Spectrometry for Environmental Monitoring (1-2S/M)

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

Multi-modal sensing enables on-device feature extraction, reducing communication overhead, perform predictions, increases privacy and enable on-device learning.

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.

Environmental monitoring continues to grab interest as climate change becomes more significant for the population, with profound economic implications that are increasingly harder to overlook. Air, water, and soil are pillars of human existence and are significantly affected by these changes. Among the many sensing mechanisms, spectrometers have shown significant potential to provide information on soil, water quality, and atmospheric parameters. In this project, students will evaluate newer, compact spectrometers [1] for extracting significant parameters and implement efficient embeddable real-time algorithms for data elaboration to be deployed on energy-efficient AI-capable platforms such as the GAP9 SoCs. These efforts will contribute to developing technology to understand the cause-effect relationship between human practices and the current changes we experience.

Project

• Sensor Driver Development and Characterization: Development of libraries for the data readout and characterization of the device for different known sources.
• Sensor Optimization and Integration: Optimizations to improve performance and creation of an embedded system integrating the spectrometer with other relevant sensing devices, compatible with and AI-capable platform.
• Application Specific DSP Development: Development of AI algorithm to extract relevant parameters for Air, water, or soil, and potential on-device learning.
• Deployment on Edge Device: Deployment and optimization of energy efficiency.

Prerequisites

• Good understanding of deep learning concepts.
• Familiarity with Python programming.
• Familiarity with the PyTorch deep learning framework.
• Basics of embedded C programming.
• PCB Design and embedded C programming.

References

[1] Hamamatsu Mini-Spectrometer Micro Series C12880MA