19,817 research outputs found
First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit
The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload
onboard the satellite TechDemoSat-1, used to study the radiation environment in
Low Earth Orbit (635km). LUCID operated from 2014 to 2017, collecting
over 2.1 million frames of radiation data from its five Timepix detectors on
board. LUCID is one of the first uses of the Timepix detector technology in
open space, with the data providing useful insight into the performance of this
technology in new environments. It provides high-sensitivity imaging
measurements of the mixed radiation field, with a wide dynamic range in terms
of spectral response, particle type and direction. The data has been analysed
using computing resources provided by GridPP, with a new machine learning
algorithm that uses the Tensorflow framework. This algorithm provides a new
approach to processing Medipix data, using a training set of human labelled
tracks, providing greater particle classification accuracy than other
algorithms. For managing the LUCID data, we have developed an online platform
called Timepix Analysis Platform at School (TAPAS). This provides a swift and
simple way for users to analyse data that they collect using Timepix detectors
from both LUCID and other experiments. We also present some possible future
uses of the LUCID data and Medipix detectors in space.Comment: Accepted for publication in Advances in Space Researc
Privacy-Preserving Facial Recognition Using Biometric-Capsules
Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design.
In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods
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