Tag Archives: sensors

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.

The post Naturebytes’ weatherproof Pi and camera case appeared first on Raspberry Pi.

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!

The post Mayank Sinha’s home security project appeared first on Raspberry Pi.

[$] 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.

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;

 

Simulate sand with Adafruit’s newest project

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/simulate-sand-with-adafruits-newest-project/

The Ruiz brothers at Adafruit have used Phillip Burgess’s PixieDust code to turn a 64×64 LED Matrix and a Raspberry Pi Zero into an awesome sand toy that refuses to defy the laws of gravity. Here’s how to make your own.

BIG LED Sand Toy – Raspberry Pi RGB LED Matrix

Simulated LED Sand Physics! These LEDs interact with motion and looks like they’re affect by gravity. An Adafruit LED matrix displays the LEDs as little grains of sand which are driven by sampling an accelerometer with Raspberry Pi Zero!

Obey gravity

As the latest addition to their online learning system, Adafruit have produced the BIG LED Sand Toy, or as I like to call it, Have you seen this awesome thing Adafuit have made?

Adafruit Sand Toy Raspberry Pi

The build uses a Raspberry Pi Zero, a 64×64 LED matrix, the Adafruit RGB Matrix Bonnet, 3D-printed parts, and a few smaller peripherals. Find the entire tutorial, including downloadable STL files, on their website.

How does it work?

Alongside the aforementioned ingredients, the project utilises the Adafruit LIS3DH Triple-Axis Accelerometer. This sensor is packed with features, and it allows the Raspberry Pi to control the virtual sand depending on how the toy is moved.

Adafruit Sand Toy Raspberry Pi

The Ruiz brothers inserted an SD card loaded with Raspbian Lite into the Raspberry Pi Zero, installed the LED Matrix driver, cloned the Adafruit_PixieDust library, and then just executed the code. They created some preset modes, but once you’re comfortable with the project code, you’ll be able to add your own take on the project.

Accelerometers and Raspberry Pi

This isn’t the first time a Raspberry Pi has met an accelerometer: the two Raspberry Pis aboard the International Space Station for the Astro Pi mission both have accelerometers thanks to their Sense HATs.

Comprised of a bundle of sensors, an LED matrix, and a five-point joystick, the Sense HAT is a great tool for exploring your surroundings with the Raspberry Pi, as well as for using your surroundings to control the Pi. You can find a whole variety of Sense HAT–based projects and tutorials on our website.

Raspberry Pi Sense HAT Slug free resource

And if you’d like to try out the Sense HAT, including its onboard accelerometer, without purchasing one, head over to our online emulator, or use the emulator preinstalled on Raspbian.

The post Simulate sand with Adafruit’s newest project appeared first on Raspberry Pi.

This IoT Pet Monitor barks back

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/iot-pet-monitor/

Jennifer Fox, founder of FoxBot Industries, uses a Raspberry Pi pet monitor to check the sound levels of her home while she is out, allowing her to keep track of when her dog Marley gets noisy or agitated, and to interact with the gorgeous furball accordingly.

Bark Back Project Demo

A quick overview and demo of the Bark Back, a project to monitor and interact with Check out the full tutorial here: https://learn.sparkfun.com/tutorials/bark-back-interactive-pet-monitor For any licensing requests please contact [email protected]

Marley, bark!

Using a Raspberry Pi 3, speakers, SparkFun’s MEMS microphone breakout board, and an analogue-to-digital converter (ADC), the IoT Pet Monitor is fairly easy to recreate, all thanks to Jennifer’s full tutorial on the FoxBot website.

Building the pet monitor

In a nutshell, once the Raspberry Pi and the appropriate bits and pieces are set up, you’ll need to sign up at CloudMQTT — it’s free if you select the Cute Cat account. CloudMQTT will create an invisible bridge between your home and wherever you are that isn’t home, so that you can check in on your pet monitor.

Screenshot CloudMQTT account set-up — IoT Pet Monitor Bark Back Raspberry Pi

Image c/o FoxBot Industries

Within the project code, you’ll be able to calculate the peak-to-peak amplitude of sound the microphone picks up. Then you can decide how noisy is too noisy when it comes to the occasional whine and bark of your beloved pup.

MEMS microphone breakout board — IoT Pet Monitor Bark Back Raspberry Pi

The MEMS microphone breakout board collects sound data and relays it back to the Raspberry Pi via the ADC.
Image c/o FoxBot Industries

Next you can import sounds to a preset song list that will be played back when the volume rises above your predefined threshold. As Jennifer states in the tutorial, the sounds can easily be recorded via apps such as Garageband, or even on your mobile phone.

Using the pet monitor

Whenever the Bark Back IoT Pet Monitor is triggered to play back audio, this information is fed to the CloudMQTT service, allowing you to see if anything is going on back home.

A sitting dog with a doll in its mouth — IoT Pet Monitor Bark Back Raspberry Pi

*incoherent coos of affection from Alex*
Image c/o FoxBot Industries

And as Jennifer recommends, a update of the project could include a camera or sensors to feed back more information about your home environment.

If you’ve created something similar, be sure to let us know in the comments. And if you haven’t, but you’re now planning to build your own IoT pet monitor, be sure to let us know in the comments. And if you don’t have a pet but just want to say hi…that’s right, be sure to let us know in the comments.

The post This IoT Pet Monitor barks back appeared first on Raspberry Pi.

Jumping Air Gaps

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/02/jumping_air_gap_2.html

Nice profile of Mordechai Guri, who researches a variety of clever ways to steal data over air-gapped computers.

Guri and his fellow Ben-Gurion researchers have shown, for instance, that it's possible to trick a fully offline computer into leaking data to another nearby device via the noise its internal fan generates, by changing air temperatures in patterns that the receiving computer can detect with thermal sensors, or even by blinking out a stream of information from a computer hard drive LED to the camera on a quadcopter drone hovering outside a nearby window. In new research published today, the Ben-Gurion team has even shown that they can pull data off a computer protected by not only an air gap, but also a Faraday cage designed to block all radio signals.

Here’s a page with all the research results.

BoingBoing post.

When tiny robot COZMO met our tiny Raspberry Pi

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

Hack your COZMO for ultimate control, using a Raspberry Pi and this tutorial from Instructables user Marcelo ‘mjrovai’ Rovai.

Cozmo – RPi 4

Full integration The complete tutorial can be found here: https://www.instructables.com/id/When-COZMO-the-Robot-Meets-the-Raspberry-Pi/

COZMO

COZMO is a Python-programmable robot from ANKI that boasts a variety of on-board sensors and a camera, and that can be controlled via an app or via code. To get an idea of how COZMO works, check out this rather excitable video from the wonderful Mayim Bialik.

The COZMO SDK

COZMO’s creators, ANKI, provide a Software Development Kit (SDK) so that users can get the most out of their COZMO. This added functionality is a great opportunity for budding coders to dive into hacking their toys, without the risk of warranty voiding/upsetting parents/not being sure how to put a toy back together again.

By the way, I should point out that this is in no way a sponsored blog post. I just think COZMO is ridiculously cute…because tiny robots are adorable, no matter their intentions.

Raspberry Pi Doctor Who Cybermat

Marcelo Rovai + Raspberry Pi + COZMO

For his Instructables tutorial, Marcelo connected an Android device running the COZMO app to his Raspberry Pi 3 via USB. Once USB debugging had been enabled on his device, he installed the Android Debug Bridge (ADB) to the Raspberry Pi. Then his Pi was able to recognise the connected Android device, and from there, Marcelo moved on to installing the SDK, including support for COZMO’s camera.

COZMO Raspberry Pi

The SDK comes with pre-installed examples, allowing users to try out the possibilities of the kit, such as controlling what COZMO says by editing a Python script.

Cozmo and RPi

Hello World The complete tutorial can be found here: https://www.instructables.com/id/When-COZMO-the-Robot-Meets-the-Raspberry-Pi/

Do more with COZMO

Marcelo’s tutorial offers more example code for users of the COZMO SDK, along with the code to run the LED button game featured in the video above, and tips on utilising the SDK to take full advantage of COZMO. Check it out here on Instructables, and visit his website for even more projects.

The post When tiny robot COZMO met our tiny Raspberry Pi appeared first on Raspberry Pi.

After Section 702 Reauthorization

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/after_section_7.html

For over a decade, civil libertarians have been fighting government mass surveillance of innocent Americans over the Internet. We’ve just lost an important battle. On January 18, President Trump signed the renewal of Section 702, domestic mass surveillance became effectively a permanent part of US law.

Section 702 was initially passed in 2008, as an amendment to the Foreign Intelligence Surveillance Act of 1978. As the title of that law says, it was billed as a way for the NSA to spy on non-Americans located outside the United States. It was supposed to be an efficiency and cost-saving measure: the NSA was already permitted to tap communications cables located outside the country, and it was already permitted to tap communications cables from one foreign country to another that passed through the United States. Section 702 allowed it to tap those cables from inside the United States, where it was easier. It also allowed the NSA to request surveillance data directly from Internet companies under a program called PRISM.

The problem is that this authority also gave the NSA the ability to collect foreign communications and data in a way that inherently and intentionally also swept up Americans’ communications as well, without a warrant. Other law enforcement agencies are allowed to ask the NSA to search those communications, give their contents to the FBI and other agencies and then lie about their origins in court.

In 1978, after Watergate had revealed the Nixon administration’s abuses of power, we erected a wall between intelligence and law enforcement that prevented precisely this kind of sharing of surveillance data under any authority less restrictive than the Fourth Amendment. Weakening that wall is incredibly dangerous, and the NSA should never have been given this authority in the first place.

Arguably, it never was. The NSA had been doing this type of surveillance illegally for years, something that was first made public in 2006. Section 702 was secretly used as a way to paper over that illegal collection, but nothing in the text of the later amendment gives the NSA this authority. We didn’t know that the NSA was using this law as the statutory basis for this surveillance until Edward Snowden showed us in 2013.

Civil libertarians have been battling this law in both Congress and the courts ever since it was proposed, and the NSA’s domestic surveillance activities even longer. What this most recent vote tells me is that we’ve lost that fight.

Section 702 was passed under George W. Bush in 2008, reauthorized under Barack Obama in 2012, and now reauthorized again under Trump. In all three cases, congressional support was bipartisan. It has survived multiple lawsuits by the Electronic Frontier Foundation, the ACLU, and others. It has survived the revelations by Snowden that it was being used far more extensively than Congress or the public believed, and numerous public reports of violations of the law. It has even survived Trump’s belief that he was being personally spied on by the intelligence community, as well as any congressional fears that Trump could abuse the authority in the coming years. And though this extension lasts only six years, it’s inconceivable to me that it will ever be repealed at this point.

So what do we do? If we can’t fight this particular statutory authority, where’s the new front on surveillance? There are, it turns out, reasonable modifications that target surveillance more generally, and not in terms of any particular statutory authority. We need to look at US surveillance law more generally.

First, we need to strengthen the minimization procedures to limit incidental collection. Since the Internet was developed, all the world’s communications travel around in a single global network. It’s impossible to collect only foreign communications, because they’re invariably mixed in with domestic communications. This is called “incidental” collection, but that’s a misleading name. It’s collected knowingly, and searched regularly. The intelligence community needs much stronger restrictions on which American communications channels it can access without a court order, and rules that require they delete the data if they inadvertently collect it. More importantly, “collection” is defined as the point the NSA takes a copy of the communications, and not later when they search their databases.

Second, we need to limit how other law enforcement agencies can use incidentally collected information. Today, those agencies can query a database of incidental collection on Americans. The NSA can legally pass information to those other agencies. This has to stop. Data collected by the NSA under its foreign surveillance authority should not be used as a vehicle for domestic surveillance.

The most recent reauthorization modified this lightly, forcing the FBI to obtain a court order when querying the 702 data for a criminal investigation. There are still exceptions and loopholes, though.

Third, we need to end what’s called “parallel construction.” Today, when a law enforcement agency uses evidence found in this NSA database to arrest someone, it doesn’t have to disclose that fact in court. It can reconstruct the evidence in some other manner once it knows about it, and then pretend it learned of it that way. This right to lie to the judge and the defense is corrosive to liberty, and it must end.

Pressure to reform the NSA will probably first come from Europe. Already, European Union courts have pointed to warrantless NSA surveillance as a reason to keep Europeans’ data out of US hands. Right now, there is a fragile agreement between the EU and the United States ­– called “Privacy Shield” — ­that requires Americans to maintain certain safeguards for international data flows. NSA surveillance goes against that, and it’s only a matter of time before EU courts start ruling this way. That’ll have significant effects on both government and corporate surveillance of Europeans and, by extension, the entire world.

Further pressure will come from the increased surveillance coming from the Internet of Things. When your home, car, and body are awash in sensors, privacy from both governments and corporations will become increasingly important. Sooner or later, society will reach a tipping point where it’s all too much. When that happens, we’re going to see significant pushback against surveillance of all kinds. That’s when we’ll get new laws that revise all government authorities in this area: a clean sweep for a new world, one with new norms and new fears.

It’s possible that a federal court will rule on Section 702. Although there have been many lawsuits challenging the legality of what the NSA is doing and the constitutionality of the 702 program, no court has ever ruled on those questions. The Bush and Obama administrations successfully argued that defendants don’t have legal standing to sue. That is, they have no right to sue because they don’t know they’re being targeted. If any of the lawsuits can get past that, things might change dramatically.

Meanwhile, much of this is the responsibility of the tech sector. This problem exists primarily because Internet companies collect and retain so much personal data and allow it to be sent across the network with minimal security. Since the government has abdicated its responsibility to protect our privacy and security, these companies need to step up: Minimize data collection. Don’t save data longer than absolutely necessary. Encrypt what has to be saved. Well-designed Internet services will safeguard users, regardless of government surveillance authority.

For the rest of us concerned about this, it’s important not to give up hope. Everything we do to keep the issue in the public eye ­– and not just when the authority comes up for reauthorization again in 2024 — hastens the day when we will reaffirm our rights to privacy in the digital age.

This essay previously appeared in the Washington Post.

2017 Weather Station round-up

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/2017-weather-station/

As we head into 2018 and start looking forward to longer days in the Northern hemisphere, I thought I’d take a look back at last year’s weather using data from Raspberry Pi Oracle Weather Stations. One of the great things about the kit is that as well as uploading all its readings to the shared online Oracle database, it stores them locally on the Pi in a MySQL or MariaDB database. This means you can use the power of SQL queries coupled with Python code to do automatic data analysis.

Soggy Surrey

My Weather Station has only been installed since May, so I didn’t have a full 52 weeks of my own data to investigate. Still, my station recorded more than 70000 measurements. Living in England, the first thing I wanted to know was: which was the wettest month? Unsurprisingly, both in terms of average daily rainfall and total rainfall, the start of the summer period — exactly when I went on a staycation — was the soggiest:

What about the global Weather Station community?

Even soggier Bavaria

Here things get slightly trickier. Although we have a shiny Oracle database full of all participating schools’ sensor readings, some of the data needs careful interpretation. Many kits are used as part of the school curriculum and do not always record genuine outdoor conditions. Nevertheless, it appears that Adalbert Stifter Gymnasium in Bavaria, Germany, had an even wetter 2017 than my home did:


View larger map

Where the wind blows

The records Robert-Dannemann Schule in Westerstede, Germany, is a good example of data which was most likely collected while testing and investigating the weather station sensors, rather than in genuine external conditions. Unless this school’s Weather Station was transported to a planet which suffers from extreme hurricanes, it wasn’t actually subjected to wind speeds above 1000km/h in November. Dismissing these and all similarly suspect records, I decided to award the ‘Windiest location of the year’ prize to CEIP Noalla-Telleiro, Spain.


View larger map

This school is right on the coast, and is subject to some strong and squally weather systems.

Weather Station at CEIP Noalla - Telleiro

Weather Station at CEIP Noalla-Telleiro

They’ve mounted their wind vane and anemometer nice and high, so I can see how they were able to record such high wind velocities.

A couple of Weather Stations have recently been commissioned in equally exposed places — it will be interesting to see whether they will record even higher speeds during 2018.

Highs and lows

After careful analysis and a few disqualifications (a couple of Weather Stations in contention for this category were housed indoors), the ‘Hottest location’ award went to High School of Chalastra in Thessaloniki, Greece. There were a couple of Weather Stations (the one at The Marwadi Education Foundation in India, for example) that reported higher average temperatures than Chalastra’s 24.54 ºC. However, they had uploaded far fewer readings and their data coverage of 2017 was only partial.


View larger map

At the other end of the thermometer, the location with the coldest average temperature is École de la Rose Sauvage in Calgary, Canada, with a very chilly 9.9 ºC.

Ecole de la Rose sauvage Weather Station

Weather Station at École de la Rose Sauvage

I suspect this school has a good chance of retaining the title: their lowest 2017 temperature of -24 ºC is likely to be beaten in 2018 due to extreme weather currently bringing a freezing start to the year in that part of the world.


View larger map

Analyse your own Weather Station data

If you have an Oracle Raspberry Pi Weather Station and would like to perform an annual review of your local data, you can use this Python script as a starting point. It will display a monthly summary of the temperature and rainfall for 2017, and you should be able to customise the code to focus on other sensor data or on a particular time of year. We’d love to see your results, so please share your findings with [email protected], and we’ll send you some limited-edition Weather Station stickers.

The post 2017 Weather Station round-up appeared first on Raspberry Pi.

Create SLUG! It’s just like Snake, but with a slug

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/slug-snake/

Recreate Snake, the favourite mobile phone game from the late nineties, using a slug*, a Raspberry Pi, a Sense HAT, and our free resource!

Raspberry Pi Sense HAT Slug free resource

*A virtual slug. Not a real slug. Please leave the real slugs out in nature.

Snake SLUG!

Move aside, Angry Birds! On your bike, Pokémon Go! When it comes to the cream of the crop of mobile phone games, Snake holds the top spot.

Snake Nokia Game

I could while away the hours…

You may still have an old Nokia 3310 lost in the depths of a drawer somewhere — the drawer that won’t open all the way because something inside is jammed at an odd angle. So it will be far easier to grab your Pi and Sense HAT, or use the free Sense HAT emulator (online or on Raspbian), and code Snake SLUG yourself. In doing so, you can introduce the smaller residents of your household to the best reptile-focused game ever made…now with added mollusc.

The resource

To try out the game for yourself, head to our resource page, where you’ll find the online Sense HAT emulator embedded and ready to roll.

Raspberry Pi Sense HAT Slug free resource

It’ll look just like this, and you can use your computer’s arrow keys to direct your slug toward her tasty treats.

From there, you’ll be taken on a step-by-step journey from zero to SLUG glory while coding your own versionof the game in Python. On the way, you’ll learn to work with two-dimensional lists and to use the Sense HAT’s pixel display and joystick input. And by completing the resource, you’ll expand your understanding of applying abstraction and decomposition to solve more complex problems, in line with our Digital Making Curriculum.

The Sense HAT

The Raspberry Pi Sense HAT was originally designed and made as part of the Astro Pi mission in December 2015. With an 8×8 RGB LED matrix, a joystick, and a plethora of on-board sensors including an accelerometer, gyroscope, and magnetometer, it’s a great add-on for your digital making toolkit, and excellent for projects involving data collection and evaluation.

You can find more of our free Sense HAT tutorials here, including for making Flappy Bird Astronaut, a marble maze, and Pong.

The post Create SLUG! It’s just like Snake, but with a slug appeared first on Raspberry Pi.

Rosie the Countdown champion

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/rosie-the-countdown-champion/

Beating the contestants at Countdown: is it cheating if you happen to know every word in the English dictionary?

Rosie plays Countdown

Allow your robots to join in the fun this Christmas with a round of Channel 4’s Countdown. https://www.rosietheredrobot.com/2017/12/tea-minus-30.html

Rosie the Red Robot

First, a little bit of backstory. Challenged by his eldest daughter to build a robot, technology-loving Alan got to work building Rosie.

I became (unusually) determined. I wanted to show her what can be done… and the how can be learnt later. After all, there is nothing more exciting and encouraging than seeing technology come alive. Move. Groove. Quite literally.

Originally, Rosie had a Raspberry Pi 3 brain controlling ultrasonic sensors and motors via Python. From there, she has evolved into something much grander, and Alan has documented her upgrades on the Rosie the Red Robot blog. Using GPS trackers and a Raspberry Pi camera module, she became Rosie Patrol, a rolling, walking, interactive bot; then, with further upgrades, the Tea Minus 30 project came to be. Which brings us back to Countdown.

T(ea) minus 30

In case it hasn’t been a big part of your life up until now, Countdown is one of the longest running televisions shows in history, and occupies a special place in British culture. Contestants take turns to fill a board with nine randomly selected vowels and consonants, before battling the Countdown clock to find the longest word they can in the space of 30 seconds.

The Countdown Clock

I’ve had quite a few requests to show just the Countdown clock for use in school activities/own games etc., so here it is! Enjoy! It’s a brand new version too, using the 2010 Office package.

There’s a numbers round involving arithmetic, too – but for now, we’re going to focus on letters and words, because that’s where Rosie’s skills shine.

Using an online resource, Alan created a dataset of the ten thousand most common English words.

Rosie the Red Robot Raspberry Pi

Many words, listed in order of common-ness. Alan wrote a Python script to order them alphabetically and by length

Next, Alan wrote a Python script to select nine letters at random, then search the word list to find all the words that could be spelled using only these letters. He used the randint function to select letters from a pre-loaded alphabet, and introduced a requirement to include at least two vowels among the nine letters.

Rosie the Red Robot Raspberry Pi

Words that match the available letters are displayed on the screen.

Rosie the Red Robot Raspberry Pi

Putting it all together

With the basic game-play working, it was time to bring the project to life. For this, Alan used Rosie’s camera module, along with optical character recognition (OCR) and text-to-speech capabilities.

Rosie the Red Robot Raspberry Pi

Alan writes, “Here’s a very amateurish drawing to brainstorm our idea. Let’s call it a design as it makes it sound like we know what we’re doing.”

Alan’s script has Rosie take a photo of the TV screen during the Countdown letters round, then perform OCR using the Google Cloud Vision API to detect the nine letters contestants have to work with. Next, Rosie runs Alan’s code to check the letters against the ten-thousand-word dataset, converts text to speech with Python gTTS, and finally speaks her highest-scoring word via omxplayer.

You can follow the adventures of Rosie the Red Robot on her blog, or follow her on Twitter. And if you’d like to build your own Rosie, Alan has provided code and tutorials for his projects too. Thanks, Alan!

The post Rosie the Countdown champion appeared first on Raspberry Pi.

Tracking People Without GPS

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/12/tracking_people_5.html

Interesting research:

The trick in accurately tracking a person with this method is finding out what kind of activity they’re performing. Whether they’re walking, driving a car, or riding in a train or airplane, it’s pretty easy to figure out when you know what you’re looking for.

The sensors can determine how fast a person is traveling and what kind of movements they make. Moving at a slow pace in one direction indicates walking. Going a little bit quicker but turning at 90-degree angles means driving. Faster yet, we’re in train or airplane territory. Those are easy to figure out based on speed and air pressure.

After the app determines what you’re doing, it uses the information it collects from the sensors. The accelerometer relays your speed, the magnetometer tells your relation to true north, and the barometer offers up the air pressure around you and compares it to publicly available information. It checks in with The Weather Channel to compare air pressure data from the barometer to determine how far above sea level you are. Google Maps and data offered by the US Geological Survey Maps provide incredibly detailed elevation readings.

Once it has gathered all of this information and determined the mode of transportation you’re currently taking, it can then begin to narrow down where you are. For flights, four algorithms begin to estimate the target’s location and narrows down the possibilities until its error rate hits zero.

If you’re driving, it can be even easier. The app knows the time zone you’re in based on the information your phone has provided to it. It then accesses information from your barometer and magnetometer and compares it to information from publicly available maps and weather reports. After that, it keeps track of the turns you make. With each turn, the possible locations whittle down until it pinpoints exactly where you are.

To demonstrate how accurate it is, researchers did a test run in Philadelphia. It only took 12 turns before the app knew exactly where the car was.

This is a good example of how powerful synthesizing information from disparate data sources can be. We spend too much time worried about individual data collection systems, and not enough about analysis techniques of those systems.

Research paper.