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- Bio-inspired Sensors for Space Situational Awareness
Bio-inspired Sensors for Space Situational Awareness
Supervisors:
Primary supervisor A/Prof. Gregory Cohen
Co-supervisor: Dr Alexandre Marcireau
Description:
Monitoring artificial Earth satellites and space debris is essential to avoid satellites collisions, plan maneuvers, and detect unknown satellites. Most monitoring systems use ground-based observatories equipped with radars or conventional cameras. A novel approach, pioneered by the International Centre for Neuromorphic Systems (ICNS) at Western Sydney University (WSU), makes use of Neuromorphic cameras instead of conventional cameras to detect, track, and characterise satellites and space junk.
Neuromorphic cameras are silicon sensors in which the pixel design is based on the functioning of photoreceptors in biological retinas. These sensors do not generate frames, but instead report changes in luminance in the form of an asynchronous stream of emitted events. This sensing strategy breaks the fundamental relationship between frame rate and data rate. As a result, Neuromorphic cameras have an extremely high temporal resolution but generate little redundant data. Their output can be processed in real time with low latency and in low-power computational devices.
The research at ICNS has demonstrated that Neuromorphic cameras are particularly well suited to the detection and tracking of space objects. The performance of existing prototypes is already on par with conventional cameras, even though the latter have received far more attention and developments over the past decades. However, current Neuromorphic cameras were not designed with space monitoring in mind. Despite their clear benefits, this leaves a lot of room for optimisation. We suspect that better pixel designs for this application have yet to be found and the natural place from which to draw inspiration is the human retina. Current neuromorphic camera designs only mimic the temporal behaviour of a few cell types in the retina. Mimicking other cells and manipulating the analogue data before it leaves the pixel array will prove critical to improving the sensitivity of Neuromorphic sensors while preserving their temporal precision and power efficiency.
Outcomes:
During this thesis, the candidate will explore novel Neuromorphic pixels and sensors designs and assess their performance in the context of Space Situational Awareness. This will include the following outcomes:
- Simulate the star light received by a virtual sensor, using a start catalogue and models of the atmosphere.
- Simulate new pixel circuits and compare their theoretical performance with existing sensors. The simulation can be validated on known circuits with data from real Neuromorphic cameras.
- Implement and manufacture silicon sensors and evaluate them using our autonomous observatories (The Astrosite Neuromorphic Observatory Network).
What does the scholarship provide?
- Domestic candidates receive a tax-free stipend of $31,828 (AUD) per annum for up to 3.5 years to support living costs, supported by the Research Training Program (RTP) Fee Offset.
- International candidates receive a tax-free stipend of $31,828 (AUD) per annum for up to 3.5 years to support living costs. Those with a strong track record will be eligible for a tuition fee waiver.
- Support for conference attendance, fieldwork, and additional costs as approved by MARCS.
International candidates are required to hold an Overseas Student Health Care (OSHC) insurance policy for the duration of their study in Australia. This cost is not covered by the scholarship.
Eligibility criteria:
The successful applicant should:
- Hold qualification and experience equal to one of the following 1) a coursework Master’s with at least 25% research component, 2) a research Master’s degree, or 3) equivalent international qualifications in electrical engineering or computer science.
- Demonstrated expertise using Verilog or VHDL for FPGA programming or experience with both scripting languages (i.e. Python / Julia / Matlab) and low-level programming languages (i.e. C / C++ / Rust).
- Have a background in electrical engineering, circuit design, or optical engineering.
- Not hold a PhD degree in any field.
- Be conversational in written and spoken English.
- Reside in Australia for the duration of their studies, except for periods of approved overseas study leave.
- Not receive income from another source to support general living costs while undertaking the program, if that income is greater than 75% of the stipend rate*.
- Be enthusiastic and motivated to undertake further study at the PhD level.
* The 75% rule referenced above does not apply to: a) income earned from sources unrelated to research or b) income related to the research but not for the purpose of supporting general living costs. International applicants must demonstrate English language proficiency.
International applicants must demonstrate English language proficiency.
About ICNS
Partnership
Research
Projects
-
- Task-Driven Model Evaluation in Large-Scale Spiking Neural Networks
- A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks
- Event Based Wavefront Sensing Modalities
- Physics-Based Encoding for Spiking Neural Networks
- Neuromorphic Computational Imaging
- Defining Performance Metrics for Closed Loop Event Based Imaging Systems
- A Neuromorphic Framework for Event-Based DNNs using Minifloats
- A RISC-V instruction set architecture (ISA) extensions for neuromorphic computing using minifloats
- Astrometry with Event-based Vision Sensors
- Automatic Evaluation of Bushfire Risk via Acoustic Scene Analysis
- Bio-inspired Sensors for Space Situational Awareness
- Building a Neuromorphic Auditory Pathway for Sensing the Surrounding Environment
- Cold Start Astrometry for High-Precision Airspace and Space Objects Tracking with Neuromorphic Cameras
- Control Systems Inspired by Insect Central Pattern Generators that can Adapt to Dynamic Environments.
- Design of Neuromorphic Spiking Neural Networks for Real-Time Processing
- Enhanced Maritime Situational Awareness with Neuromorphic Cameras
- Environmental Situational Awareness using Neuromorphic Vision Sensors and IMU-based SLAM
- Fault Tolerant Distributed Swarm Intelligence using Neuromorphic Computing and Local Learning Principles
- Honey Bee Waggle Dance Detection via Neuromorphic Engineering
- Integrated Circuit Design for Event-based Vision Sensors
- Low-Power Acoustic Ecological Monitoring in Remote Areas using Machine Learning and Neuromorphic Engineering
- Neuromorphic Computing in Extreme Environments
- Neuromorphic Cyber Security at the Edge
- Neuromorphic Engineering for Acoustic Aerial Drone Detection in Visually Obscured Environments
- Machine Learning-Based Tool for Therapists to Monitor Speech Progress in Late Talkers
- Machine Learning for Automated Child Reading Assessment and Intervention
- Underwater Acoustic Drone Detection via Neuromorphic Models of Marine Mammal Audition
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