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asassy

Member Since: July 5, 2019

Country: United States

The second part of a series on different technologies that could be used to find missing objects - in this case a lost frisbee.

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The first part of a series on different technologies that could be used to find missing objects - in this case a lost frisbee.

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Once Rob and Avra receive a text with GPS coordinates, who will get to the location fastest? They find out using trains, planes, bikes, blades, and of course the new SparkFun LTE GNSS Breakout with SARA-R5.

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We're releasing the living hookup guide for the Machine Learning @ Home Kit! Put what you've learned in the DLI Course to practice and make your home a little smarter with machine learning!

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A look at how many environmental and agricultural problems can be addressed with the same framework of audio analysis and environmental sensing through machine learning!

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No need for a multimeter - just build a component tester yourself with a Redboard and Flowcode!

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See the full prototyping process of building a wireless system for business applications from the experts at Tincubate!

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From Teletubbies to noodles - a game that navigates through Wikipedia from one topic to another on the MicroMod ecosystem.

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Porting the DIY cabin temperature monitoring system to MicroMod

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Our attempt to fly a drone with some of the same technology as NASA and none of the international scrutiny.

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Building a question and answer machine learning model with the RP2040 Thing Plus

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A closer look at the open-source technology NASA has deployed on the tiny Martian helicopter

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A closer look at all the possibilities Loomia wearable products create!

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Building a network of temperature monitors through an IoT app.

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Capitalizing on the new wearable of masks by integrating technology to better understand breath composition and how it correlates to health.

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Building a system that records temperature based on a user's unique RFID tag into an Excel spreadsheet for the world of COVID-19.

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Zach Halvorson, an engineering student at Boston University and a coffee enthusiast, is out to create the most affordable, technological tools to perfect the coffee roasting process.

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Utilizing the robust libraries and machine learning algorithms from OpenMV, let's explore how to do object detection on trails to see how much they get used!

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The added functionality of BLE in the Artemis Development Kit means it can be connected to a local web application! We explore this by streaming the on-board sensor data to a local host.

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We tested out the new SparkFun OpenLog Artemis by taking it down one of Boulder's many hills of a driftable trike!

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An exploration of the different frameworks you can use with your Raspberry Pi to connect to the web!

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We test out our new GPS-RTK Dead Reckoning Boards by sending them down the river!

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Let's explore a Real-Time Operating System for RISC-V.

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Meet Avra!

SparkFun's newest Technical Content Creator is joining Rob to bring you all the information you need on SparkFun products and projects!

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Machine Learning @ Home Kit Hookup Guide

July 16, 2021

A living hookup guide with three different machine learning applications that will help you around the home as well as teach the basics of taking the examples of machine learning you find in the DLI Course and working towards deploying those projects beyond an example in Jupyter Notebooks!

Introduction to the Raspberry Pi GPIO and Physical Computing

January 20, 2021

This tutorial will teach you how to set up your Raspberry Pi as a full desktop computer and read in sensor data through the GPIO using SparkFun hardware.
  • Alas, that is unfortunately true, it is only designed for Windows.

  • This is a wonderful point to bring up - think about how much more diverse the group of scientists is who worked on this launch versus the group trying to put a man on the moon. I think what I love about the Perseverence/Ingenuity launch is the reflection it has on the improvements we are making as humans, not just the fact that we are searching for life on another planet.

    Another fun note - the name Ingenuity came from a high school junior named Vaneeza Rupan...she won NASA's "Name the Rover" essay contest.

  • Ahhh, yes this is incredibly helpful! It always comes back to dimensionality and unit conversion. Let me revisit my code and see if approximating lung capacity (I think it's ~4L for women) will help clarify the data. Thank you!

  • The Bosch Arduino library did a lot of the heavy lifting - it's incredibly robust! Here's a link to repo if you'd like to check it out!

    https://github.com/BoschSensortec/BSEC-Arduino-library

  • Hi there! I'm hoping this resource should help: https://classic.yarnpkg.com/en/docs/cli/install/

    yarn install installs all the dependencies listed within package.json. So first, make sure that yarn is installed on your specific operating system (https://classic.yarnpkg.com/en/docs/install#mac-stable), and then run yarn install in the directory to install all of the packages for this project.

    If this still doesn't directly answer your question, don't hesitate to comment again!

  • There are a ton of incredible possibilities for applications with SDR! Anywhere from diving deep into radio astronomy to building your own cellular network. Thanks for the input...I'll be sure to incorporate SDR into future projects!