“Creating images with a single pixel alone is impossible if we only consider spatial information, as a single-point detector has none. However, such a detector can still provide valuable information about time. What we’ve managed to do is find a new way to turn one-dimensional data – a simple measurement of time – into a moving image which represents the three dimensions of space in any given scene,” said Dr Alex Turpin, Fellow in Data Science at the University of Glasgow’s School of Computing Science.
“The most important way that differs from conventional image-making is that our approach is capable of decoupling light altogether from the process. Although much of the paper discusses how we’ve used pulsed laser light to collect the temporal data from our scenes, it also demonstrates how we’ve managed to use radar waves for the same purpose.”
“We’re confident that the method can be adapted to any system which is capable of probing a scene with short pulses and precisely measuring the return ‘echo’. This is really just the start of a whole new way of visualising the world using time instead of light.”
Currently, the neural net’s ability to create images is limited to what it has been trained to pick out from the temporal data of scenes created by the researchers. However, with further training and even by using more advanced algorithms, it could visualise a much varied range of scenes, widening its potential applications in real-world situations say the researchers.
“The single-point detectors which collect the temporal data are small, light and inexpensive, which means they could be easily added to existing systems like the cameras in autonomous vehicles to increase the accuracy and speed of their pathfinding,” said Turpin.
“Alternatively, they could augment existing sensors in mobile devices like the Google Pixel 4, which already has a simple gesture-recognition system based on radar technology. Future generations of our technology might even be used to monitor the rise and fall of a patient’s chest in hospital to alert staff to changes in their breathing, or to keep track of their movements to ensure their safety in a data-compliant way,” he said.
“We’re very excited about the potential of the system we’ve developed, and we’re looking forward to continuing to explore its potential. Our next step is to work on a self-contained, portable system-in-a-box and we’re keen to start examining our options for furthering our research with input from commercial partners.”
‘Spatial images from temporal data’, is published in Optica. The research was funded by the Royal Academy of Engineering, the Alexander von Humboldt Stiftung, the Engineering and Physical Sciences Research Council (ESPRC) and Amazon.
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