no code implementations • 13 Sep 2023 • Ning Zhang, Timothy Shea, Arto Nurmikko
In this paper a new optical-computational method is introduced to unveil images of targets whose visibility is severely obscured by light scattering in dense, turbid media.
1 code implementation • 16 Mar 2023 • Jonathan Timcheck, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, Adam Kupryjanow, Garrick Orchard, Lukasz Pindor, Timothy Shea, Mike Davies
A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
no code implementations • 29 Sep 2021 • Kenneth Michael Stewart, Andreea Danielescu, Timothy Shea, Emre Neftci
Our novel approach consists of an event-based guided Variational Autoencoder (VAE) which encodes event-based data sensed by a Dynamic Vision Sensor (DVS) into a latent space representation suitable to compute the similarity of mid-air gesture data.
no code implementations • 31 Mar 2021 • Kenneth Stewart, Andreea Danielescu, Timothy Shea, Emre Neftci
We also implement the encoder component of the model on neuromorphic hardware and discuss the potential for our algorithm to enable real-time learning from real-world event data.
no code implementations • 25 May 2020 • Mohammad K. Ebrahimpour, Timothy Shea, Andreea Danielescu, David C. Noelle, Christopher T. Kello
Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum.