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Title: Neuromorphic Sensor Integration for Plant Health Monitoring
This project will be carried out in collaboration with the FHNW Institute for Sensors and Electronics
Monitoring plant health is crucial for early detection of pests, identifying anomalies, and ensuring timely interventions. While numerous sensors are available for this purpose, selecting the most effective ones and eliminating redundancy remains a challenge. Additionally, transmitting large volumes of data to the cloud is power-intensive, especially in resource-constrained environments. To address these challenges, local preprocessing is essential to reduce data load and enhance efficiency. Leveraging neuromorphic hardware provides a promising approach to achieve low-power, real-time processing for plant status monitoring.
Project: The goal of this project is to implement Spiking Neural Networks (SNNs) on mixed-signal DYNAPSE chips to integrate sensor data for plant health monitoring. The student will:
Identify essential sensors and determine which are redundant.
Design and implement an SNN-based approach for local data preprocessing.
Implement the network on the mixed-signal DYNAP-SE chip[2]
Use data and experimental setups provided in collaboration with the FHNW Institute for Sensors and Electronics to validate the system's performance.
This interdisciplinary project merges cutting-edge neuromorphic computing with real-world agricultural applications, providing an exciting opportunity for impactful research.
Project: The goal of this project is to implement Spiking Neural Networks (SNNs) on mixed-signal DYNAPSE chips to integrate sensor data for plant health monitoring. The student will: Identify essential sensors and determine which are redundant. Design and implement an SNN-based approach for local data preprocessing. Implement the network on the mixed-signal DYNAP-SE chip[2] Use data and experimental setups provided in collaboration with the FHNW Institute for Sensors and Electronics to validate the system's performance. This interdisciplinary project merges cutting-edge neuromorphic computing with real-world agricultural applications, providing an exciting opportunity for impactful research.
Leveraging neuromorphic hardware to achieve low-power, real-time processing for plant status monitoring.
Leveraging neuromorphic hardware to achieve low-power, real-time processing for plant status monitoring.
