spelling fixes 4

.wordlist.txt

@@ -2155,6 +2155,7 @@ vcLQKY
 Roboflow
 SRDF
 OpenVINO’s
+OpenVINO's
 oakd
 MRD
 oAxaV
@@ -2166,4 +2167,30 @@ OpenAI's
 PRjhA
 jBE
 gnhUcwYqrI
+RTU
+shrimplets
+TetrisBot
+serv
+pairplot
+CLK
+datacenter
+Printables
+Dall
+overpredicting
+Autotuning
+RTU
+shrimplets
+TetrisBot
+pairplot
+CLK
+datacenter
+Printables
+Autotuning
+Powerstrip
+Chardev
+tera
+HailoTracker
+Roboflow’s
+Lescaudron
+ECR
 

air-quality-and-environmental-projects/water-pollution-detection-arduino-nano-esp32.md

@@ -52,7 +52,7 @@ Even though this underwater air bubble and water pollution detection device is c
 
 Then, I employed the web application to communicate with UNIHIKER to generate a pre-formatted CSV file from the stored sample text files (ultrasonic scan data records) and transfer the latest neural network model detection result (ultrasonic scan buffer and the detected label) via an HTTP GET request.
 
-As mentioned repeatedly, each generated ultrasonic scan buffer provides 400 data points as a 20 x 20 ultrasonic image despite the fact that Nano ESP32 cannot utilize the given buffer to produce an ultrasonic image after running the neural network model with the Ridge classifier. Therefore, after receiving the latest model detection result via the web application, I employed UNIKIHER to modify a template image (black square) via the built-in OpenCV functions to convert the given ultrasonic scan buffer to a JPG file and save the modified image to visualize the latest aquatic ultrasonic scan with thoroughly encoded pixels.
+As mentioned repeatedly, each generated ultrasonic scan buffer provides 400 data points as a 20 x 20 ultrasonic image despite the fact that Nano ESP32 cannot utilize the given buffer to produce an ultrasonic image after running the neural network model with the Ridge classifier. Therefore, after receiving the latest model detection result via the web application, I employed UNIHIKER to modify a template image (black square) via the built-in OpenCV functions to convert the given ultrasonic scan buffer to a JPG file and save the modified image to visualize the latest aquatic ultrasonic scan with thoroughly encoded pixels.
 
 Since the RetinaNet object detection model provides accurate bounding box measurements, I also utilized UNIHIKER to modify the resulting images to draw the associated bounding boxes and save the modified resulting images as JPG files for further inspection.
 

readme/featured-machine-learning-projects/robotic-arm-sorting-arduino-braccio.md

@@ -372,7 +372,7 @@ $ mo --model_name ei-pnp_yolov5n_320 \
 --input_model ei-pnp_yolov5n_320_batch32_epoch100_prune.onnx
 ```
 
-After converting the model to OpenVINO’s IR format, run the following script to compile it into a `.blob` file, which can be deployed to the OAK-D device.
+After converting the model to OpenVINO's IR format, run the following script to compile it into a `.blob` file, which can be deployed to the OAK-D device.
 
 ```
 import blobconverter
@@ -474,7 +474,7 @@ Planning groups in MoveIt 2 semantically describe different parts of the robot,
 
 ![moveit2\_assistant\_4](../../.gitbook/assets/robotic-arm-sorting-arduino-braccio/moveit2_assistant_4.png)
 
-The Setup Assistant allows us to add predefined poses to the robot’s configuration, which can be useful for defining specific initial or ready poses. Later, the robot can be commanded to move to these poses using the MoveIt API. Click on the **Add Pose** and choose a name for the pose.
+The Setup Assistant allows us to add predefined poses to the robot's configuration, which can be useful for defining specific initial or ready poses. Later, the robot can be commanded to move to these poses using the MoveIt API. Click on the **Add Pose** and choose a name for the pose.
 
 ![moveit2\_assistant\_5](../../.gitbook/assets/robotic-arm-sorting-arduino-braccio/moveit2_assistant_5.png)