Maker Perspective

For Beginners/ Aspiring Makers Arduino UNO is my recommendation, very straight forward and very easy to get started.

Tech Specifications

• Powerd by ATmega328P
• 14 Digital I/O Pin, which 6 provide PWM output
• 6 Analog Input Pins
• 16 MHz Clock
• 32 KB (ATmega328P) Flash Memory
• 2 KB SRAM
• 1KB EEPROM

The Arduino Uno is an open-source microcontroller board based on the Microchip ATmega328P microcontroller and developed by Arduino.cc. The board is equipped with sets of digital and analogue input/output pins that may be interfaced to various expansion boards and other circuits.

The main thing I loved Arduino is the community and peripherals That supported, also the Shields. these are boards that can be plugged on top of the Arduino PCB extending its capabilities. The different shields follow the same philosophy as the original toolkit: they are easy to mount and cheap to produce.

Another one is the IDE, The Arduino Integrated Development Environment is a cross-platform application that is written in functions from C and C++. It is used to write and upload programs to Arduino compatible boards, but also, with the help of 3rd party cores, other vendor development boards.

The Original Arduino Uno cost around $21, but you can also get clones even cheaper, but I recommend to buy original to support there mission and vision.

Website: https://www.arduino.cc/

There are some recommendations if you're into hackathons or quick prototype/POC.

ESP32/ESP8266

Others who have experience with the Arduino ecosystem, I suggest the Esperssif ESP8266 or ESP32 Low-cost WiFi and BLE* development platform, which is highly supported by the Arduino ecosystem. That means you can reuse many of the Arduino functions and libraries out of the box.

Particle Photon

The Photon is a $19 tiny Wi-Fi IoT device for creating connected projects and products for the Internet of Things. It's easy to use, it's powerful, and it's connected to the cloud. The board is powered by a Cypress Wi-Fi chip alongside a powerful STM32 ARM Cortex M3 microcontroller.

It's very easy to quickstart, also super easy to connect with particle cloud if you project involve Cloud and Connectivity.

Website: https://www.particle.io/

If you need more computing and processing power you can check the Raspberry Pi and BeagleBoard Single Board Computers

Raspberry Pi

The Raspberry Pi is a series of small single-board computers developed in the United Kingdom by the Raspberry Pi Foundation to promote the teaching of basic computer science in schools and in developing countries Since the aim is to teach kids about physical computing and computer science, the documentation and getting started is very straight forward and simple.

There are different models of Raspberry Pi SBC(Single Board Computer) and the latest one is Raspberry Pi 4 Model B which cost starts from $35. There is also lighter version called PiZero which cost around $5

Website: https://www.raspberrypi.org/

BeagleBoard

The BeagleBoard is a low-power open-source single-board computer produced by Texas Instruments in association with Digi-Key and Newark element14. The BeagleBoard was also designed with open source software development in mind and as a way of demonstrating the Texas Instrument's OMAP3530 system-on-a-chip.

If you are a beginner and want to get started the embedded Linux development, then you need to checkout beagleboard series single-board computer.

Website: https://beagleboard.org/

The list is not over yet, I'll share more thought on the thread as a followup.

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Sharing a tip that can be very useful when working with Raspberry pi GPIO, The GPIO Pinout is designed to be both a quick and interactive reference to the Raspberry Pi GPIO pins, plus a comprehensive guide to your Raspberry Pi's GPIO interfaces. It also includes dozens of pinouts for Raspberry Pi add-on boards, HATs and pHATs.

This Pinout.xyz gather useful information about the Raspberry Pi GPIO interface and add-on boards and invite board manufacturers to produce their own "overlay" files which describe which pins their Pi add-ons use. It's very handy when comes quick prototyping.

command

pinout

more details: https://pinout.xyz/

Are you Interested to Build Electronics Projects?

• Learn how to use setup raspberry pi
• Learn how to use raspberry pi GPIO
• Learn how to use the NodeRED platform
• Learn how to work with Linux command Line at a basic level

Join 2-Days Raspberry Pi Beginners Workshop on 28 and 29 August 2021.

Day 1

• Introduction to Raspberry Pi Computer
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• Raspberry Pi OS Tour
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Day 2

• Introduction to Physical Computing
• Understand GPIO and Its Functions
• Introduction to NodeRED Platform
• Write First NodeRED Application
• Manipulate GPIO and Blink LED
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• Possibilities.

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Raspberry Pi Zero 2 W uses the same Broadcom BCM2710A1 SoC die as the launch version of Raspberry Pi 3, with Arm cores slightly down-clocked to 1GHz, bundled into a single space-saving package alongside 512MB of LPDDR2 SDRAM.

Here are the highlights:

• Broadcom BCM2710A1, quad-core 64-bit SoC (Arm Cortex-A53 @ 1GHz)
• 512MB LPDDR2 SDRAM
• 2.4GHz IEEE 802.11b/g/n wireless LAN, * Bluetooth 4.2, BLE
• 1 × USB 2.0 interface with OTG
• HAT-compatible 40 pin I/O header footprint
• MicroSD card slot
• Mini HDMI port
• Composite video and reset pin solder points
• CSI-2 camera connector
• H.264, MPEG-4 decode (1080p30); H.264 encode (1080p30)
• OpenGL ES 1.1, 2.0 graphics

src: https://www.raspberrypi.com/news/new-raspberry-pi-zero-2-w-2/

In this blog, we are going to learn to host web pages on GitHub for free using GitHub Pages!🤩 . we will host a very simple web page like https://0xsalfar.github.io/ from scratch to complex one.

You can read the blog from here: https://blog.makergram.com/part1-host-website-on-github-for-free/

Hi @ZeeZa ,

You need to build a custom board with the ESP32 SoC and the MPU6050 IMU, right?

first, you need to select appropriate ESP32 SoC from the wide variety of model, after that using the datasheet find out the minimum configuration circuit for the SoC and add the MPU6050 module. that's all you need. the most tricky part is the minimum configuration circuit.

Fab Foundation has started a GitLab repository for the Fab Lab Network for #Covid-19 projects HERE. Please note that in order to edit the repository you need to have a fablabs.io or GitHub account. This repository includes links to many of the incredible projects and efforts being organized around the world, as well as projects of the Fab Lab Network in development. Use this as both a resource and as a central place to sync around projects, aligning with CBA, Fab Foundation and with other gurus from the fab network and beyond.

Hi @kowshik1729 ,

Yes, you can do Multi Thread in Raspberry Pi using python and note that different threads do not actually execute at the same time: they merely appear to.

We can easily implement simple threads using threading module. I'll show you some example of how actually it works.

Example :
I was trying to blink two LED's at the same time, for that I created a function that does the blink infinitely using while(True) .

import RPi.GPIO as GPIO
import time

Rled = 12
Bled = 6

GPIO.setmode(GPIO.BCM)

GPIO.setup(Rled,GPIO.OUT)
GPIO.setup(Bled,GPIO.OUT)


def blue():
        while True:
                print "LED BLUE is  ON"
                GPIO.output(Bled,GPIO.LOW)
                time.sleep(1)
                print "LED BLUE is  OFF"
                GPIO.output(Bled,GPIO.HIGH)
                time.sleep(1)
def red():
        while True:
                print "LED RED is  ON"
                GPIO.output(Rled,GPIO.LOW)
                time.sleep(1)
                print "LED RED is OFF"
                GPIO.output(Rled,GPIO.HIGH)
                time.sleep(1)

blue()
red()

Output :

Video : https://www.youtube.com/watch?v=AypMEQFpEWo&feature=youtu.be

since we are using the infinity loop for both methods, the blue() will not stop and red() will not invoke either.

In these scenarios, we can use multithread both function without waiting for another , let's try.

import RPi.GPIO as GPIO
import time
import threading



Rled = 12
Bled = 6

GPIO.setmode(GPIO.BCM)

GPIO.setup(Rled,GPIO.OUT)
GPIO.setup(Bled,GPIO.OUT)


def blue():
        while True:
                print "LED BLUE is  ON"
                GPIO.output(Bled,GPIO.LOW)
                time.sleep(1)
                print "LED BLUE is  OFF"
                GPIO.output(Bled,GPIO.HIGH)
                time.sleep(1)
def red():
        while True:
                print "LED RED is  ON"
                GPIO.output(Rled,GPIO.LOW)
                time.sleep(1)
                print "LED RED is OFF"
                GPIO.output(Rled,GPIO.HIGH)
                time.sleep(1)


t1 = threading.Thread(target=blue)
t2 = threading.Thread(target=red)


t1.start()
t2.start()

Output :

Video : https://www.youtube.com/watch?v=HMKYzgdNwd8&feature=youtu.be

with the help threading module we can simply run both methods at the same time (how we feel).

I think you got your answer. also, keep in mind to avoid race condition and deadlock while multithreading.

@kowshik1729 if you are more into ML take a look at the Jetson nano module, it's come with 4GB and 2GM Ram with 128-core NVIDIA Maxwell GPU and Quad-core ARM A57 CPU

more information: https://developer.nvidia.com/embedded/jetson-nano

Hi @binishmatheww , Welcome to Makergram.

I see you want to build a GPS Logger. For that, you can use any basic GPS module available in the market such as NEO-6M GPS Module or any from the list here.

After that, you need to choose a microcontroller, which can communicate with the GPS module read the GPS and translate the NMEA (data format for GPS packets) to a more usable format (you can do the post-processing in the phone too).

Then you can send the data to hone via BLE, for this you can use a BLE-supported microcontroller or use a standalone BLE co-processor. My suggestion is to go with esp32 as it has BLE support and other cores can be used for the GPS data acquisition. To do the post-processing and other things you can do with the mobile app.

You can split the project into different parts.

1. GPS Data Acquisition. (You can use Arduino IDE + ESP32/Pico W + GPS module of your choice)
2. GPS data translation (There will be Arduino lib which can make the translation easier)
3. Send Data to the mobile app via BLE (Arduino IDE + ESP32/Pico W + BLE LIB + Mobile application).

Hope you get an idea, please check and let us know. Happy building.

@jacob_jipson Glad to hear that.

@jacob_jipson said in Need Help: Error compiling for board AI Thinker ESP32-CAM.:

ESP-NN

Did you replace the NN folder in the lib as we do for XIAO esp32? and for the AI Thinker ESP32-CAM, you don't need to do that.

now, try to delete the library and re-add a new one and compile.

@jacob_jipson said in Need Help: Error compiling for board AI Thinker ESP32-CAM.:

exit status 1
Error compiling for board AI Thinker ESP32-CAM.

can you please share the full error log here and pleas share the screenshot of the Arduino IDE tools section.

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@fabme In this case, you need to upload a code XIAO, To fetch the IMU and print on serial. From there, use Edge Impulse CLI to forward the data from the serial to the Edge Impulse.

Hi @fabme , What kind of data are you trying to collect, and can you specify the sensor?

Thanks for the insights @kowshik1729 .

When I was checking the edgeImpulse memory profiler, it only mentioned 19.1K RAM and I don't have any additional application running along with the inference program. It looks like something is wrong in the compilation.

I will try to check the memory status as you mentioned and get back, Thanks

Recently, I got a chance to work work the SeeedStudio T1000 tracker and I made a tracker and data logger with it. It comes with a LoRa module to transmit the data and has an inbuilt temperature and light sensor. The device is very easy to set up and get-started and I can say the experience of switching from different LoRa network servers is very pleasant and straightforward, same for the integration. I chose Ubidots to visualise the data captured.

I also like the mobile application very much, it’s so easy to pair and change the tracker configuration on the go. I love it.

I will experiment more and share my insights here. let me know if you want to know any specific details about the SenseCAP T1000.

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Hi, I was trying to inference an audio KWS project on XIAO ESP-S3 with Sense HAT. The device was working fine on the previous audio project, only facing issues with this particular project. Thanks in Advance.

ERR: Failed to run classifier (-3)
Arena size is too small for all buffers. Needed 15280 but only 12160 was available.
AllocateTensors() failed
ERR: Failed to run classifier (-3)
Edge Impulse Inferencing Demo
Inferencing settings:
Interval: 0.062500 ms.
Frame size: 16000
Sample length: 1000 ms.
No. of classes: 2