Robots and Physical Computing

Posts

Moving Eyes with an Adafruit Adabox kit

One of the things I enjoy is a subscription to Adabox from Adafruit, receiving the box and very soon before that finding out what the main item is the box is a treat. The latest, at the time of writing, is AdaBox 13 with the MONSTER M4SK a set of small screens, based around 240x240 IPS TFT displays, acting as moving eyes in a mask, along with a couple of masks, speaker, microphone, lenses, etc and craft materials for decorating a mask - a full list can be found here . My goal was, to play with this, was to create a slightly creepy mask where the eyes move - a simple build but fun to do. The Adafruit quick start guide for this (available here ) provides all the instructions on setting it and downloading the different sets of eyes (that is really creepy to write). A set of different graphics files are already available for different sets of eyes; a couple of examples are shown below. One suggestion is when you download the files for the eyes ...

starting with NVIDA jetson nano

This is the third of a planned occasional series of posts on playing with some of the current AI specific boards for Intenet of Things (IoT). In the series, it is planned that will be some experiments with the Google Coral accelerator adapter and the Development Board; as well the NVIDIA Jetson Nano. In previous posts I started playing the Coral Accelerator adapter ki t and the Coral Development Board. This post looks a starting with the NVIDIA Jetson Nano Development Kit which like the Coral Development Board is a small computer designed for running combined embedded and neural network applications. The processing power comes from a quad-core 64-bit ARM CPU and a 128-core integrated NVIDIA GPU (for more details see here ) So before we all get spooked; getting going is relatively easy, basically, follow https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#intro . Following these instructions I would suggest if you are able to...

Coral Dev Board and Raspberry Pi

This is the second of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of Things (IoT). In the series, it is planned that some experiments with the Google Coral adapter and the Development Board; as well the NVIDIA Jetson Nano will be shown. In the previous post I started playing with the Coral Accelerator with a Raspberry Pi https://robotsandphysicalcomputing.blogspot.com/2019/08/coral-accelerator-on-raspberry-pi.html . The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range https://aiyprojects.withgoogle.com/edge-tpu/ and designed to work with TensorFlow Lite . In this post, the bigger sibling the Coral Development Board or Coral Dev Board is connected to a Raspberry Pi. The Coral Dev board is a single board Linux computer in its own right, running a derivative of Debian called Mendel. The Pi (in this case I used A Raspberry Pi 2 running Rasp...

Coral Accelerator on a Raspberry Pi

This is the first of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of Things (IoT). In the series, it is planned that some experiments with the Google Coral adapter and the Development Board; as well the NVIDIA Jetson Nano will be shown. Why bother? Basic reason is I love playing with AI and hardware - so it is kind of fun. Another reason is AI, IoT and e dge computing, are important and growing technologies, and I want to start getting my head around them a bit. In this post, I look at starting to use Coral Accelerator with a Raspberry Pi. The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range https://aiyprojects.withgoogle.com/edge-tpu/ and designed to work with TensorFlow Lite . Good place to start is Google's Get started with the USB Accelerator pretty much all you need to do to get going is in it, it also mentions Raspberry Pi. It makes a good ...

Produce a Microbit python neural network 2: Building a Physical Microbit Neural Network

This is second in a two-post series on building a neural network using microbits with micropython. In the first post python was used to produce a neural network without the microbits. In this post the network is as shown in figure 1 is developed. The figure below shows the arrangement of the connections to be built; pin 2 is the output of each neuron. The two micro:bits/neurons on the left of the picture taking in the two same inputs ; the output from these neurons are the two inputs to the output neuron on the right. figure 1 The micro:bit objects used in Figure 1 were produced using the micro:bit Fritzing diagram available at https://github.com/microbit-foundation/dev-docs/issues/36 thanks to David Whale ( @whalleygeek ) for this. The Inputs neurons Neuron 1: from microbit import * W=[-1,-1,1] while True: x1=pin0.read_digital() x2=pin1.read_digital() net = W[0]+W[1]*x1+W[2]*x2 ...

Produce a Microbit python neural network 1: Without the microbit initially

These two posts are really extensions of a set of post in response to a question from Carl Simmons ( @Activ8Thinking ) concerning building a micro:bit simple neuron. In those, the Microsoft MakeCode was used. In this two-post series is going to go through building neurones and neural networks in Python and by the end of the second post a python-based microbit neural network Post 1 (this one) A single neuron and a simple neuron network will be produced in Python are produced. Post 2 looks at producing a network of neurons, ie. neural network using the idea from post 1 but using a three microbits.; looking to solve the problem that a single neuron can't solve, making an Exclusive OR gate (XOR) 1. Overview and non-microbit neuron 1.1 Overview: The characteristics of the system will be: Inputs are going to be binary Weighted sum is bias+W1*input1+w2*input2 If weighted sum>=0 then the output is True (T on the LEDs) or '1' If weigh...