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- IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
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- 1 to 5
- Sponsoring Org:
- National Science Foundation
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Automatic slowness vector measurements of seismic arrivals with uncertainty estimates using bootstrap sampling, array methods and unsupervised learningSUMMARY Horizontal slowness vector measurements using array techniques have been used to analyse many Earth phenomena from lower mantle heterogeneity to meteorological event location. While providing observations essential for studying much of the Earth, slowness vector analysis is limited by the necessary and subjective visual inspection of observations. Furthermore, it is challenging to determine the uncertainties caused by limitations of array processing such as array geometry, local structure, noise and their effect on slowness vector measurements. To address these issues, we present a method to automatically identify seismic arrivals and measure their slowness vector properties with uncertainty bounds. We do this by bootstrap sampling waveforms, therefore also creating random sub arrays, then use linear beamforming to measure the coherent power at a range of slowness vectors. For each bootstrap sample, we take the top N peaks from each power distribution as the slowness vectors of possible arrivals. The slowness vectors of all bootstrap samples are gathered and the clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to identify arrivals as clusters of slowness vectors. The mean of slowness vectors in each cluster gives the slowness vector measurement for that arrival and the distribution of slowness vectorsmore »
Vital signs (e.g., heart and respiratory rate) are indicative for health status assessment. Efforts have been made to extract vital signs using radio frequency (RF) techniques (e.g., Wi-Fi, FMCW, UWB), which offer a non-touch solution for continuous and ubiquitous monitoring without users’ cooperative efforts. While RF-based vital signs monitoring is user-friendly, its robustness faces two challenges. On the one hand, the RF signal is modulated by the periodic chest wall displacement due to heartbeat and breathing in a nonlinear manner. It is inherently hard to identify the fundamental heart and respiratory rates (HR and RR) in the presence of higher order harmonics of them and intermodulation between HR and RR, especially when they have overlapping frequency bands. On the other hand, the inadvertent body movements may disturb and distort the RF signal, overwhelming the vital signals, thus inhibiting the parameter estimation of the physiological movement (i.e., heartbeat and breathing). In this paper, we propose DeepVS, a deep learning approach that addresses the aforementioned challenges from the non-linearity and inadvertent movements for robust RF-based vital signs sensing in a unified manner. DeepVS combines 1D CNN and attention models to exploit local features and temporal correlations. Moreover, it leverages a two-stream schememore »
The reconstruction of Faraday depth structure from incomplete spectral polarization radio measurements using the RM synthesis technique is an underconstrained problem requiring additional regularization. In this paper, we present cs-romer: a novel object-oriented compressed sensing framework to reconstruct Faraday depth signals from spectropolarization radio data. Unlike previous compressed sensing applications, this framework is designed to work directly with data that are irregularly sampled in wavelength-squared space and to incorporate multiple forms of compressed sensing regularization. We demonstrate the framework using simulated data for the VLA telescope under a variety of observing conditions, and we introduce a methodology for identifying the optimal basis function for reconstruction of these data, using an approach that can also be applied to data sets from other telescopes and over different frequency ranges. In this work, we show that the delta basis function provides optimal reconstruction for VLA L-band data and we use this basis with observations of the low-mass galaxy cluster Abell 1314 in order to reconstruct the Faraday depth of its constituent cluster galaxies. We use the cs-romer framework to de-rotate the Galactic Faraday depth contribution directly from the wavelength-squared data and to handle the spectral behaviour of different radio sources in a direction-dependentmore »
The radio frequency spectral shaper is an essential component in emerging multi-service mobile communications, multiband satellite and radar systems, and future 5G/6G radio frequency systems for equalizing spectral unevenness, removing out-of-band noise and interference, and manipulating multi-band signal simultaneously. While it is easy to achieve simple spectral functions using either conventional microwave photonic filters or the optical spectrum to microwave spectra mapping techniques, it is challenging to enable complex spectral shaping functions over tens of GHz bandwidth as well as to achieve point-by-point shaping capability to fulfill the needs in dynamic wireless communications. In this paper, we proposed and demonstrated a novel spectral shaping system, which utilizes a two-section algorithm to automatically decompose the target RF response into a series of Gaussian functions and to reconstruct the desired RF response by microwave photonic techniques. The devised spectral shaping system is capable of manipulating the spectral function in various bands (S, C, and X) simultaneously with step resolution of as fine as tens of MHz. The resolution limitation in optical spectral processing is mitigated using the discrete convolution technique. Over 10 dynamic and independently adjustable spectral control points are experimentally achieved based on the proposed spectral shaper.
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