Saad  Kassam

Saad Kassam

1622706657

Arduino Nano RP2040 Connect - Arduino meets Raspberry Pi

Let’s get started with the new Arduino Nano RP2040 Connect, an Arduino board with a Raspberry Pi RP2040 MCU.

The highly-anticipated fusion between Arduino And Raspberry Pi has finally arrived!

The Arduino Nano RP2040 Connect board fuses a Raspberry Pi RP2040 MCU with an ESP32-based WiFi & Bluetooth Module, a MEMS Microphone, an IMU with Machine Learning capabilities, extra Flash memory, and a Cryptographic Coprocessor. All in the same form-factor and pinout as the original Arduino Nano.

This powerful little board brings the power of the new RP2040 microcontroller to the Arduino ecosystem. And with the addition of WiFi and Bluetooth, along with a number of advanced onboard peripherals, this little board is certain to find a myriad of uses.

Today we will take a first look at the Arduino Nano RP2040.

After examining the board’s many advanced features we will set up our Arduino IDE to work with our new microcontroller. Linux users will want to make note of a possible bug in the installation process, but don’t worry, I have a simple fix for that.

Once we have the IDE up and running and have tested out the board with the usual Blink sketch we will run a few Arduino-supplied experiments to get a feel for using some of the Nano RP2040’s advanced features.

The Arduino Nano RP2040 Connect has an onboard RGB LED, and in our first experiment we will create a WiFi Access Point with a web page that will allow us to control the Red, Green, and Blue segments. You can use this simple sketch as the basis for more advanced remote control applications.

Next, we will put the 6-axis IMU (Inertial Measurement Unit) to the test by exploiting one of its most unique features - built-in Machine Learning. We will use this to build a motion sensor that can determine whether we are stationary, walking, jogging, cycling, or driving. As I’m doing this in the confines of my workshop I’ll just have to shake the board to simulate walking and jogging!

And finally, we will use the onboard MEMS Microphone to control the blue segment of the RGB LED by clapping - similar to the old “Clapper” device from the 1980s!

Here is the Table of Contents for today’s video:

00:00 - Introduction
01:55 - Arduino Nano RP2040 Connect Intro
06:12 - Look at Nano RP2040
08:48 - Installing the Boards Manager
09:39 - Linux Post Install
12:58 - Blink Test
15:00 - Web Server AP Mode Demo
22:01 - IMU Machine Learning Core Demo
29:31 - Reading Microphone Data Demo (“Clapper”)
35:59 - Conclusion

Subscribe: https://www.youtube.com/c/Dronebotworkshop1/featured

#arduino #raspberry

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Buddha Community

Arduino Nano RP2040 Connect - Arduino meets Raspberry Pi
Saad  Kassam

Saad Kassam

1622706657

Arduino Nano RP2040 Connect - Arduino meets Raspberry Pi

Let’s get started with the new Arduino Nano RP2040 Connect, an Arduino board with a Raspberry Pi RP2040 MCU.

The highly-anticipated fusion between Arduino And Raspberry Pi has finally arrived!

The Arduino Nano RP2040 Connect board fuses a Raspberry Pi RP2040 MCU with an ESP32-based WiFi & Bluetooth Module, a MEMS Microphone, an IMU with Machine Learning capabilities, extra Flash memory, and a Cryptographic Coprocessor. All in the same form-factor and pinout as the original Arduino Nano.

This powerful little board brings the power of the new RP2040 microcontroller to the Arduino ecosystem. And with the addition of WiFi and Bluetooth, along with a number of advanced onboard peripherals, this little board is certain to find a myriad of uses.

Today we will take a first look at the Arduino Nano RP2040.

After examining the board’s many advanced features we will set up our Arduino IDE to work with our new microcontroller. Linux users will want to make note of a possible bug in the installation process, but don’t worry, I have a simple fix for that.

Once we have the IDE up and running and have tested out the board with the usual Blink sketch we will run a few Arduino-supplied experiments to get a feel for using some of the Nano RP2040’s advanced features.

The Arduino Nano RP2040 Connect has an onboard RGB LED, and in our first experiment we will create a WiFi Access Point with a web page that will allow us to control the Red, Green, and Blue segments. You can use this simple sketch as the basis for more advanced remote control applications.

Next, we will put the 6-axis IMU (Inertial Measurement Unit) to the test by exploiting one of its most unique features - built-in Machine Learning. We will use this to build a motion sensor that can determine whether we are stationary, walking, jogging, cycling, or driving. As I’m doing this in the confines of my workshop I’ll just have to shake the board to simulate walking and jogging!

And finally, we will use the onboard MEMS Microphone to control the blue segment of the RGB LED by clapping - similar to the old “Clapper” device from the 1980s!

Here is the Table of Contents for today’s video:

00:00 - Introduction
01:55 - Arduino Nano RP2040 Connect Intro
06:12 - Look at Nano RP2040
08:48 - Installing the Boards Manager
09:39 - Linux Post Install
12:58 - Blink Test
15:00 - Web Server AP Mode Demo
22:01 - IMU Machine Learning Core Demo
29:31 - Reading Microphone Data Demo (“Clapper”)
35:59 - Conclusion

Subscribe: https://www.youtube.com/c/Dronebotworkshop1/featured

#arduino #raspberry

Tools and Images to Build a Raspberry Pi n8n server

n8n-pi

Tools and Images to Build a Raspberry Pi n8n server

Introduction

The purpose of this project is to create a Raspberry Pi image preconfigured with n8n so that it runs out of the box.

What is n8n?

n8n is a no-code/low code environment used to connect and automate different systems and services. It is programmed using a series of connected nodes that receive, transform, and then transmit date from and to other nodes. Each node represents a service or system allowing these different entities to interact. All of this is done using a WebUI.

Why n8n-pi?

Whevever a new technology is released, two common barriers often prevent potential users from trying out the technology:

  1. System costs
  2. Installation & configuration challenges

The n8n-pi project eliminates these two roadblocks by preconfiguring a working system that runs on easily available, low cost hardware. For as little as $40 and a few minutes, they can have a full n8n system up and running.

Thanks!

This project would not be possible if it was not for the help of the following:

Documentation

All documentation for this project can be found at http://n8n-pi.tephlon.xyz.

Download Details:

Author: TephlonDude

GitHub: https://github.com/TephlonDude/n8n-pi

#pi #raspberry pi #raspberry #raspberry-pi

TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera

I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!

Prerequisites:

  • I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)

  • I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)

#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera

The Raspberry Pi 400 - A full computer in a keyboard!

The Raspberry Pi 400 has arrived in the studio, and in this video I’ll give it a review. I’ll show an unboxing of the Personal Computer Kit from Canakit, which is a great way to get started on the Pi 400. Then I’ll show off the hardware, as well as the out-of-box experience.

#raspberry pi #pi #raspberry-pi

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

In our previous posts in this series, we spoke at length about using PgBouncer  and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !

The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.

It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!

Using PgBouncer with Pgpool-II - Connection Pooling Diagram

PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II

CLICK TO TWEET

Performance Testing

While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.

Testing Conditions

All of the PostgreSQL benchmark tests were run under the following conditions:

  1. Initialized pgbench using a scale factor of 100.
  2. Disabled auto-vacuuming on the PostgreSQL instance to prevent interference.
  3. No other workload was working at the time.
  4. Used the default pgbench script to run the tests.
  5. Used default settings for both PgBouncer and Pgpool-II, except max_children*. All PostgreSQL limits were also set to their defaults.
  6. All tests ran as a single thread, on a single-CPU, 2-core machine, for a duration of 5 minutes.
  7. Forced pgbench to create a new connection for each transaction using the -C option. This emulates modern web application workloads and is the whole reason to use a pooler!

We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:

  • For PgBouncer, we installed it on the same box as the PostgreSQL server(s). This is the configuration we use in our managed PostgreSQL clusters. Since PgBouncer is a very light-weight process, installing it on the box has no impact on overall performance.
  • For Pgpool-II, we tested both when the Pgpool-II instance was installed on the same machine as PostgreSQL (on box column), and when it was installed on a different machine (off box column). As expected, the performance is much better when Pgpool-II is off the box as it doesn’t have to compete with the PostgreSQL server for resources.

Throughput Benchmark

Here are the transactions per second (TPS) results for each scenario across a range of number of clients:

#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling