embedded_sys_exercise.qmd

# Setup Nicla Vision {.unnumbered}

The [Arduino Nicla
Vision](https://docs.arduino.cc/hardware/nicla-vision) (sometimes called
*NiclaV*) is a development board that includes two processors that can
run tasks in parallel. It is part of a family of development boards with
the same form factor but designed for specific tasks, such as the [Nicla
Sense
ME](https://www.bosch-sensortec.com/software-tools/tools/arduino-nicla-sense-me/)
and the [Nicla
Voice](https://store-usa.arduino.cc/products/nicla-voice?_gl=1*l3abc6*_ga*MTQ3NzE4Mjk4Mi4xNjQwMDIwOTk5*_ga_NEXN8H46L5*MTY5NjM0Mzk1My4xMDIuMS4xNjk2MzQ0MjQ1LjAuMC4w).
The *Niclas* can efficiently run processes created with TensorFlow™
Lite. For example, one of the cores of the NiclaV computing a computer
vision algorithm on the fly (inference), while the other leads with
low-level operations like controlling a motor and communicating or
acting as a user interface.

> *The onboard wireless module allows the management of WiFi and
> Bluetooth Low Energy (BLE) connectivity simultaneously.*

![](images_2/media/image29.jpg){width="6.5in"
height="3.861111111111111in"}

## Two Parallel Cores

The central processor is the dual-core
[STM32H747,](https://content.arduino.cc/assets/Arduino-Portenta-H7_Datasheet_stm32h747xi.pdf?_gl=1*6quciu*_ga*MTQ3NzE4Mjk4Mi4xNjQwMDIwOTk5*_ga_NEXN8H46L5*MTY0NzQ0NTg1My4xMS4xLjE2NDc0NDYzMzkuMA..)
including a Cortex® M7 at 480 MHz and a Cortex® M4 at 240 MHz. The two
cores communicate via a Remote Procedure Call mechanism that seamlessly
allows calling functions on the other processor. Both processors share
all the on-chip peripherals and can run:

-   Arduino sketches on top of the Arm® Mbed™ OS

-   Native Mbed™ applications

-   MicroPython / JavaScript via an interpreter

-   TensorFlow™ Lite

![](images_2/media/image22.jpg){width="5.78125in"
height="5.78125in"}

## Memory

Memory is crucial for embedded machine learning projects. The NiclaV
board can host up to 16 MB of QSPI Flash for storage. However, it is
essential to consider that the MCU SRAM is the one to be used with
machine learning inferences; the STM32H747 is only 1MB, shared by both
processors. This MCU also has incorporated 2MB of FLASH, mainly for code
storage.

## Sensors

-   **Camera**: A GC2145 2 MP Color CMOS Camera.

-   **Microphone**: A
    > [MP34DT05,](https://content.arduino.cc/assets/Nano_BLE_Sense_mp34dt05-a.pdf?_gl=1*12fxus9*_ga*MTQ3NzE4Mjk4Mi4xNjQwMDIwOTk5*_ga_NEXN8H46L5*MTY0NzQ0NTg1My4xMS4xLjE2NDc0NDc3NzMuMA..)
    > an ultra-compact, low-power, omnidirectional, digital MEMS
    > microphone built with a capacitive sensing element and an IC
    > interface.

-   **6-Axis IMU**: 3D gyroscope and 3D accelerometer data from the
    > LSM6DSOX 6-axis IMU.

-   **Time of Flight Sensor**: The VL53L1CBV0FY Time-of-Flight sensor
    > adds accurate and low power-ranging capabilities to the Nicla
    > Vision. The invisible near-infrared VCSEL laser (including the
    > analog driver) is encapsulated with receiving optics in an
    > all-in-one small module below the camera.

### **HW Installation (Arduino IDE)**

Start connecting the board (USB-C) to your computer :

![](images_2/media/image14.jpg){width="6.5in"
height="3.0833333333333335in"}

Install the Mbed OS core for Nicla boards in the Arduino IDE. Having the
IDE open, navigate to Tools \> Board \> Board Manager, look for Arduino
Nicla Vision on the search window, and install the board.

![](images_2/media/image2.jpg){width="6.5in"
height="2.7083333333333335in"}

Next, go to Tools \> Board \> Arduino Mbed OS Nicla Boards and select
Arduino Nicla Vision. Having your board connected to the USB, you should
see the Nicla on Port and select it.

> *Open the Blink sketch on Examples/Basic and run it using the IDE
> Upload button. You should see the Built-in LED (green RGB) blinking,
> which means the Nicla board is correctly installed and functional!*

## Testing the Microphone

On Arduino IDE, go to Examples \> PDM \> PDMSerialPlotter, open and run
the sketch. Open the Plotter and see the audio representation from the
microphone:

![](images_2/media/image9.png){width="6.5in"
height="4.361111111111111in"}

> *Vary the frequency of the sound you generate and confirm that the mic
> is working correctly.*

## Testing the IMU

Before testing the IMU, it will be necessary to install the LSM6DSOX
library. For that, go to Library Manager and look for LSM6DSOX. Install
the library provided by Arduino:

![](images_2/media/image19.jpg){width="6.5in"
height="2.4027777777777777in"}

Next, go to Examples \> Arduino_LSM6DSOX \> SimpleAccelerometer and run
the accelerometer test (you can also run Gyro and board temperature):

![](images_2/media/image28.png){width="6.5in"
height="4.361111111111111in"}

### **Testing the ToF (Time of Flight) Sensor**

As we did with IMU, installing the ToF library, the VL53L1X is
necessary. For that, go to Library Manager and look for VL53L1X. Install
the library provided by Pololu:

![](images_2/media/image15.jpg){width="6.5in"
height="2.4583333333333335in"}

Next, run the sketch
[proximity_detection.ino](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/distance_image_meter.py):

![](images_2/media/image12.png){width="4.947916666666667in"
height="4.635416666666667in"}

On the Serial Monitor, you will see the distance from the camera and an
object in front of it (max of 4m).

![](images_2/media/image13.jpg){width="6.5in"
height="4.847222222222222in"}

## Testing the Camera

We can also test the camera using, for example, the code provided on
Examples \> Camera \> CameraCaptureRawBytes. We can not see the image
directly, but it is possible to get the raw image data generated by the
camera.

Anyway, the best test with the camera is to see a live image. For that,
we will use another IDE, the OpenMV.

## Installing the OpenMV IDE

OpenMV IDE is the premier integrated development environment for use
with OpenMV Cameras and the one on the Portenta. It features a powerful
text editor, debug terminal, and frame buffer viewer with a histogram
display. We will use MicroPython to program the camera.

Go to the [OpenMV IDE page](https://openmv.io/pages/download), download
the correct version for your Operating System, and follow the
instructions for its installation on your computer.

![](images_2/media/image21.png){width="6.5in"
height="4.791666666666667in"}

The IDE should open, defaulting the helloworld_1.py code on its Code
Area. If not, you can open it from Files \> Examples \> HelloWord \>
helloword.py

![](images_2/media/image7.png){width="6.5in"
height="4.444444444444445in"}

Any messages sent through a serial connection (using print() or error
messages) will be displayed on the **Serial Terminal** during run time.
The image captured by a camera will be displayed in the **Camera
Viewer** Area (or Frame Buffer) and in the Histogram area, immediately
below the Camera Viewer.

OpenMV IDE is the premier integrated development environment with OpenMV
Cameras and the Arduino Pro boards. It features a powerful text editor,
debug terminal, and frame buffer viewer with a histogram display. We
will use MicroPython to program the Nicla Vision.

> *Before connecting the Nicla to the OpenMV IDE, ensure you have the
> latest bootloader version. To that, go to your Arduino IDE, select the
> Nicla board, and open the sketch on Examples \> STM_32H747_System
> STM_32H747_updateBootloader. Upload the code to your board. The Serial
> Monitor will guide you.*

After updating the bootloader, put the Nicla Vision in bootloader mode
by double-pressing the reset button on the board. The built-in green LED
will start fading in and out. Now return to the OpenMV IDE and click on
the connect icon (Left ToolBar):

![](images_2/media/image23.jpg){width="4.010416666666667in"
height="1.0520833333333333in"}

A pop-up will tell you that a board in DFU mode was detected and ask you
how you would like to proceed. First, select \"Install the latest
release firmware.\" This action will install the latest OpenMV firmware
on the Nicla Vision.

![](images_2/media/image10.png){width="6.5in"
height="2.6805555555555554in"}

You can leave the option of erasing the internal file system unselected
and click \[OK\].

Nicla\'s green LED will start flashing while the OpenMV firmware is
uploaded to the board, and a terminal window will then open, showing the
flashing progress.

![](images_2/media/image5.png){width="4.854166666666667in"
height="3.5416666666666665in"}

Wait until the green LED stops flashing and fading. When the process
ends, you will see a message saying, \"DFU firmware update complete!\".
Press \[OK\].

![](images_2/media/image1.png){width="3.875in"
height="5.708333333333333in"}

A green play button appears when the Nicla Vison connects to the Tool
Bar.

![](images_2/media/image18.jpg){width="4.791666666666667in"
height="1.4791666666666667in"}

Also, note that a drive named "NO NAME" will appear on your computer.:

![](images_2/media/image3.png){width="6.447916666666667in"
height="2.4166666666666665in"}

Every time you press the \[RESET\] button on the board, it automatically
executes the main.py script stored on it. You can load the
[main.py](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/main.py)
code on the IDE (File \> Open File\...).

![](images_2/media/image16.png){width="4.239583333333333in"
height="3.8229166666666665in"}

> *This code is the \"Blink\" code, confirming that the HW is OK.*

For testing the camera, let\'s run helloword_1.py. For that, select the
script on File \> Examples \> HelloWorld \> helloword.py,

When clicking the green play button, the MicroPython script
(hellowolrd.py) on the Code Area will be uploaded and run on the Nicla
Vision. On-Camera Viewer, you will start to see the video streaming. The
Serial Monitor will show us the FPS (Frames per second), which should be
around 14fps.

![](images_2/media/image6.png){width="6.5in"
height="3.9722222222222223in"}

Let\'s go through the [helloworld.py](http://helloworld.py/) script:

```python
# Hello World Example 2
#
# Welcome to the OpenMV IDE! Click on the green run arrow button below to run the script!

import sensor, image, time

sensor.reset()                      # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA)   # Set frame size to QVGA (320x240)
sensor.skip_frames(time = 2000)     # Wait for settings take effect.
clock = time.clock()                # Create a clock object to track the FPS.

while(True):
    clock.tick()                    # Update the FPS clock.
    img = sensor.snapshot()         # Take a picture and return the image.
    print(clock.fps())
```


In GitHub, you can find the Python scripts used here.

The code can be split into two parts:

-   **Setup**: Where the libraries are imported and initialized, and the
    > variables are defined and initiated.

-   **Loop**: (while loop) part of the code that runs continually. The
    > image (img variable) is captured (a frame). Each of those frames
    > can be used for inference in Machine Learning Applications.

To interrupt the program execution, press the red \[X\] button.

> *Note: OpenMV Cam runs about half as fast when connected to the IDE.
> The FPS should increase once disconnected.*

In [[the GitHub, You can find other Python
scripts]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Micropython).
Try to test the onboard sensors.

## Connecting the Nicla Vision to Edge Impulse Studio

We will use the Edge Impulse Studio later in other exercises. [Edge
Impulse I](https://www.edgeimpulse.com/)s a leading development platform
for machine learning on edge devices.

Edge Impulse officially supports the Nicla Vision. So, for starting,
please create a new project on the Studio and connect the Nicla to it.
For that, follow the steps:

-   Download the [last EI
    > Firmware](https://cdn.edgeimpulse.com/firmware/arduino-nicla-vision.zip)
    > and unzip it.

-   Open the zip file on your computer and select the uploader related
    > to your OS:

![](images_2/media/image17.png){width="4.416666666666667in"
height="1.5520833333333333in"}

-   Put the Nicla-Vision on Boot Mode, pressing the reset button twice.

-   Execute the specific batch code for your OS for uploading the binary
    > (arduino-nicla-vision.bin) to your board.

Go to your project on the Studio, and on the Data Acquisition tab,
select WebUSB (1). A window will appear; choose the option that shows
that the Nicla is pared (2) and press \[Connect\] (3).

![](images_2/media/image27.png){width="6.5in"
height="4.319444444444445in"}

In the Collect Data section on the Data Acquisition tab, you can choose
what sensor data you will pick.

![](images_2/media/image25.png){width="6.5in"
height="4.319444444444445in"}

For example. IMU data:

![](images_2/media/image8.png){width="6.5in"
height="4.319444444444445in"}

Or Image:

![](images_2/media/image4.png){width="6.5in"
height="4.319444444444445in"}

And so on. You can also test an external sensor connected to the Nicla
ADC (pin 0) and the other onboard sensors, such as the microphone and
the ToF.

### **Expanding the Nicla Vision Board (optional)**

A last item to be explored is that sometimes, during prototyping, it is
essential to experiment with external sensors and devices, and an
excellent expansion to the Nicla is the [Arduino MKR Connector Carrier
(Grove
compatible)](https://store-usa.arduino.cc/products/arduino-mkr-connector-carrier-grove-compatible).

The shield has 14 Grove connectors: five single analog inputs, one
single analog input, five single digital I/Os, one double digital I/O,
one I2C, and one UART. All connectors are 5V compatible.

> *Note that besides all 17 Nicla Vision pins that will be connected to
> the Shield Groves, some Grove connections are disconnected.*

![](images_2/media/image20.jpg){width="6.5in"
height="4.875in"}

This shield is MKR compatible and can be used with the Nicla Vision and
the Portenta.

![](images_2/media/image26.jpg){width="4.34375in"
height="5.78125in"}

For example, suppose that on a TinyML project, you want to send
inference results using a LoRaWan device and add information about local
luminosity. Besides, with offline operations, a local low-power display
as an OLED display is advised. This setup can be seen here:

![](images_2/media/image11.jpg){width="6.5in"
height="4.708333333333333in"}

The [Grove Light
Sensor](https://wiki.seeedstudio.com/Grove-Light_Sensor/) would be
connected to one of the single Analog pins (A0/PC4), the [LoRaWan
device](https://wiki.seeedstudio.com/Grove_LoRa_E5_New_Version/) to the
UART, and the [OLED](https://arduino.cl/producto/display-oled-grove/) to
the I2C connector.

The Nicla Pins 3 (Tx) and 4 (Rx) are connected with the Shield Serial
connector. The UART communication is used with the LoRaWan device. Here
is a simple code to use the UART.:

```python
# UART Test - By: marcelo_rovai - Sat Sep 23 2023

import time
from pyb import UART
from pyb import LED

redLED = LED(1) # built-in red LED

# Init UART object.
# Nicla Vision's UART (TX/RX pins) is on "LP1"
uart = UART("LP1", 9600)

while(True):
    uart.write("Hello World!\r\n")
    redLED.toggle()
    time.sleep_ms(1000)

```

To verify if the UART is working, you should, for example, connect
another device as an [Arduino
UNO](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Arduino-IDE/teste_uart_UNO/teste_uart_UNO.ino),
displaying the Hello Word.

![](images_2/media/image24.gif){width="2.8125in"
height="3.75in"}

Here is a Hello World code to be used with the I2C OLED. The MicroPython
SSD1306 OLED driver (ssd1306.py), created by Adafruit, should also be
uploaded to the Nicla (the
[[ssd1306.py]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/ssd1306.py)
can be found in GitHub).


```python
# Nicla_OLED_Hello_World - By: marcelo_rovai - Sat Sep 30 2023

#Save on device: MicroPython SSD1306 OLED driver, I2C and SPI interfaces created by Adafruit
import ssd1306

from machine import I2C
i2c = I2C(1)

oled_width = 128
oled_height = 64
oled = ssd1306.SSD1306_I2C(oled_width, oled_height, i2c)

oled.text('Hello, World', 10, 10)
oled.show()
```

Finally, here is a simple script to read the ADC value on pin \"PC4\"
(Nicla pin A0):
```python

# Light Sensor (A0) - By: marcelo_rovai - Wed Oct 4 2023

import pyb
from time import sleep

adc = pyb.ADC(pyb.Pin("PC4"))     # create an analog object from a pin
val = adc.read()                  # read an analog value

while (True):

    val = adc.read()  
    print ("Light={}".format (val))
    sleep (1)
```

The ADC can be used for other valuable sensors, such as
[Temperature](https://wiki.seeedstudio.com/Grove-Temperature_Sensor_V1.2/).

> *Note that the above scripts ([[downloaded from
> Github]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main/Micropython))
> only introduce how to connect external devices with the Nicla Vision
> board using MicroPython.*

## Conclusion

The Arduino Nicla Vision is an excellent *tiny device* for industrial
and professional uses! However, it is powerful, trustworthy, low power,
and has suitable sensors for the most common embedded machine learning
applications such as vision, movement, sensor fusion, and sound.

> *On the* *[GitHub
> repository,](https://github.com/Mjrovai/Arduino_Nicla_Vision/tree/main)
> you will find the last version of all the codes used or commented on
> in this exercise.*