This paper describes the design and implementation of stingray, a library in Python built to perform time series analysis and related tasks on astronomical light curves. Its core functionality comprises a range of Fourier analysis techniques commonly used in spectral-timing analysis, as well as extensions for analyzing pulsar data, simulating data sets, and statistical modeling. Its modular build allows for easy extensions and incorporation of its methods into data analysis workflows and pipelines. We aim for the library to be a platform for the implementation of future spectral-timing techniques. Here, we describe the overall vision and framework, core functionality, extensions, and connections to high-level command-line and graphical interfaces. The code is Corresponding author: Daniela Huppenkothen dhuppenk@uw.edu arXiv:1901.07681v2 [astro-ph.IM] 9 Aug 2019 2 HUPPENKOTHEN ET AL.well-tested, with a test coverage of currently 95%, and is accompanied by extensive API documentation and a set of step-by-step tutorials.

This paper presents an end-to-end deep learning framework using passive WiFi sensing to classify and estimate human respiration activity. A passive radar test-bed is used with two channels where the first channel provides the reference WiFi signal, whereas the other channel provides a surveillance signal that contains reflections from the human target. Adaptive filtering is performed to make the surveillance signal source-data invariant by eliminating the echoes of the direct transmitted signal. We propose a novel convolutional neural network to classify the complex time series data and determine if it corresponds to a breathing activity, followed by a random forest estimator to determine breathing rate. We collect an extensive dataset to train the learning models and develop reference benchmarks for the future studies in the field. Based on the results, we conclude that deep learning techniques coupled with passive radars offer great potential for end-to-end human activity recognition.

No abstract

In this paper, we propose VitaNet, a radio frequency based contactless approach that accurately estimates the PPG signal using radar for stationary participants. The main insight behind VitaNet is that the changes in the blood volume that manifest in the PPG waveform are correlated to the physical movements of the heart, which the radar can capture. To estimate the PPG waveform, VitaNet uses a self-attention architecture to identify the most informative reflections in an unsupervised manner, and then uses an encoder decoder network to transform the radar phase profile to the PPG sequence. We have trained and extensively evaluated VitaNet on a large dataset obtained from 25 participants over 179 full nights. Our evaluations show that VitaNet accurately estimates the PPG waveform and its derivatives with high accuracy, significantly improves the heart rate and heart rate variability estimates from the prior works, and also accurately estimates several useful PPG features. We have released the codes of VitaNet as well as the trained models and the dataset used in this paper.

No abstract