Talk to your rPi over a Slack channel, getting status, controlling devices, and running shell (bash) commands and viewing the console output posted back to your Slack channel
In this two-part series, you’ll learn how to turn a Raspberry Pi into a flexible and powerful Internet-of-Things device with AWS Greengrass v2, ultimately creating an off-grid remote-tasked data and image collection device.
Talk to your rPi over a Slack channel, getting status, controlling devices, and running shell (bash) commands and viewing the console output posted back to your Slack channel
As a first application that involves hardware, I decided to keep things simple. The idea is just to hook up a couple Inputs and Outputs and have a program that will work with the GPIO to read/write the values.
Build a motion-triggered cam with room illumination, off-site storage of captured movies, and secure remote administration... all with simple bash scripts. Great for off-site monitoring and wildlife cams.
Processing the positioning information of visual objects detected by Pixy camera and received on Raspberry Pi via I2C, and using common design patterns in a C# program parsing robotics sensor data
This article series will show you how to build a reasonably accurate traffic speed detector using nothing but Deep Learning, and run it on an edge device like a Raspberry Pi.
In this article, we’ll wrap the face identification model in a simple Web API, create a client application on the Raspberry Pi, and run the client-server system.
In this article, we focus on developing a computer vision framework that can run the various Machine Learning and neural network models – like SSD MobileNet – on live and recorded vehicle traffic videos.
In this article, we explore the different ways of measuring vehicle speed and the different Deep Learning models for object detection that can be used in our TrafficCV program.
In this article, we’ll test our detection algorithm on a Raspberry Pi 3 device and create the "scare pests away" part of our pest eliminator by playing a loud sound.
In this two-part series, you’ll learn how to turn a Raspberry Pi into a flexible and powerful Internet-of-Things device with AWS Greengrass v2, ultimately creating an off-grid remote-tasked data and image collection device.
In this article, we have a look at the details of the TrafficCV implementation and the various object detection models to use for detecting vehicles and calculating their speed.
In this we discuss improvements we can make to the software in terms of performance or accuracy. We also compare our homebrew open-source system to commercial vehicle speed detection systems.
In the first post in our series about Internet of Things (IoT) developer journey, we introduced the four stages of IoT development. This post explores the first stage.
In this article, we set up a development environment on Windows 10 for cross-platform computer vision and machine learning projects to run on our Pi device.
Raspberry Pi - the incredibly small computer of the size of credit card and cost less than $40 has gained popularity not only because of this but its capability for connecting to electronics and devices using the GIPO pins.
Windows IoT on Raspberry Pi doesn't yet have native support for a hardware real time clock. I've created a solution for getting Windows IoT to initialize its clock from RTC at bootup so that other solutions don't need to be modified to read from the RTC directly.
In this two-part series, you’ll learn how to turn a Raspberry Pi into a flexible and powerful Internet-of-Things device with AWS Greengrass v2, ultimately creating an off-grid remote-tasked data and image collection device.
This article series will show you how to build a reasonably accurate traffic speed detector using nothing but Deep Learning, and run it on an edge device like a Raspberry Pi.
In this article, we’ll wrap the face identification model in a simple Web API, create a client application on the Raspberry Pi, and run the client-server system.
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