build cost effective and highly scalable solutions on the cloud. In his spare time, he bakes cookies and cupcakes for family and friends here in the PNW. He loves vintage racing cars.

 

 

 

Pranabesh Mandal is a Solutions Architect in AWS. He has over a decade of IT experience. He is passionate about cloud technology and focuses on Analytics. In his spare time, he likes to hike and explore the beautiful nature and wild life of most divine national parks around the United States alongside his wife.

 

 

 

 

Colour sensing with a Raspberry Pi

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/colour-sensing-raspberry-pi/

In their latest video and tutorial, Electronic Hub shows you how to detect colour using a Raspberry Pi and a TCS3200 colour sensor.

Raspberry Pi Color Sensor (TCS3200) Interface | Color Detector

A simple Raspberry Pi based project using TCS3200 Color Sensor. The project demonstrates how to interface a Color Sensor (like TCS3200) with Raspberry Pi and implement a simple Color Detector using Raspberry Pi.

What is a TCS3200 colour sensor?

Colour sensors sense reflected light from nearby objects. The bright light of the TCS3200’s on-board white LEDs hits an object’s surface and is reflected back. The sensor has an 8×8 array of photodiodes, which are covered by either a red, blue, green, or clear filter. The type of filter determines what colour a diode can detect. Then the overall colour of an object is determined by how much light of each colour it reflects. (For example, a red object reflects mostly red light.)

Colour sensing with the TCS3200 Color Sensor and a Raspberry Pi

As Electronics Hub explains:

TCS3200 is one of the easily available colour sensors that students and hobbyists can work on. It is basically a light-to-frequency converter, i.e. based on colour and intensity of the light falling on it, the frequency of its output signal varies.

I’ll save you a physics lesson here, but you can find a detailed explanation of colour sensing and the TCS3200 on the Electronics Hub blog.

Raspberry Pi colour sensor

The TCS3200 colour sensor is connected to several of the onboard General Purpose Input Output (GPIO) pins on the Raspberry Pi.

Colour sensing with the TCS3200 Color Sensor and a Raspberry Pi

These connections allow the Raspberry Pi 3 to run one of two Python scripts that Electronics Hub has written for the project. The first displays the RAW RGB values read by the sensor. The second detects the primary colours red, green, and blue, and it can be expanded for more colours with the help of the first script.

Colour sensing with the TCS3200 Color Sensor and a Raspberry Pi

Electronic Hub’s complete build uses a breadboard for simply prototyping

Use it in your projects

This colour sensing setup is a simple means of adding a new dimension to your builds. Why not build a candy-sorting robot that organises your favourite sweets by colour? Or add colour sensing to your line-following buggy to allow for multiple path options!

If your Raspberry Pi project uses colour sensing, we’d love to see it, so be sure to share it in the comments!

The post Colour sensing with a Raspberry Pi appeared first on Raspberry Pi.

New – Machine Learning Inference at the Edge Using AWS Greengrass

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-machine-learning-inference-at-the-edge-using-aws-greengrass/

What happens when you combine the Internet of Things, Machine Learning, and Edge Computing? Before I tell you, let’s review each one and discuss what AWS has to offer.

Internet of Things (IoT) – Devices that connect the physical world and the digital one. The devices, often equipped with one or more types of sensors, can be found in factories, vehicles, mines, fields, homes, and so forth. Important AWS services include AWS IoT Core, AWS IoT Analytics, AWS IoT Device Management, and Amazon FreeRTOS, along with others that you can find on the AWS IoT page.

Machine Learning (ML) – Systems that can be trained using an at-scale dataset and statistical algorithms, and used to make inferences from fresh data. At Amazon we use machine learning to drive the recommendations that you see when you shop, to optimize the paths in our fulfillment centers, fly drones, and much more. We support leading open source machine learning frameworks such as TensorFlow and MXNet, and make ML accessible and easy to use through Amazon SageMaker. We also provide Amazon Rekognition for images and for video, Amazon Lex for chatbots, and a wide array of language services for text analysis, translation, speech recognition, and text to speech.

Edge Computing – The power to have compute resources and decision-making capabilities in disparate locations, often with intermittent or no connectivity to the cloud. AWS Greengrass builds on AWS IoT, giving you the ability to run Lambda functions and keep device state in sync even when not connected to the Internet.

ML Inference at the Edge
Today I would like to toss all three of these important new technologies into a blender! You can now perform Machine Learning inference at the edge using AWS Greengrass. This allows you to use the power of the AWS cloud (including fast, powerful instances equipped with GPUs) to build, train, and test your ML models before deploying them to small, low-powered, intermittently-connected IoT devices running in those factories, vehicles, mines, fields, and homes that I mentioned.

Here are a few of the many ways that you can put Greengrass ML Inference to use:

Precision Farming – With an ever-growing world population and unpredictable weather that can affect crop yields, the opportunity to use technology to increase yields is immense. Intelligent devices that are literally in the field can process images of soil, plants, pests, and crops, taking local corrective action and sending status reports to the cloud.

Physical Security – Smart devices (including the AWS DeepLens) can process images and scenes locally, looking for objects, watching for changes, and even detecting faces. When something of interest or concern arises, the device can pass the image or the video to the cloud and use Amazon Rekognition to take a closer look.

Industrial Maintenance – Smart, local monitoring can increase operational efficiency and reduce unplanned downtime. The monitors can run inference operations on power consumption, noise levels, and vibration to flag anomalies, predict failures, detect faulty equipment.

Greengrass ML Inference Overview
There are several different aspects to this new AWS feature. Let’s take a look at each one:

Machine Learning ModelsPrecompiled TensorFlow and MXNet libraries, optimized for production use on the NVIDIA Jetson TX2 and Intel Atom devices, and development use on 32-bit Raspberry Pi devices. The optimized libraries can take advantage of GPU and FPGA hardware accelerators at the edge in order to provide fast, local inferences.

Model Building and Training – The ability to use Amazon SageMaker and other cloud-based ML tools to build, train, and test your models before deploying them to your IoT devices. To learn more about SageMaker, read Amazon SageMaker – Accelerated Machine Learning.

Model Deployment – SageMaker models can (if you give them the proper IAM permissions) be referenced directly from your Greengrass groups. You can also make use of models stored in S3 buckets. You can add a new machine learning resource to a group with a couple of clicks:

These new features are available now and you can start using them today! To learn more read Perform Machine Learning Inference.

Jeff;