GITBOOK-7: Renaming Test

SUMMARY.md

@@ -53,7 +53,7 @@
 * [Acoustic Pipe Leakage Detection - Arduino Portenta H7](acoustic-pipe-leak-detection-arduino-portenta-h7.md)
 * [Worker Safety Monitoring with Nvidia Jetson Nano](worker-safety-monitoring.md)
 * [Bhopal 84, Detect Harmful Gases](detect-harmful-gases.md)
-* [Bean Leaf Classification with Sony Spresense](bean-leaf-classification.md)
+* [Bean Leaf Disease Classification - Sony Spresense](prototype-and-concept-projects/bean-leaf-disease-classification-sony-spresense.md)
 * [Collect Data for Keyword Spotting with Raspberry Pi Pico and Edge Impulse](collect-data-raspberrypi-pico.md)
 * [Voice-Activated LED Strip for $10: Raspberry Pi Pico and Edge Impulse](voice-activated-led-controller.md)
 * [Oil Tank Measurement and Delivery Improvement Using Computer Vision](oil-tank-gauge-monitoring.md)

acoustic-pipe-leak-detection-arduino-portenta-h7.md

@@ -1,14 +1,14 @@
 ---
-description: Using an Arduino Portenta H7 to listen for and classify the flow of water in a pipe.
+description: >-
+  Using an Arduino Portenta H7 to listen for and classify the flow of water in a
+  pipe.
 ---
 
 # Acoustic Pipe Leakage Detection - Arduino Portenta H7
 
-Created By:
-Manivannan Sivan 
+Created By: Manivannan Sivan
 
-Public Project Link:
-[https://studio.edgeimpulse.com/public/111978/latest](https://studio.edgeimpulse.com/public/111978/latest)
+Public Project Link: [https://studio.edgeimpulse.com/public/111978/latest](https://studio.edgeimpulse.com/public/111978/latest)
 
 ## Project Demo
 
@@ -18,12 +18,11 @@ Public Project Link:
 
 Water is the world's most precious resource, yet it is also the one which is almost universally mismanaged. As a result, water shortages are becoming ever more common. In the case of water supply and distribution networks, these manifest themselves in the intermittent operation of the system. Not only is this detrimental to the structural condition of the pipes, but can also adversely affect the quality of the water delivered to the customer's taps. Further, leakage often exceeds 50% of the production. Not only does this have a significant economic impact, but an environmental one too. But to recover leakage has a cost to undertake a hydraulic study of the network, create a permanent monitoring system, and eliminate the leaks. So how low should leakage go and how can a lower leakage level be maintained over time? This is the objective of the very innovative EU funded PALM project recently completed in central Italy.
 
-
 ### Increase in Carbon Level Due to Water Leakage
 
-There is an increased carbon footprint of having pumps constantly running to make up for the water lost due to leakage. It is the increased pump use, and pump maintenance/replacement costs that increase CO2 in the air from the fossil fuels being burned to support it. According to a study done by Von Sacken in 2001, water utilities are the largest user of electricity accounting for 3% of the total electricity consumption in the US. In addition, it is estimated that 2-3 billion kW/h of electricity is expended pumping water due to leakage.
+There is an increased carbon footprint of having pumps constantly running to make up for the water lost due to leakage. It is the increased pump use, and pump maintenance/replacement costs that increase CO2 in the air from the fossil fuels being burned to support it. According to a study done by Von Sacken in 2001, water utilities are the largest user of electricity accounting for 3% of the total electricity consumption in the US. In addition, it is estimated that 2-3 billion kW/h of electricity is expended pumping water due to leakage.
 
-*Costs, health, the environment, and infrastructure are just a few things that can come into play when water system leakage goes uncorrected.* 
+_Costs, health, the environment, and infrastructure are just a few things that can come into play when water system leakage goes uncorrected._ 
 
 More than 2 billion people globally live in countries with high water stress, per the 2018 statistics provided by the United Nations (UN). In order to tackle this problem, it is necessary to conserve and utilize water safely. Installation of proper water pipeline leak detection systems assist in specifying the leakages in installed water pipes, which ultimately avoids wasting water through cracks and holes. Therefore, the increasing scarcity of water is propelling the demand for water leak solutions, which in turn drives the market.
 
@@ -41,7 +40,7 @@ On the contrary, in recent years, pipeline leak detection systems have undergone
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/intro-4.jpg)
 
-In recent years, the increase in acoustic-based pipe leakage detection has started increasing due to investment in R&D.
+In recent years, the increase in acoustic-based pipe leakage detection has started increasing due to investment in R\&D.
 
 ## A TinyML-based Solution for Pipe Leakage Detection:
 
@@ -53,7 +52,7 @@ My prototype is based on acoustic data collected on an Arduino Portenta H7 and a
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/prototype-2.jpg)
 
-In the data acquisition stage, the pipe is simulated with "Idle" mode, where the tap is fully closed so no water flows,  and then slightly opened to simulate "leakage mode".  Finally, is it fully opened to simulate "water flow" mode.
+In the data acquisition stage, the pipe is simulated with "Idle" mode, where the tap is fully closed so no water flows, and then slightly opened to simulate "leakage mode". Finally, is it fully opened to simulate "water flow" mode.
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/data-acquisition.jpg)
 
@@ -71,15 +70,15 @@ For Neural Network configuration, I have used couple of 1D-Conv layers followed
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/nn-settings.jpg)
 
-The number of Training cycles is set to 100 and Learning rate is set to 0.005. The accuracy obtained was 99.1 % with loss of 0.02 only. As the model is performing well at classifying the data, we can move on. 
+The number of Training cycles is set to 100 and Learning rate is set to 0.005. The accuracy obtained was 99.1 % with loss of 0.02 only. As the model is performing well at classifying the data, we can move on.
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/model-accuracy.jpg)
 
 ## Model Testing
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/model-testing.jpg)
 
-In Model testing, the trained model is tested with data and it is able to predict all 3 conditions we trained on with 100% accuracy. 
+In Model testing, the trained model is tested with data and it is able to predict all 3 conditions we trained on with 100% accuracy.
 
 ## Deployment
 
@@ -102,5 +101,3 @@ The prototype demonstrated an acoustic method to predict leakage in a pipe. The
 By integrating well-designed enclosures with higher quality microphones, the Arduino Portenta H7 will be ideal for industrial use-cases for pipe leakage detection.
 
 ![](.gitbook/assets/acoustic-pipe-leak-detection-arduino-portenta-h7/summary.jpg)
-
-