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+4 Embedded AI
+
+# Exercise - Image Classification
+
+### **Introduction**
+
+As we initiate our studies into embedded machine learning or tinyML,
+it\'s impossible to overlook the transformative impact of Computer
+Vision (CV) and Artificial Intelligence (AI) in our lives. These two
+intertwined disciplines redefine what machines can perceive and
+accomplish, from autonomous vehicles and robotics to healthcare and
+surveillance.
+
+More and more, we are facing an artificial intelligence (AI) revolution
+where, as stated by Gartner, **Edge AI** has a very high impact
+potential, and **it is for now**!
+
+![](images_4/media/image2.jpg){width="4.729166666666667in"
+height="4.895833333333333in"}
+
+In the \"bull-eye\" of emerging technologies, radar is the *Edge
+Computer Vision*, and when we talk about Machine Learning (ML) applied
+to vision, the first thing that comes to mind is **Image
+Classification**, a kind of ML \"Hello World\"!
+
+This exercise will explore a computer vision project utilizing
+Convolutional Neural Networks (CNNs) for real-time image classification.
+Leveraging TensorFlow\'s robust ecosystem, we\'ll implement a
+pre-trained MobileNet model and adapt it for edge deployment. The focus
+will be optimizing the model to run efficiently on resource-constrained
+hardware without sacrificing accuracy.
+
+We\'ll employ techniques like quantization and pruning to reduce the
+computational load. By the end of this tutorial, you\'ll have a working
+prototype capable of classifying images in real time, all running on a
+low-power embedded system based on the Arduino Nicla Vision board.
+
+### **Computer Vision**
+
+At its core, computer vision aims to enable machines to interpret and
+make decisions based on visual data from the world---essentially
+mimicking the capability of the human optical system. Conversely, AI is
+a broader field encompassing machine learning, natural language
+processing, and robotics, among other technologies. When you bring AI
+algorithms into computer vision projects, you supercharge the system\'s
+ability to understand, interpret, and react to visual stimuli.
+
+When discussing Computer Vision projects applied to embedded devices,
+the most common applications that come to mind are *Image
+Classification* and *Object Detection*.
+
+![image.png](images_4/media/image15.jpg){width="6.5in"
+height="2.8333333333333335in"}
+
+Both models can be implemented on tiny devices like the Arduino Nicla
+Vision and used on real projects. Let\'s start with the first one.
+
+### **Image Classification Project**
+
+The first step in any ML project is to define our goal. In this case, it
+is to detect and classify two specific objects present in one image. For
+this project, we will use two small toys: a *robot* and a small
+Brazilian parrot (named *Periquito*). Also, we will collect images of a
+*background* where those two objects are absent.
+
+![image.png](images_4/media/image36.jpg){width="6.5in"
+height="3.638888888888889in"}
+
+### **Data Collection**
+
+Once you have defined your Machine Learning project goal, the next and
+most crucial step is the dataset collection. You can use the Edge
+Impulse Studio, the OpenMV IDE we installed, or even your phone for the
+image capture. Here, we will use the OpenMV IDE for that.
+
+**Collecting Dataset with OpenMV IDE**
+
+First, create in your computer a folder where your data will be saved,
+for example, \"data.\" Next, on the OpenMV IDE, go to Tools \> Dataset
+Editor and select New Dataset to start the dataset collection:
+
+![image.png](images_4/media/image29.png){width="6.291666666666667in"
+height="4.010416666666667in"}
+
+The IDE will ask you to open the file where your data will be saved and
+choose the \"data\" folder that was created. Note that new icons will
+appear on the Left panel.
+
+![image.png](images_4/media/image46.png){width="0.9583333333333334in"
+height="1.5208333333333333in"}
+
+Using the upper icon (1), enter with the first class name, for example,
+\"periquito\":
+
+![image.png](images_4/media/image22.png){width="3.25in"
+height="2.65625in"}
+
+Run the dataset_capture_script.py, and clicking on the bottom icon (2),
+will start capturing images:
+
+![image.png](images_4/media/image43.png){width="6.5in"
+height="4.041666666666667in"}
+
+Repeat the same procedure with the other classes
+
+![image.png](images_4/media/image6.jpg){width="6.5in"
+height="3.0972222222222223in"}
+
+> *We suggest around 60 images from each category. Try to capture
+> different angles, backgrounds, and light conditions.*
+
+The stored images use a QVGA frame size 320x240 and RGB565 (color pixel
+format).
+
+After capturing your dataset, close the Dataset Editor Tool on the Tools
+\> Dataset Editor.
+
+On your computer, you will end with a dataset that contains three
+classes: periquito, robot, and background.
+
+![image.png](images_4/media/image20.png){width="6.5in"
+height="2.2083333333333335in"}
+
+You should return to Edge Impulse Studio and upload the dataset to your
+project.
+
+### **Training the model with Edge Impulse Studio**
+
+We will use the Edge Impulse Studio for training our model. Enter your
+account credentials at Edge Impulse and create a new project:
+
+![image.png](images_4/media/image45.png){width="6.5in"
+height="4.263888888888889in"}
+
+> *Here, you can clone a similar project:*
+> *[NICLA-Vision_Image_Classification](https://studio.edgeimpulse.com/public/273858/latest).*
+
+### **Dataset**
+
+Using the EI Studio (or *Studio*), we will pass over four main steps to
+have our model ready for use on the Nicla Vision board: Dataset,
+Impulse, Tests, and Deploy (on the Edge Device, in this case, the
+NiclaV).
+
+![image.png](images_4/media/image41.jpg){width="6.5in"
+height="4.194444444444445in"}
+
+Regarding the Dataset, it is essential to point out that our Original
+Dataset, captured with the OpenMV IDE, will be split into three parts:
+Training, Validation, and Test. The Test Set will be divided from the
+beginning and left a part to be used only in the Test phase after
+training. The Validation Set will be used during training.
+
+![image.png](images_4/media/image7.jpg){width="6.5in"
+height="4.763888888888889in"}
+
+On Studio, go to the Data acquisition tab, and on the UPLOAD DATA
+section, upload from your computer the files from chosen categories:
+
+![image.png](images_4/media/image39.png){width="6.5in"
+height="4.263888888888889in"}
+
+Left to the Studio to automatically split the original dataset into
+training and test and choose the label related to that specific data:
+
+![image.png](images_4/media/image30.png){width="6.5in"
+height="4.263888888888889in"}
+
+Repeat the procedure for all three classes. At the end, you should see
+your \"raw data in the Studio:
+
+![image.png](images_4/media/image11.png){width="6.5in"
+height="4.263888888888889in"}
+
+The Studio allows you to explore your data, showing a complete view of
+all the data in your project. You can clear, inspect, or change labels
+by clicking on individual data items. In our case, a simple project, the
+data seems OK.
+
+![image.png](images_4/media/image44.png){width="6.5in"
+height="4.263888888888889in"}
+
+### **The Impulse Design**
+
+In this phase, we should define how to:
+
+-   Pre-process our data, which consists of resizing the individual
+    > images and determining the color depth to use (RGB or Grayscale)
+    > and
+
+-   Design a Model that will be \"Transfer Learning (Images)\" to
+    > fine-tune a pre-trained MobileNet V2 image classification model on
+    > our data. This method performs well even with relatively small
+    > image datasets (around 150 images in our case).
+
+![image.png](images_4/media/image23.jpg){width="6.5in"
+height="4.0in"}
+
+Transfer Learning with MobileNet offers a streamlined approach to model
+training, which is especially beneficial for resource-constrained
+environments and projects with limited labeled data. MobileNet, known
+for its lightweight architecture, is a pre-trained model that has
+already learned valuable features from a large dataset (ImageNet).
+
+![image.png](images_4/media/image9.jpg){width="6.5in"
+height="1.9305555555555556in"}
+
+By leveraging these learned features, you can train a new model for your
+specific task with fewer data and computational resources yet achieve
+competitive accuracy.
+
+![image.png](images_4/media/image32.jpg){width="6.5in"
+height="2.3055555555555554in"}
+
+This approach significantly reduces training time and computational
+cost, making it ideal for quick prototyping and deployment on embedded
+devices where efficiency is paramount.
+
+Go to the Impulse Design Tab and create the *impulse*, defining an image
+size of 96x96 and squashing them (squared form, without crop). Select
+Image and Transfer Learning blocks. Save the Impulse.
+
+![image.png](images_4/media/image16.png){width="6.5in"
+height="4.263888888888889in"}
+
+### **Image Pre-Processing**
+
+All input QVGA/RGB565 images will be converted to 27,640 features
+(96x96x3).
+
+![image.png](images_4/media/image17.png){width="6.5in"
+height="4.319444444444445in"}
+
+Press \[Save parameters\] and Generate all features:
+
+![image.png](images_4/media/image5.png){width="6.5in"
+height="4.263888888888889in"}
+
+### **Model Design**
+
+In 2007, Google introduced
+[[MobileNetV1]{.underline}](https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html),
+a family of general-purpose computer vision neural networks designed
+with mobile devices in mind to support classification, detection, and
+more. MobileNets are small, low-latency, low-power models parameterized
+to meet the resource constraints of various use cases. in 2018, Google
+launched [MobileNetV2: Inverted Residuals and Linear
+Bottlenecks](https://arxiv.org/abs/1801.04381).
+
+MobileNet V1 and MobileNet V2 aim for mobile efficiency and embedded
+vision applications but differ in architectural complexity and
+performance. While both use depthwise separable convolutions to reduce
+the computational cost, MobileNet V2 introduces Inverted Residual Blocks
+and Linear Bottlenecks to enhance performance. These new features allow
+V2 to capture more complex features using fewer parameters, making it
+computationally more efficient and generally more accurate than its
+predecessor. Additionally, V2 employs a non-linear activation in the
+intermediate expansion layer. Still, it uses a linear activation for the
+bottleneck layer, a design choice found to preserve important
+information through the network better. MobileNet V2 offers a more
+optimized architecture for higher accuracy and efficiency and will be
+used in this project.
+
+Although the base MobileNet architecture is already tiny and has low
+latency, many times, a specific use case or application may require the
+model to be smaller and faster. MobileNets introduces a straightforward
+parameter α (alpha) called width multiplier to construct these smaller,
+less computationally expensive models. The role of the width multiplier
+α is to thin a network uniformly at each layer.
+
+Edge Impulse Studio has available MobileNetV1 (96x96 images) and V2
+(96x96 and 160x160 images), with several different **α** values (from
+0.05 to 1.0). For example, you will get the highest accuracy with V2,
+160x160 images, and α=1.0. Of course, there is a trade-off. The higher
+the accuracy, the more memory (around 1.3M RAM and 2.6M ROM) will be
+needed to run the model, implying more latency. The smaller footprint
+will be obtained at another extreme with MobileNetV1 and α=0.10 (around
+53.2K RAM and 101K ROM).
+
+![image.png](images_4/media/image27.jpg){width="6.5in"
+height="3.5277777777777777in"}
+
+For this project, we will use **MobileNetV2 96x96 0.1**, which estimates
+a memory cost of 265.3 KB in RAM. This model should be OK for the Nicla
+Vision with 1MB of SRAM. On the Transfer Learning Tab, select this
+model:
+
+![image.png](images_4/media/image24.png){width="6.5in"
+height="4.263888888888889in"}
+
+Another necessary technique to be used with Deep Learning is **Data
+Augmentation**. Data augmentation is a method that can help improve the
+accuracy of machine learning models, creating additional artificial
+data. A data augmentation system makes small, random changes to your
+training data during the training process (such as flipping, cropping,
+or rotating the images).
+
+Under the rood, here you can see how Edge Impulse implements a data
+Augmentation policy on your data:
+
+  -----------------------------------------------------------------------
+  \# Implements the data augmentation policy\
+  def augment_image(image, label):\
+  \# Flips the image randomly\
+  image = tf.image.random_flip_left_right(image)\
+  \
+  \# Increase the image size, then randomly crop it down to\
+  \# the original dimensions\
+  resize_factor = random.uniform(1, 1.2)\
+  new_height = math.floor(resize_factor \* INPUT_SHAPE\[0\])\
+  new_width = math.floor(resize_factor \* INPUT_SHAPE\[1\])\
+  image = tf.image.resize_with_crop_or_pad(image, new_height, new_width)\
+  image = tf.image.random_crop(image, size=INPUT_SHAPE)\
+  \
+  \# Vary the brightness of the image\
+  image = tf.image.random_brightness(image, max_delta=0.2)\
+  \
+  return image, label
+  -----------------------------------------------------------------------
+
+  -----------------------------------------------------------------------
+
+Exposure to these variations during training can help prevent your model
+from taking shortcuts by \"memorizing\" superficial clues in your
+training data, meaning it may better reflect the deep underlying
+patterns in your dataset.
+
+The final layer of our model will have 12 neurons with a 15% dropout for
+overfitting prevention. Here is the Training result:
+
+![image.png](images_4/media/image31.jpg){width="6.5in"
+height="3.5in"}
+
+The result is excellent, with 77ms of latency, which should result in
+13fps (frames per second) during inference.
+
+### **Model Testing**
+
+![image.png](images_4/media/image10.jpg){width="6.5in"
+height="3.8472222222222223in"}
+
+Now, you should take the data put apart at the start of the project and
+run the trained model having them as input:
+
+![image.png](images_4/media/image34.png){width="3.1041666666666665in"
+height="1.7083333333333333in"}
+
+The result was, again, excellent.
+
+![image.png](images_4/media/image12.png){width="6.5in"
+height="4.263888888888889in"}
+
+### **Deploying the model**
+
+At this point, we can deploy the trained model as.tflite and use the
+OpenMV IDE to run it using MicroPython, or we can deploy it as a C/C++
+or an Arduino library.
+
+![image.png](images_4/media/image28.jpg){width="6.5in"
+height="3.763888888888889in"}
+
+**Arduino Library**
+
+First, Let\'s deploy it as an Arduino Library:
+
+![image.png](images_4/media/image48.png){width="6.5in"
+height="4.263888888888889in"}
+
+You should install the library as.zip on the Arduino IDE and run the
+sketch nicla_vision_camera.ino available in Examples under your library
+name.
+
+> *Note that Arduino Nicla Vision has, by default, 512KB of RAM
+> allocated for the M7 core and an additional 244KB on the M4 address
+> space. In the code, this allocation was changed to 288 kB to guarantee
+> that the model will run on the device
+> (malloc_addblock((void\*)0x30000000, 288 \* 1024);).*
+
+The result was good, with 86ms of measured latency.
+
+![image.png](images_4/media/image25.jpg){width="6.5in"
+height="3.4444444444444446in"}
+
+Here is a short video showing the inference results:
+[[https://youtu.be/bZPZZJblU-o]{.underline}](https://youtu.be/bZPZZJblU-o)
+
+**OpenMV**
+
+It is possible to deploy the trained model to be used with OpenMV in two
+ways: as a library and as a firmware.
+
+Three files are generated as a library: the.tflite model, a list with
+the labels, and a simple MicroPython script that can make inferences
+using the model.
+
+![image.png](images_4/media/image26.png){width="6.5in"
+height="1.0in"}
+
+Running this model as a.tflite directly in the Nicla was impossible. So,
+we can sacrifice the accuracy using a smaller model or deploy the model
+as an OpenMV Firmware (FW). As an FW, the Edge Impulse Studio generates
+optimized models, libraries, and frameworks needed to make the
+inference. Let\'s explore this last one.
+
+Select OpenMV Firmware on the Deploy Tab and press \[Build\].
+
+![image.png](images_4/media/image3.png){width="6.5in"
+height="4.263888888888889in"}
+
+On your computer, you will find a ZIP file. Open it:
+
+![Pasted Graphic
+64.png](images_4/media/image33.png){width="6.5in"
+height="2.625in"}
+
+Use the Bootloader tool on the OpenMV IDE to load the FW on your board:
+
+![Pasted Graphic
+63.png](images_4/media/image35.jpg){width="6.5in"
+height="3.625in"}
+
+Select the appropriate file (.bin for Nicla-Vision):
+
+![Pasted Graphic
+65.png](images_4/media/image8.png){width="6.5in"
+height="1.9722222222222223in"}
+
+After the download is finished, press OK:
+
+![DFU firmware update
+complete!.png](images_4/media/image40.png){width="3.875in"
+height="5.708333333333333in"}
+
+If a message says that the FW is outdated, DO NOT UPGRADE. Select
+\[NO\].
+
+![image.png](images_4/media/image42.png){width="4.572916666666667in"
+height="2.875in"}
+
+Now, open the script **ei_image_classification.py** that was downloaded
+from the Studio and the.bin file for the Nicla.
+
+![image.png](images_4/media/image14.png){width="6.5in"
+height="4.0in"}
+
+And run it. Pointing the camera to the objects we want to classify, the
+inference result will be displayed on the Serial Terminal.
+
+![image.png](images_4/media/image37.png){width="6.5in"
+height="3.736111111111111in"}
+
+**Changing Code to add labels:**
+
+The code provided by Edge Impulse can be modified so that we can see,
+for test reasons, the inference result directly on the image displayed
+on the OpenMV IDE.
+
+[[Upload the code from
+GitHub,]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification.py)
+or modify it as below:
+
+  -----------------------------------------------------------------------
+  \# Marcelo Rovai - NICLA Vision - Image Classification\
+  \# Adapted from Edge Impulse - OpenMV Image Classification Example\
+  \# \@24Aug23\
+  \
+  import sensor, image, time, os, tf, uos, gc\
+  \
+  sensor.reset() \# Reset and initialize the sensor.\
+  sensor.set_pixformat(sensor.RGB565) \# Set pxl fmt to RGB565 (or
+  GRAYSCALE)\
+  sensor.set_framesize(sensor.QVGA) \# Set frame size to QVGA (320x240)\
+  sensor.set_windowing((240, 240)) \# Set 240x240 window.\
+  sensor.skip_frames(time=2000) \# Let the camera adjust.\
+  \
+  net = None\
+  labels = None\
+  \
+  try:\
+  \# Load built in model\
+  labels, net = tf.load_builtin_model(\'trained\')\
+  except Exception as e:\
+  raise Exception(e)\
+  \
+  clock = time.clock()\
+  while(True):\
+  clock.tick() \# Starts tracking elapsed time.\
+  \
+  img = sensor.snapshot()\
+  \
+  \# default settings just do one detection\
+  for obj in net.classify(img,\
+  min_scale=1.0,\
+  scale_mul=0.8,\
+  x_overlap=0.5,\
+  y_overlap=0.5):\
+  fps = clock.fps()\
+  lat = clock.avg()\
+  \
+  print(\"\*\*\*\*\*\*\*\*\*\*\\nPrediction:\")\
+  img.draw_rectangle(obj.rect())\
+  \# This combines the labels and confidence values into a list of
+  tuples\
+  predictions_list = list(zip(labels, obj.output()))\
+  \
+  max_val = predictions_list\[0\]\[1\]\
+  max_lbl = \'background\'\
+  for i in range(len(predictions_list)):\
+  val = predictions_list\[i\]\[1\]\
+  lbl = predictions_list\[i\]\[0\]\
+  \
+  if val \> max_val:\
+  max_val = val\
+  max_lbl = lbl\
+  \
+  \# Print label with the highest probability\
+  if max_val \< 0.5:\
+  max_lbl = \'uncertain\'\
+  print(\"{} with a prob of {:.2f}\".format(max_lbl, max_val))\
+  print(\"FPS: {:.2f} fps ==\> latency: {:.0f} ms\".format(fps, lat))\
+  \
+  \# Draw label with highest probability to image viewer\
+  img.draw_string(\
+  10, 10,\
+  max_lbl + \"\\n{:.2f}\".format(max_val),\
+  mono_space = False,\
+  scale=2\
+  )
+  -----------------------------------------------------------------------
+
+  -----------------------------------------------------------------------
+
+Here you can see the result:
+
+![image.png](images_4/media/image47.jpg){width="6.5in"
+height="2.9444444444444446in"}
+
+Note that the latency (136 ms) is almost double what we got directly
+with the Arduino IDE. This is because we are using the IDE as an
+interface and the time to wait for the camera to be ready. If we start
+the clock just before the inference:
+
+![image.png](images_4/media/image13.jpg){width="6.5in"
+height="2.0972222222222223in"}
+
+The latency will drop to only 71 ms.
+
+![image.png](images_4/media/image1.jpg){width="3.5520833333333335in"
+height="1.53125in"}
+
+### ***OpenMV Cam runs about half as fast when connected to the IDE. The FPS should increase once disconnected.***
+
+### **Post-Processing with LEDs**
+
+When working with embedded machine learning, we are looking for devices
+that can continually proceed with the inference and result, taking some
+action directly on the physical world and not displaying the result on a
+connected computer. To simulate this, we will define one LED to light up
+for each one of the possible inference results.
+
+![image.png](images_4/media/image38.jpg){width="6.5in"
+height="3.236111111111111in"}
+
+For that, we should [[upload the code from
+GitHub]{.underline}](https://github.com/Mjrovai/Arduino_Nicla_Vision/blob/main/Micropython/nicla_image_classification_LED.py)
+or change the last code to include the LEDs:
+
+  -----------------------------------------------------------------------
+  \# Marcelo Rovai - NICLA Vision - Image Classification with LEDs\
+  \# Adapted from Edge Impulse - OpenMV Image Classification Example\
+  \# \@24Aug23\
+  \
+  import sensor, image, time, os, tf, uos, gc, pyb\
+  \
+  ledRed = pyb.LED(1)\
+  ledGre = pyb.LED(2)\
+  ledBlu = pyb.LED(3)\
+  \
+  sensor.reset() \# Reset and initialize the sensor.\
+  sensor.set_pixformat(sensor.RGB565) \# Set pixl fmt to RGB565 (or
+  GRAYSCALE)\
+  sensor.set_framesize(sensor.QVGA) \# Set frame size to QVGA (320x240)\
+  sensor.set_windowing((240, 240)) \# Set 240x240 window.\
+  sensor.skip_frames(time=2000) \# Let the camera adjust.\
+  \
+  net = None\
+  labels = None\
+  \
+  ledRed.off()\
+  ledGre.off()\
+  ledBlu.off()\
+  \
+  try:\
+  \# Load built in model\
+  labels, net = tf.load_builtin_model(\'trained\')\
+  except Exception as e:\
+  raise Exception(e)\
+  \
+  clock = time.clock()\
+  \
+  \
+  def setLEDs(max_lbl):\
+  \
+  if max_lbl == \'uncertain\':\
+  ledRed.on()\
+  ledGre.off()\
+  ledBlu.off()\
+  \
+  if max_lbl == \'periquito\':\
+  ledRed.off()\
+  ledGre.on()\
+  ledBlu.off()\
+  \
+  if max_lbl == \'robot\':\
+  ledRed.off()\
+  ledGre.off()\
+  ledBlu.on()\
+  \
+  if max_lbl == \'background\':\
+  ledRed.off()\
+  ledGre.off()\
+  ledBlu.off()\
+  \
+  \
+  while(True):\
+  img = sensor.snapshot()\
+  clock.tick() \# Starts tracking elapsed time.\
+  \
+  \# default settings just do one detection.\
+  for obj in net.classify(img,\
+  min_scale=1.0,\
+  scale_mul=0.8,\
+  x_overlap=0.5,\
+  y_overlap=0.5):\
+  fps = clock.fps()\
+  lat = clock.avg()\
+  \
+  print(\"\*\*\*\*\*\*\*\*\*\*\\nPrediction:\")\
+  img.draw_rectangle(obj.rect())\
+  \# This combines the labels and confidence values into a list of
+  tuples\
+  predictions_list = list(zip(labels, obj.output()))\
+  \
+  max_val = predictions_list\[0\]\[1\]\
+  max_lbl = \'background\'\
+  for i in range(len(predictions_list)):\
+  val = predictions_list\[i\]\[1\]\
+  lbl = predictions_list\[i\]\[0\]\
+  \
+  if val \> max_val:\
+  max_val = val\
+  max_lbl = lbl\
+  \
+  \# Print label and turn on LED with the highest probability\
+  if max_val \< 0.8:\
+  max_lbl = \'uncertain\'\
+  \
+  setLEDs(max_lbl)\
+  \
+  print(\"{} with a prob of {:.2f}\".format(max_lbl, max_val))\
+  print(\"FPS: {:.2f} fps ==\> latency: {:.0f} ms\".format(fps, lat))\
+  \
+  \# Draw label with highest probability to image viewer\
+  img.draw_string(\
+  10, 10,\
+  max_lbl + \"\\n{:.2f}\".format(max_val),\
+  mono_space = False,\
+  scale=2\
+  )
+  -----------------------------------------------------------------------
+
+  -----------------------------------------------------------------------
+
+Now, each time that a class gets a result superior of 0.8, the
+correspondent LED will be light on as below:
+
+-   Led Red 0n: Uncertain (no one class is over 0.8)
+
+-   Led Green 0n: Periquito \> 0.8
+
+-   Led Blue 0n: Robot \> 0.8
+
+-   All LEDs Off: Background \> 0.8
+
+Here is the result:
+
+![image.png](images_4/media/image18.jpg){width="6.5in"
+height="3.6527777777777777in"}
+
+In more detail
+
+![image.png](images_4/media/image21.jpg){width="6.5in"
+height="2.0972222222222223in"}
+
+### **Image Classification (non-official) Benchmark**
+
+Several development boards can be used for embedded machine learning
+(tinyML), and the most common ones for Computer Vision applications
+(with low energy), are the ESP32 CAM, the Seeed XIAO ESP32S3 Sense, the
+Arduinos Nicla Vison, and Portenta.
+
+![image.png](images_4/media/image19.jpg){width="6.5in"
+height="4.194444444444445in"}
+
+Using the opportunity, the same trained model was deployed on the
+ESP-CAM, the XIAO, and Portenta (in this one, the model was trained
+again, using grayscaled images to be compatible with its camera. Here is
+the result, deploying the models as Arduino\'s Library:
+
+![image.png](images_4/media/image4.jpg){width="6.5in"
+height="3.4444444444444446in"}
+
+### **Conclusion**
+
+Before we finish, consider that Computer Vision is more than just image
+classification. For example, you can develop Edge Machine Learning
+projects around vision in several areas, such as:
+
+-   **Autonomous Vehicles**: Use sensor fusion, lidar data, and computer
+    > vision algorithms to navigate and make decisions.
+
+-   **Healthcare**: Automated diagnosis of diseases through MRI, X-ray,
+    > and CT scan image analysis
+
+-   **Retail**: Automated checkout systems that identify products as
+    > they pass through a scanner.
+
+-   **Security and Surveillance**: Facial recognition, anomaly
+    > detection, and object tracking in real-time video feeds.
+
+-   **Augmented Reality**: Object detection and classification to
+    > overlay digital information in the real world.
+
+-   **Industrial Automation**: Visual inspection of products, predictive
+    > maintenance, and robot and drone guidance.
+
+-   **Agriculture**: Drone-based crop monitoring and automated
+    > harvesting.
+
+-   **Natural Language Processing**: Image captioning and visual
+    > question answering.
+
+-   **Gesture Recognition**: For gaming, sign language translation, and
+    > human-machine interaction.
+
+-   **Content Recommendation**: Image-based recommendation systems in
+    > e-commerce.
diff --git a/embedded_sys_exercise.qmd b/embedded_sys_exercise.qmd
new file mode 100644
index 00000000..d4d36577
--- /dev/null
+++ b/embedded_sys_exercise.qmd
@@ -0,0 +1,496 @@
+---
+title: "[]{#_6ugxbtjss6rg .anchor}2 Embedded Systems"
+---
+
+# Exercise - The Nicla Vision
+
+**Introduction**
+
+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.*
+
+![image.png](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
+
+![image.png](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 :
+
+![image.png](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.
+
+![image.png](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:
+
+![image.png](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:
+
+![image.png](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):
+
+![image.png](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:
+
+![image.png](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):
+
+![image.png](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).
+
+![image.png](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.
+
+![image.png](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
+
+![image.png](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):
+
+![image.png](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.
+
+![image.png](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.
+
+![image.png](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 \[OK\].
+
+![image.png](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.
+
+![image.png](images_2/media/image18.jpg){width="4.791666666666667in"
+height="1.4791666666666667in"}
+
+Also, note that a drive named "NO NAME" will appear on your computer.:
+
+![image.png](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\...).
+
+![image.png](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.
+
+![image.png](images_2/media/image6.png){width="6.5in"
+height="3.9722222222222223in"}
+
+Let\'s go through the [helloworld.py](http://helloworld.py/) script:
+
+  -----------------------------------------------------------------------
+  \# Hello World Example\
+  \#\
+  \# 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:
+
+![image.png](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).
+
+![image.png](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.
+
+![image.png](images_2/media/image25.png){width="6.5in"
+height="4.319444444444445in"}
+
+For example. IMU data:
+
+![image.png](images_2/media/image8.png){width="6.5in"
+height="4.319444444444445in"}
+
+Or Image:
+
+![image.png](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.*
+
+![image.png](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.
+
+![image.png](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:
+
+![image.png](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.:
+
+  -----------------------------------------------------------------------
+  \# 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.
+
+![B3D78F51-83F9-413D-8BF9-ED46FAA82F49.GIF](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).
+
+  -----------------------------------------------------------------------
+  \# 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):
+
+  -----------------------------------------------------------------------
+  \# 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.*
diff --git a/images_2/media/image1.png b/images_2/media/image1.png
new file mode 100644
index 00000000..fc6af05b
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diff --git a/images_2/media/image10.png b/images_2/media/image10.png
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index 00000000..1abb3211
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diff --git a/images_2/media/image11.jpg b/images_2/media/image11.jpg
new file mode 100644
index 00000000..ec677570
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diff --git a/images_2/media/image12.png b/images_2/media/image12.png
new file mode 100644
index 00000000..827cd7db
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diff --git a/images_2/media/image13.jpg b/images_2/media/image13.jpg
new file mode 100644
index 00000000..ef54e2b8
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diff --git a/images_2/media/image14.jpg b/images_2/media/image14.jpg
new file mode 100644
index 00000000..08380d3a
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diff --git a/images_2/media/image15.jpg b/images_2/media/image15.jpg
new file mode 100644
index 00000000..89f94595
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diff --git a/images_2/media/image16.png b/images_2/media/image16.png
new file mode 100644
index 00000000..e42200ce
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diff --git a/images_2/media/image17.png b/images_2/media/image17.png
new file mode 100644
index 00000000..7e91e833
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diff --git a/images_2/media/image18.jpg b/images_2/media/image18.jpg
new file mode 100644
index 00000000..2befeb33
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diff --git a/images_2/media/image19.jpg b/images_2/media/image19.jpg
new file mode 100644
index 00000000..48ea5478
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diff --git a/images_2/media/image2.jpg b/images_2/media/image2.jpg
new file mode 100644
index 00000000..cc513fc7
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diff --git a/images_2/media/image20.jpg b/images_2/media/image20.jpg
new file mode 100644
index 00000000..78af95e3
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diff --git a/images_2/media/image21.png b/images_2/media/image21.png
new file mode 100644
index 00000000..ca21a4e3
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diff --git a/images_2/media/image22.jpg b/images_2/media/image22.jpg
new file mode 100644
index 00000000..082fce1a
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diff --git a/images_2/media/image23.jpg b/images_2/media/image23.jpg
new file mode 100644
index 00000000..01abff78
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diff --git a/images_2/media/image24.gif b/images_2/media/image24.gif
new file mode 100644
index 00000000..0ed868cb
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diff --git a/images_2/media/image25.png b/images_2/media/image25.png
new file mode 100644
index 00000000..37bfebed
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diff --git a/images_2/media/image26.jpg b/images_2/media/image26.jpg
new file mode 100644
index 00000000..065f5fa0
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diff --git a/images_2/media/image27.png b/images_2/media/image27.png
new file mode 100644
index 00000000..f086876a
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diff --git a/images_2/media/image28.png b/images_2/media/image28.png
new file mode 100644
index 00000000..31d21367
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diff --git a/images_2/media/image29.jpg b/images_2/media/image29.jpg
new file mode 100644
index 00000000..ae46fa75
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diff --git a/images_2/media/image3.png b/images_2/media/image3.png
new file mode 100644
index 00000000..43e6542f
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diff --git a/images_2/media/image4.png b/images_2/media/image4.png
new file mode 100644
index 00000000..e6aa3c2b
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diff --git a/images_2/media/image5.png b/images_2/media/image5.png
new file mode 100644
index 00000000..16e03f14
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diff --git a/images_2/media/image6.png b/images_2/media/image6.png
new file mode 100644
index 00000000..7b467707
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diff --git a/images_2/media/image7.png b/images_2/media/image7.png
new file mode 100644
index 00000000..31869aa1
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diff --git a/images_2/media/image8.png b/images_2/media/image8.png
new file mode 100644
index 00000000..2add8235
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diff --git a/images_2/media/image9.png b/images_2/media/image9.png
new file mode 100644
index 00000000..22ebf5be
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diff --git a/images_4/media/image1.jpg b/images_4/media/image1.jpg
new file mode 100644
index 00000000..48985805
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diff --git a/images_4/media/image10.jpg b/images_4/media/image10.jpg
new file mode 100644
index 00000000..8cf6eb84
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diff --git a/images_4/media/image11.png b/images_4/media/image11.png
new file mode 100644
index 00000000..613f3581
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diff --git a/images_4/media/image12.png b/images_4/media/image12.png
new file mode 100644
index 00000000..24d869c4
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diff --git a/images_4/media/image13.jpg b/images_4/media/image13.jpg
new file mode 100644
index 00000000..5298efc0
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diff --git a/images_4/media/image14.png b/images_4/media/image14.png
new file mode 100644
index 00000000..d8702a68
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diff --git a/images_4/media/image15.jpg b/images_4/media/image15.jpg
new file mode 100644
index 00000000..5aea170d
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diff --git a/images_4/media/image16.png b/images_4/media/image16.png
new file mode 100644
index 00000000..af218e99
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diff --git a/images_4/media/image17.png b/images_4/media/image17.png
new file mode 100644
index 00000000..8fc0973d
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diff --git a/images_4/media/image18.jpg b/images_4/media/image18.jpg
new file mode 100644
index 00000000..38d81100
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diff --git a/images_4/media/image19.jpg b/images_4/media/image19.jpg
new file mode 100644
index 00000000..c60501e6
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diff --git a/images_4/media/image2.jpg b/images_4/media/image2.jpg
new file mode 100644
index 00000000..f5c2e439
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diff --git a/images_4/media/image20.png b/images_4/media/image20.png
new file mode 100644
index 00000000..dd254917
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diff --git a/images_4/media/image21.jpg b/images_4/media/image21.jpg
new file mode 100644
index 00000000..b552ec21
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diff --git a/images_4/media/image22.png b/images_4/media/image22.png
new file mode 100644
index 00000000..71987ffd
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diff --git a/images_4/media/image23.jpg b/images_4/media/image23.jpg
new file mode 100644
index 00000000..48bcaef2
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diff --git a/images_4/media/image24.png b/images_4/media/image24.png
new file mode 100644
index 00000000..918d3a66
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diff --git a/images_4/media/image25.jpg b/images_4/media/image25.jpg
new file mode 100644
index 00000000..f4cf0e5d
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diff --git a/images_4/media/image26.png b/images_4/media/image26.png
new file mode 100644
index 00000000..e6752d55
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diff --git a/images_4/media/image27.jpg b/images_4/media/image27.jpg
new file mode 100644
index 00000000..bc2635d3
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diff --git a/images_4/media/image28.jpg b/images_4/media/image28.jpg
new file mode 100644
index 00000000..b857f18f
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diff --git a/images_4/media/image29.png b/images_4/media/image29.png
new file mode 100644
index 00000000..272a0fe8
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diff --git a/images_4/media/image3.png b/images_4/media/image3.png
new file mode 100644
index 00000000..bdc1b381
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diff --git a/images_4/media/image30.png b/images_4/media/image30.png
new file mode 100644
index 00000000..92857046
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diff --git a/images_4/media/image31.jpg b/images_4/media/image31.jpg
new file mode 100644
index 00000000..7b4d60bd
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diff --git a/images_4/media/image32.jpg b/images_4/media/image32.jpg
new file mode 100644
index 00000000..b3764526
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diff --git a/images_4/media/image33.png b/images_4/media/image33.png
new file mode 100644
index 00000000..e6549013
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diff --git a/images_4/media/image34.png b/images_4/media/image34.png
new file mode 100644
index 00000000..28f85e7d
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diff --git a/images_4/media/image35.jpg b/images_4/media/image35.jpg
new file mode 100644
index 00000000..9797154d
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diff --git a/images_4/media/image36.jpg b/images_4/media/image36.jpg
new file mode 100644
index 00000000..5bfffd04
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diff --git a/images_4/media/image37.png b/images_4/media/image37.png
new file mode 100644
index 00000000..a91610b7
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diff --git a/images_4/media/image38.jpg b/images_4/media/image38.jpg
new file mode 100644
index 00000000..1c5e92a8
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diff --git a/images_4/media/image39.png b/images_4/media/image39.png
new file mode 100644
index 00000000..2c908993
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diff --git a/images_4/media/image4.jpg b/images_4/media/image4.jpg
new file mode 100644
index 00000000..2a0f5dfc
Binary files /dev/null and b/images_4/media/image4.jpg differ
diff --git a/images_4/media/image40.png b/images_4/media/image40.png
new file mode 100644
index 00000000..28100ac2
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diff --git a/images_4/media/image41.jpg b/images_4/media/image41.jpg
new file mode 100644
index 00000000..6b9b7890
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diff --git a/images_4/media/image42.png b/images_4/media/image42.png
new file mode 100644
index 00000000..3d4bbead
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diff --git a/images_4/media/image43.png b/images_4/media/image43.png
new file mode 100644
index 00000000..04bbb50a
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diff --git a/images_4/media/image44.png b/images_4/media/image44.png
new file mode 100644
index 00000000..2514285d
Binary files /dev/null and b/images_4/media/image44.png differ
diff --git a/images_4/media/image45.png b/images_4/media/image45.png
new file mode 100644
index 00000000..ccc0a398
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diff --git a/images_4/media/image46.png b/images_4/media/image46.png
new file mode 100644
index 00000000..deff43bc
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diff --git a/images_4/media/image47.jpg b/images_4/media/image47.jpg
new file mode 100644
index 00000000..d38a5128
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diff --git a/images_4/media/image48.png b/images_4/media/image48.png
new file mode 100644
index 00000000..050992b0
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diff --git a/images_4/media/image5.png b/images_4/media/image5.png
new file mode 100644
index 00000000..585a0644
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diff --git a/images_4/media/image6.jpg b/images_4/media/image6.jpg
new file mode 100644
index 00000000..20205865
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diff --git a/images_4/media/image7.jpg b/images_4/media/image7.jpg
new file mode 100644
index 00000000..a7de9ffe
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diff --git a/images_4/media/image8.png b/images_4/media/image8.png
new file mode 100644
index 00000000..d5648cfa
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diff --git a/images_4/media/image9.jpg b/images_4/media/image9.jpg
new file mode 100644
index 00000000..60c44fae
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