3 citations · 4 across the 6 of their papers we have counts for
7 papers
Event-to-Video Conversion for Overhead Object Detection
Darryl Hannan, Ragib Arnab, Gavin Parpart +3
Collecting overhead imagery using an event camera is desirable due to the energy efficiency of the image sensor compared to standard cameras. However, event cameras complicate down…
Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor
Gavin Parpart, Sumedh R. Risbud, Garrett T. Kenyon +1
Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing. The Locally Competitive Algorithm (LCA…
ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images
Cuong Ly, Grayson Jorgenson, Dan Rosa de Jesus +3
In the last decade, Convolutional Neural Network (CNN) and transformer based object detectors have achieved high performance on a large variety of datasets. Though the majority of…
Universal Fourier Attack for Time Series
Elizabeth Coda, Brad Clymer, Chance DeSmet +2
A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can dir…
Dictionary Learning with Accumulator Neurons
Gavin Parpart, Carlos Gonzalez, Terrence C. Stewart +6
The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time exec…
Digital Signal Processing Using Deep Neural Networks
Brian Shevitski, Yijing Watkins, Nicole Man +1
Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript, we describe a cus…
Image Compression: Sparse Coding vs. Bottleneck Autoencoders
Yijing Watkins, Mohammad Sayeh, Oleksandr Iaroshenko +1
Bottleneck autoencoders have been actively researched as a solution to image compression tasks. However, we observed that bottleneck autoencoders produce subjectively low quality r…