Share Your Project At The Next Open Source Hardware Summit

The Open Source Hardware Summit (OHS) is inviting talk/demo proposals for the tenth annual summit! To be held on Friday, March 13,2020 at the NYU School of Law. The Open Source Hardware Summit is for presenting, discussing, and learning about open hardware of all kinds. The summit examines open hardware […]

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The 555SE and 741SE surface-mount soldering kits

555SE and 741SE kits

Today we are pleased to announce the release of two new soldering kits: the 555SE discrete 555 timer and the 741SE discrete op-amp.

Both of these new kits are surface mount soldering kits — our first surface mount soldering kits — and we think that you’re going to love them.

555 kits, big and small

You might be familiar with our Three Fives discrete 555 timer and XL741 discrete op-amp kits. Both are easy soldering kits that let you build working transistor-scale replicas of the classic 555 timer chip and the famous µA741 op-amp. Those two are constructed with traditional through-hole soldering techniques and are styled to like “DIP” packaged (through-hole) integrated circuits.

Our new 555SE and 741SE kits implement the same circuits, now with surface mount components, and are styled to look like smaller “SOIC” packaged (surface mount) integrated circuits, complete with a heavy-gauge aluminum leadframe stand. Side by side with their through-hole siblings, the new kits are exactly to scale, with half the lead pitch and a lower profile.

555SE kit for scale

The 555SE and 741SE kits each come with eight (tiny) color-coded thumbscrew binding posts that you can use to hook up wires and other connections.

You can also probe anywhere that you like in these circuits — something that you generally can’t do with the integrated circuit versions.

741SE kit close up

The new 555SE and 741SE circuit boards are black in color, with a gold finish and clear solder mask so that you can see the wiring traces between individual components. There are a few other neat details here and there, such as countersunk holes for mounting the board to the leadframe.

The surface mount components are relatively large, with 1206-sized resistors and SOT-23 sized transistors, and assembly is straightforward with our clear and comprehensive instructions. These kits are designed to be a joy to build, whether you’re an old hand at surface mount soldering, want some practice before tackling a project, or are introducing someone to it for the first time.

Family portrait

And here is the new family: XL741, the Three Fives, along with the new 741SE and 555SE.

You can find the datasheets and assembly instructions for these kits, as well as links to additional documentation, on their respective product pages.

Both new kits are part of our ongoing collaboration with Eric Schlaepfer, who we have worked with on a number of dis-integrated circuit projects including the four kits here and the MOnSter 6502.

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Check Out These Fascinating Videos Of Hand Carving Wood

It seems that the well of mesmerizing hand skills is infinitely deep. There’s always a new one to discover and consequently lose hours to, obsessing over how fascinating it is. In this case, we meet Tatiana Baldina who is incredibly skilled at carving wood.   The process can appear deceptively […]

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The Intelligence Community Took Months to Respond to a Key Question About Section 215, And It Still Doesn’t Have Any “Legal Conclusion”

Even with the looming expiration of Section 215 and other key provisions of the Patriot Act, it took the Intelligence Community almost four months to respond to a letter written by Senator Ron Wyden (D-Oregon) seeking clarification on how the Intelligence Community interprets the landmark Supreme Court decision in Carpenter v. United States and whether it is using Section 215 to collect Americans’ location data.

Wyden’s concerns were entirely justified. We know that the NSA has used Section 215 to collect cell phone location data in the past. But last year in Carpenter, the Supreme Court held that police violated the Fourth Amendment when they collected days of cell site location information about a robbery suspect without a warrant. In his letter, Senator Wyden noted that he and other senators had repeatedly asked others in the government what it saw as Carpenter’s effects on the intelligence community, but hadn’t gotten any answers. Indeed, EFF, ACLU, and others have been asking these same questions. “If Congress is to reauthorize Section 215 before it expires in December,” Wyden wrote, “it needs to know how this law is being interpreted now, as well as how it could be interpreted in the future.”

Senator Wyden sent that letter to the then-Director of National Intelligence (DNI) Dan Coats on July 30, and then he waited. And waited. And waited.

Now, we finally have a response. Unfortunately, it’s not a very satisfying one. In his November 14 response, Assistant DNI Benjamin Fallon wrote that although the DOJ and ODNI have not used Section 215 to collect location data since Carpenter was decided, they had not “reached a legal conclusion” about whether they were authorized to do so.

We recognize this belated nonanswer for what it is—a signal that the intelligence community is not taking its duties of transparency and oversight seriously. Carpenter may be the most important privacy decision from the Supreme Court in a generation, and it should have clear and immediate impact on any warrantless collection of location data as part of criminal investigations and intelligence activities alike. And even if lawyers for the intelligence community read the case differently, they should be able to reach a “legal conclusion” eighteen months after it was decided.

Congress and the public deserve to see these legal conclusions, especially given the NSA’s track record of reaching secret interpretations of Section 215 that crumble under scrutiny by courts. Moreover, waiting this much time only to reveal such paltry information about how these far-reaching surveillance programs function is unacceptable. Politics takes time. Legislation takes time. And to leave these types of answers until the last-minute shows how little regard the Intelligence Community has for Congressional oversight of their invasive surveillance programs.

Now, a 90-day reauthorization of Section 215 and other provisions of the Patriot Act is being shoved into a continuing resolution to fund the government. This tactic will no doubt be touted as a necessity because of the short timespan before the December 15 sunset. The government might not be feeling the pinch, of course, if it took this more seriously and engaged in even the most basic transparency.

Now that we have ODNI’s paltry response to Senator Wyden’s question, it is even more important that Congress pass Section 215 legislation that includes clarification that the law cannot be used to collect location information. Simply put, Congress and the Intelligence Community cannot put off reckoning with Section 215 indefinitely. EFF and others have been pushing for significant reforms to the law—including codifying Carpenter’s effect—and we will fight just as hard as the new sunset date approaches.

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James Bruton’s 512 LED DJ helmet adds more glow to his Performance Robots show

If you’ve ever thought that your musical performance needed more LEDs, then James Bruton’s DJ helmet may be just the thing for you.

The YouTuber’s wearable device is built on the base of a protective face shield, substituting in a 3D-printed support for an 8×32 LED matrix, as well as four smaller 8×8 LED matrices arranged above and below the main section.

The 512 LEDs are powered using a portable LiPo battery and a 10A power regulator. Control is via an Arduino Mega, which is connected to an RJ45 jack that enables it to work with DMX lighting data. 

The result is a spectacular display, shown off nicely in an electronic concert (with his barcode guitar) starting at around 8:20 in the video below!

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Soofa Signs use solar power and electronic paper – Giant EInk … wow! @mysoofa

Eink01-2

Soofa Signs use solar power and electronic paper, and by that I mean it was a giant 42″ eink display, I’ve never seen one this big so I needed to take a photo and look it up later (now), it appears to be this one, here’s a bit about the company…

Soofa is a female founded company, launched out of MIT and Harvard in 2014. Soofa is for people with a shared stake in a special place. We create the neighborhood news feed that connects a community with screens everyone can see and anyone can use. Our Soofa Signs provide a platform for everyone in the community to have a voice with Soofa Talk.

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Related

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With a Raid on Javier Smaldone, Argentinian Authorities Have Restarted Their Harassment of E-Voting Critics

Javier Smaldone is a well-known figure in the Argentinian infosec community. As a security researcher, he’s worked to highlight the flaws in electronic voting in Argentina, despite the country’s local and federal attempts to move ahead with insecure software and electoral procedures.

The Argentinian authorities have a reputation of responding poorly to such criticism: In 2016, when Joaquín Sorianello warned an e-voting company about vulnerabilities in their e-voting software, his home was raided by the Buenos Aires’ police. Another technologist, Ivan Barrera Oro, was raided in 2017 shortly after demonstrating voting vulnerabilities in current software. The cases against Sorianello and Oro were both subsequently dismissed.

Now, it seems, it’s Smaldone’s turn to fend off a questionable criminal investigation. In early October, his home in Buenos Aires was raided by federal police, his phone and computers seized, and he was detained for questioning. The warrant for the search was in connection with a highly-publicized leak of data, exfiltrated in late July from the federal police themselves. The 700GB data was hosted anonymously, and caused political embarrassment both to law enforcement and the Argentinian politicians mentioned in the leaks.

Smaldone’s surprising raid was one of a series across the country against technologists by law enforcement investigating the leak. However, the material submitted by the police to the courts to obtain a warrant mostly points to perfectly lawful acts of free expression that would be entirely expected from an outspoken security researcher—not to any suspicious acts by Smaldone.

Police cited as incriminating Smaldone’s public discussion on Twitter of the high-profile politicians whose data was in the leaks, and his own subsequent analyses (on his blog and in the media) about how the attacks were carried out. It’s not surprising – much less incriminating – that Smaldone, who has testified before the Argentinian Senate on cyber-security, might have political opinions, or might express his expert opinion on the attacks. The police also claim in the request that Smaldone’s Twitter accounts “constantly expresses aversion to the police,” and that this “aversion” sometimes goes “beyond mockery.” But, again, this is not evidence of a crime.

Additional technical evidence for Smaldone’s involvement is weak, based on vague correlations between the geotracked location of Smaldone’s phone and activity related to the attack. The police even submitted as evidence that the leakers’ Tor onion service used the same version of the Nginx web server software as Smaldone’s blog – despite the fact that their shared version was the latest, stable update of what is currently the most popular web server application in the world, and was therefore also installed on millions of other Nginx servers at the time. At least based on the evidence that has been publicly disclosed, the raid on Smaldone appears unjustified.

Smaldone’s case is the latest, not just in a pattern of persecution against e-voting critics in Argentina, but in an accelerating trend of misunderstanding and scapegoating technologists in the wider region – one which we described in detail in our 2018 whitepaper, “Protecting Security Researchers’ Rights in the Americas.” Latin American technologists are increasingly caught up in unrelated, politicized, cyber-security investigations, with little evidence, conducted under too broad laws, by poorly-advised justices.

EFF has been fighting against such prosecutions since its founding in 1990. Argentina’s Javier Smaldone joins Ecuador’s Ola Bini as independent computer experts who are still being treated as dangerous suspects, for no more than practicing their lawful work, and using their inalienable right of free expression. We have joined with Access Now, and digital rights groups across the Americas in a letter to the judge and justice minister involved in the case, calling for Smaldone’s rights to be respected.

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Join Us On November 21 For a LIVE Tour Of Co Lab Community Makerspace

Coming up on November 21, we’ll be taking a live tour of Co.Lab Community Makers thanks to Digi-Key, our sponsors. This is an incredible space located in Austin Texas that is free to use! We did a Makerspace Spotlight article where you can learn more about the space. It looks […]

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Friday Product Post: Pro Skills Pay the Bills

Hello everyone and happy Friday! We're kicking off the week with a brand new development kit to make you a pro with the Qwiic Connect System. Following the kit, we have a super small Serial Flash Memory IC that will be perfect with the Qwiic Micro Development Board we released a few weeks ago, as well as a new 2.8Ah LiPo battery pack. Let's dive in!

Be a Qwiic Pro with the Qwiic Pro Kit!

SparkFun Qwiic Pro Kit

added to your cart!

SparkFun Qwiic Pro Kit

33 available KIT-15349

This kit provides you with a RedBoard Turbo, two sensors, a joystick, and an OLED screen as well as all the cables you need t…

$59.95

Looking to get started with the SparkFun Qwiic system? The Qwiic Pro Kit provides you with a RedBoard Turbo, two sensors, two accessory boards and all the cables you need to start utilizing Qwiic and I2C easily. This kit was designed to allow users to get started with Arduino without the need for soldering or breadboarding. Hooking up a handful of inputs and outputs to a RedBoard has never been so easy - use the joystick, accelerometer or proximity sensor, and one small display for outputting text, graphics or even a microPong game!


Low Current Lithium Ion Battery Pack - 2.5Ah (USB)

added to your cart!

Low Current Lithium Ion Battery Pack - 2.5Ah (USB)

In stock TOL-15204

We've taken the classic, portable, rechargeable lithium ion battery pack and tweaked the design to make it amenable to low cu…

$9.95

We've taken the classic, portable, rechargeable lithium ion battery pack and tweaked the design to make it applicable to low-current applications. While similar power banks are typically designed to power off automatically at lower currents, this battery pack will continue to operate if your device is drawing a mere 20mA or more. Just connect your device to the USB-A port on the battery pack and you're good to go! To recharge the battery pack, just plug it into your computer or phone charger using the included USB micro-B cable.


Serial Flash Memory - W25Q32FV (32Mb, 104MHz, SOIC-8)

added to your cart!

Serial Flash Memory - W25Q32FV (32Mb, 104MHz, SOIC-8)

28 available COM-15809

The W25Q32FV (32M-bit) Serial Flash memory provides a storage solution for systems with limited space, pins, and power.

$0.95

The W25Q32FV (32M-bit) Serial Flash memory provides a storage solution for systems with limited space, pins and power. This small SMD IC series offers flexibility and performance well beyond ordinary Serial Flash devices. They are ideal for code shadowing to RAM, executing code directly from Dual/Quad SPI (XIP), and storing voice, text and data.


That's it for this week! As always, we can't wait to see what you make! Shoot us a tweet @sparkfun, or let us know on Instagram or Facebook. We’d love to see what projects you’ve made!

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Fruit identification using Arduino and TensorFlow

By Dominic Pajak and Sandeep Mistry

Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple machine vision and even an end-to-end gesture recognition training tutorial. For a comprehensive background we recommend you take a look at that article

In this article we are going to walk through an even simpler end-to-end tutorial using the TensorFlow Lite Micro library and the Arduino Nano 33 BLE Sense’s colorimeter and proximity sensor to classify objects. To do this, we will be running a small neural network on the board itself. 

Arduino BLE 33 Nano Sense running TensorFlow Lite Micro

The philosophy of TinyML is doing more on the device with less resources – in smaller form-factors, less energy and lower cost silicon. Running inferencing on the same board as the sensors has benefits in terms of privacy and battery life and means its can be done independent of a network connection. 

The fact that we have the proximity sensor on the board means we get an instant depth reading of an object in front of the board – instead of using a camera and having to determine if an object is of interest through machine vision. 

In this tutorial when the object is close enough we sample the color – the onboard RGB sensor can be viewed as a 1 pixel color camera. While this method has limitations it provides us a quick way of classifying objects only using a small amount of resources. Note that you could indeed run a complete CNN-based vision model on-device. As this particular Arduino board includes an onboard colorimeter, we thought it’d be fun and instructive to demonstrate in this way to start with.

We’ll show a simple but complete end-to-end TinyML application can be achieved quickly and without a deep background in ML or embedded. What we cover here is data capture, training, and classifier deployment. This is intended to be a demo, but there is scope to improve and build on this should you decide to connect an external camera down the road. We want you to get an idea of what is possible and a starting point with tools available.

What you’ll need

About the Arduino board

The Arduino Nano 33 BLE Sense board we’re using here has an Arm Cortex-M4 microcontroller running mbedOS and a ton of onboard sensors – digital microphone, accelerometer, gyroscope, temperature, humidity, pressure, light, color and proximity. 

While tiny by cloud or mobile standards the microcontroller is powerful enough to run TensorFlow Lite Micro models and classify sensor data from the onboard sensors.

Setting up the Arduino Create Web Editor

In this tutorial we’ll be using the Arduino Create Web Editor – a cloud-based tool for programming Arduino boards. To use it you have to sign up for a free account, and install a plugin to allow the browser to communicate with your Arduino board over USB cable.

You can get set up quickly by following the getting started instructions which will guide you through the following:

  • Download and install the plugin
  • Sign in or sign up for a free account

(NOTE: If you prefer, you can also use the Arduino IDE desktop application. The setup for which is described in the previous tutorial.)

Capturing training data

We now we will capture data to use to train our model in TensorFlow. First, choose a few different colored objects. We’ll use fruit, but you can use whatever you prefer. 

Setting up the Arduino for data capture

Next we’ll use Arduino Create to program the Arduino board with an application object_color_capture.ino that samples color data from objects you place near it. The board sends the color data as a CSV log to your desktop machine over the USB cable.

To load the object_color_capture.ino application onto your Arduino board:

  • Connect your board to your laptop or PC with a USB cable
    • The Arduino board takes a male micro USB
  • Open object_color_capture.ino in Arduino Create by clicking this link

Your browser will open the Arduino Create web application (see GIF above).

  • Press OPEN IN WEB EDITOR
    • For existing users this button will be labeled ADD TO MY SKETCHBOOK
  • Press Upload & Save
    • This will take a minute
    • You will see the yellow light on the board flash as it is programmed
  • Open the serial Monitor
    • This opens the Monitor panel on the left-hand side of the web application
    • You will now see color data in CSV format here when objects are near the top of the board

Capturing data in CSV files for each object

For each object we want to classify we will capture some color data. By doing a quick capture with only one example per class we will not train a generalized model, but we can still get a quick proof of concept working with the objects you have to hand! 

Say, for example, we are sampling an apple:

  • Reset the board using the small white button on top.
    • Keep your finger away from the sensor, unless you want to sample it!
    • The Monitor in Arduino Create will say ‘Serial Port Unavailable’ for a minute
  • You should then see Red,Green,Blue appear at the top of the serial monitor
  • Put the front of the board to the apple. 
    • The board will only sample when it detects an object is close to the sensor and is sufficiently illuminated (turn the lights on or be near a window)
  • Move the board around the surface of the object to capture color variations
  • You will see the RGB color values appear in the serial monitor as comma separated data. 
  • Capture at a few seconds of samples from the object
  • Copy and paste this log data from the Monitor to a text editor
    • Tip: untick AUTOSCROLL check box at the bottom to stop the text moving
  • Save your file as apple.csv
  • Reset the board using the small white button on top.

Do this a few more times, capturing other objects (e.g. banana.csv, orange.csv). 

NOTE: The first line of each of the .csv files should read:

Red,Green,Blue

If you don’t see it at the top, you can just copy and paste in the line above. 

Training the model

We will now use colab to train an ML model using the data you just captured in the previous section.

  • First open the FruitToEmoji Jupyter Notebook in colab
  • Follow the instructions in the colab
    • You will be uploading your *.csv files 
    • Parsing and preparing the data
    • Training a model using Keras
    • Outputting TensorFlowLite Micro model
    • Downloading this to run the classifier on the Arduino 

With that done you will have downloaded model.h to run on your Arduino board to classify objects!

The colab will guide you to drop your .csv files into the file window, the result shown above
Normalized color samples captured by the Arduino board are graphed in colab

Program TensorFlow Lite Micro model to the Arduino board

Finally, we will take the model we trained in the previous stage and compile and upload to our Arduino board using Arduino Create. 

Your browser will open the Arduino Create web application:

  • Press the OPEN IN WEB EDITOR button
  • Import the  model.h you downloaded from colab using Import File to Sketch: 
Import the model.h you downloaded from colab
The model.h tab should now look like this
  • Compile and upload the application to your Arduino board 
    • This will take a minute
    • When it’s done you’ll see this message in the Monitor:
  • Put your Arduino’s RGB sensor near the objects you trained it with
  • You will see the classification output in the Monitor:
Classifier output in the Arduino Create Monitor

You can also edit the object_color_classifier.ino sketch to output emojis instead (we’ve left the unicode in the comments in code!), which you will be able to view in Mac OS X or Linux terminal by closing the web browser tab with Arduino Create in, resetting your board, and typing cat /cu/usb.modem[n]. 

Output from Arduino serial to Linux terminal using ANSI highlighting and unicode emojis

Learning more

The resources around TinyML are still emerging but there’s a great opportunity to get a head start and meet experts coming up 2-3 December 2019 in Mountain View, California at the Arm IoT Dev Summit. This includes workshops from Sandeep Mistry, Arduino technical lead for on-device ML and from Google’s Pete Warden and Daniel Situnayake who literally wrote the book on TinyML. You’ll be able to hang out with these experts and more at the TinyML community sessions there too. We hope to see you there!

Conclusion

We’ve seen a quick end-to-end demo of machine learning running on Arduino. The same framework can be used to sample different sensors and train more complex models. For our object by color classification we could do more, by sampling more examples in more conditions to help the model generalize. In future work, we may also explore how to run an on-device CNN. In the meantime, we hope this will be a fun and exciting project for you. Have fun!

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