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[[Category:Digital]]
 
[[Category:Digital]]
[[Category:Completed]]
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[[Category:Available]]
 
[[Category:Bachelor Thesis]]
 
[[Category:Bachelor Thesis]]
 
[[Category:Semester Thesis]]
 
[[Category:Semester Thesis]]
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[[Category:EmbeddedAI]]
 
[[Category:EmbeddedAI]]
 
[[Category:Low Power Embedded Systems and Wireless Sensors Networks]]
 
[[Category:Low Power Embedded Systems and Wireless Sensors Networks]]
[[Category:2022]]
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[[Category:2023]]
 
[[Category:Lbertaccini]]
 
[[Category:Lbertaccini]]
  
 
[[File:smart_meters.png|600px|right|thumb]]
 
[[File:smart_meters.png|600px|right|thumb]]
  
===Status: Completed ===
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===Status: Available ===
  
 
== Description ==
 
== Description ==
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A prototype connecting GAPuino with the modem and able to send messages to a server has already been implemented. During this project, you will optimize the pipeline for energy efficiency, interface GAPuino with the ultra-low-power camera and build the pattern recognition application.
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A prototype connecting GAPuino with the modem and the low-power camera and able to send messages to a server is already available. During this project, you will train the NN model, deploy it on GAPuino, test the final device and optimize the pipeline for energy efficiency.
  
 
===== Application Scenario =====  
 
===== Application Scenario =====  
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===== Requirements =====  
 
===== Requirements =====  
  
* Familiarity with C programming
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* Familiarity with C and Python programming
 
* Basic knowledge of communication protocols
 
* Basic knowledge of communication protocols
  
 
===== Task Description =====
 
===== Task Description =====
* Interfacing PULP with the ultra-low-power camera
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* Training of the NN model for meter detection and recognition
* Implementation of a pattern recognition algorithm on a microcontroller
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* Deployment of the model on the IoT device
 
* Testing the system and evaluate the power consumption  
 
* Testing the system and evaluate the power consumption  
 
* Optimization for energy efficiency
 
* Optimization for energy efficiency
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===== Project Supervisor =====  
 
===== Project Supervisor =====  
 
* [[:User:Lbertaccini | Luca Bertaccini]]: [mailto:lbertaccini@iis.ee.ethz.ch lbertaccini@iis.ee.ethz.ch]
 
* [[:User:Lbertaccini | Luca Bertaccini]]: [mailto:lbertaccini@iis.ee.ethz.ch lbertaccini@iis.ee.ethz.ch]
* [[:User:scheremo | Moritz Scherer]]: [mailto:scheremo@iis.ee.ethz.ch scheremo@iis.ee.ethz.ch];
 

Latest revision as of 11:14, 7 November 2022

Smart meters.png

Status: Available

Description

The Internet of Things (IoT) era is characterized by billions of devices gathering data and sending them to servers, where they can be analyzed and processed. A pre-processing step can also be implemented directly on the IoT device to save energy and bandwidth. Extracting information on the edge allows sending a lighter payload to the server, thus reducing the time spent in transmission.


The goal of this project is to implement a low-cost solution to make mechanical meters smart, instead of replacing them with costly devices. The students will work on a Smart Meter, an IoT system based on:

  • GAPuino, a development board based on PULP (Parallel Ultra-Low-Power Processing Platform), developed here at IIS. PULP is an open-source multi-core platform achieving leading-edge energy efficiency and featuring widely-tunable performance
  • a modem for wireless connectivity
  • an ultra-low-power camera

The system will periodically wake up, take a picture, process the image extracting the number displayed on the meters and transmit the value wirelessly. A wide range of different meters exists and many of them are located in environments with difficult lighting conditions. Therefore, analyzing the image on the edge will require robust pattern recognition algorithms.


A prototype connecting GAPuino with the modem and the low-power camera and able to send messages to a server is already available. During this project, you will train the NN model, deploy it on GAPuino, test the final device and optimize the pipeline for energy efficiency.

Application Scenario

The smart meter will be employed in an IoT scenario. The automatic recognition of the number displayed on the meter and its wireless transmission will replace the need for a person to read the meter and annotate the measurement.

Requirements
  • Familiarity with C and Python programming
  • Basic knowledge of communication protocols
Task Description
  • Training of the NN model for meter detection and recognition
  • Deployment of the model on the IoT device
  • Testing the system and evaluate the power consumption
  • Optimization for energy efficiency
Project Supervisor