Waterflow Monitoring with Doppler Ultrasound (1S)

Status: Available

• Type: Semester Thesis
• Professor: Prof. Dr. L. Benini
• Supervisors:
  • Philip Wiese (IIS): wiesep@iis.ee.ethz.ch
  • Victor Kartsch Morinigo (IIS): victor.kartsch@iis.ee.ethz.ch

Introduction

Doppler-Ultrasound based water or gas flow monitoring.

Smart sensors, which are sensors equipped with advanced computation capabilities, present a unique opportunity for innovation in multiple critical domains such as environmental sensing to reduce global greenhouse gas emissions or infrastructure monitoring. Our infrastructure is responsible for almost 80% of all global greenhouse gas emissions. Therefore, it is very important to better understand the individual contributors and to be able to build precise models to plan future infrastructure.

A near-sensor processing of the gathered data that directly extracts meaning from the signal can lead to huge improvements in terms of energy efficiency and increased privacy. The reduced energy consumption comes from the fact that more specialized hardware such as the PULP platform including the GAP9 can be used, and, additionally, that the information density of the transmitted data is higher, and thus less data must be transmitted via the often very power-intensive wireless link.

Our long-term goal is to develop a low-power sensing node with very low power consumption. These nodes should incorporate multiple sensors to enable multi-modal sensing. In other words, combine the data of the different sensors to extract additional data. The idea is to use the very efficient GAP9 multicore processor and deploy multi-modal neural networks to perform feature extraction, prediction, anomaly detection, and even on-device learning to adaptively fine-tune the model based on the measured data.

Water or gas flow monitoring is essential for ensuring efficient and safe distribution systems and managing resource usage effectively. Traditional flow rate sensors often require invasive procedures for installation, which can disrupt service and increase maintenance complexity. In contrast, using ultrasound technology with two transducers that send signals back and forth through the medium offers a significant advancement. By employing the Doppler effect, this method can accurately determine the flow rate of the medium, whether it be water or gas. The primary advantage of ultrasound flow monitoring is its non-invasive nature. Hence, sensors can be installed externally without altering or penetrating the pipeline, simplifying the installation process, minimizing the risk of contamination, and reducing downtime associated with maintenance. In this work, students will investigate current US technology, develop feature extraction and machine learning models for accurate reading, and embed these algorithms on an efficient low-power platform to provide a stand-alone solution.

Project

• Data Acquisition: Development of a platform for the signal acquisition and creation of a dataset.
• Algorithm and Network Architecture: Benchmarking of different algorithms and neural networks based on the current literature.
• Implementation: Implementation of algorithm on GAP9.

Prerequisites

• Fundamental deep learning concepts.
• PCB Design and embedded C programming.
• Familiarity with Python programming.
• Familiarity with the PyTorch deep learning framework.