03/30/2026
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation defense by Flore Stécie Norcéide on: "Neuromorphic Imaging and Processing for Tracking and Evaluation at the Edge (NIPTEE)."

Candidate Name: Flore Stécie Norcéide
Degree: Doctoral
Defense Date: Friday, April 10, 2026
Time: 11 a.m.-1 p.m.
Location: Ball 313

Committee:

  • Advisor: Kavitha Chandra, Eng. D., Professor, Electrical and Computer Engineering, UMass Lowell
  • Charles Thompson, Ph.D., Professor, Electrical and Computer Engineering, UMass Lowell
  • Orlando Arias, Ph.D., Assistant Professor, Electrical and Computer Engineering, UMass Lowell
  • Ian Humphrey, Technology Director, Department Lead, Technology Engineering, Raytheon, An RTX Business

Brief Abstract:
Unmanned Aerial Vehicles (UAVs) equipped with electro-optical sensors are being increasingly deployed in mission-critical environments, for real-time situational awareness. They are constrained by size, weight, power and cost (SWaP-C) requirements and often operate in unreliable network connectivity, with adversarial interference, and limited onboard computing resources. Conventional frame-based vision sensors embedded in these systems require high-bandwidth connectivity, often producing redundant information. This research investigates the application of neuromorphic vision sensors (NVS) on board UAVs for tracking, classification, and detection of moving targets. Using the asynchronous, event-based output of NVS systems, a computational framework is designed to augment frame-based methods and reduce data volume, enhance temporal resolution, and improve mission performance under varying lighting and motion conditions. The proposed architecture integrates NVS with lightweight embedded computing systems capable of running local real-time vision workloads. The contributions of this research include the application of machine learning algorithms for motion segmentation of event data to isolate mission-relevant targets from dynamic backgrounds; the deployment of energy-efficient, low-latency inference algorithms such as spiking neural networks and event-driven filters; and the development of a benchmarking methodology to evaluate SWaP-C trade-offs in hardware and software.