Jonathan Larochelle

I am a doctoral researcher focusing on the design of sustainable electronics within the circular economy paradigm. I specifically consider intelligent embedded systems such as Internet of Things (IoT) devices. I am additionnally contributing to projects concerned with environmental sensing and embedded machine learning. I am part of Prof. Laura Maria Comella's Laboratory for Intelligent Embedded Systems at the Karlsruhe University of Applied Sciences, Germany.

While passionate about the dizzying technological progress of the current times, I am critical about many of its applications, especially with regard to its unforeseen negative consequences on our society and the environment. I am still figuring out how I can integrate stronger notions of sustainability, conviviality, and circularity in my design practice. Any pointers are more than welcome.

Outside of office hours, I enjoy biking in the german landscape and sailing on the Schluchsee. I am also part of the organisation committee for the SustainableICT Doctoral School, the Repair-Café Freiburg, and a sailing teacher at the University of Freiburg.

Rethinking the Design of Internet of Things Devices in a Circular Economy

In my doctoral project, I aim to analyze the _circularity of IoT_, as opposed to the use of _IoT for circularity_. Specifically, I will bridge the gap between _a posteriori_ environmental assessment methods and electronics design methodology to enable _a priori_ consideration of circularity issues. Because of the breadth of the subject, I will focus specifically on the circularity of IoT power supply, which is composed of energy source (mains power, batteries, energy harvesting) and storage technologies.

This work is funded by the Hans-Böckler Stiftung.

LAIsens - Low power optical sensing system for Leaf Area Index measurement using a dynamically adjustable field-of-view

Metrics such as the Leaf area index (LAI) and the Photosynthetically active radiation (PAR) are used by biologists to characterize plant metabolism and follow its evolution over time. The most common measurement systems use a combination of photodiodes to measure the incident light. To account for the irregular placement of leaves in a plant canopy, it is generally recommended to take many measurements at different positions around the plant, which prevents unattended continuous monitoring (in a forest, for example).

To solve this issue, I have developed a sensor concept which can "scan" its incident field-of-view, enabling identifying and processing the areas with lower or higher leaves density. This novel system uses a liquid crystal display to serve as an optical shutter. I have prototyped the system, developed a calibration procedure and set-up, and validated the system during a field measurement campaign. I have written my Master's thesis on this project. Additionnally, the system concept and a calibration procedure have been the subject of an article.

The current follow-up work is investigating the use of another scanning principle based on a digital micromirror device (DMD).

This work is funded by the Carl-Zeiss-Stiftung and was conducted in collaboration with the Kompetenzzentrum Obstbau Bodensee.

Brain-MEP - Miniaturized Electrical Pulse Generator for Brain

This project aimed to develop a minimally invasive implantable device for responsive neurostimulation used in epilepsy treatment. I was responsible for the optimization of machine learning algorithms for deployment in a ultra-low-power microcontroller. I have developed a hardware-aware neural architecture search pipeline in order to optimize a convolutional neural network for development on a specific microcontroller. Additionally, I have investigated the impact of EEG preprocessing parameters and random forest architectures on epilepsy detection accuracy and energy consumption.

This work was funded by BWInvest and conducted in collaboration with the medical centers of the University of Freiburg and the University of Mannheim, as well as Precisis.